Bridging Data and Diagnostics: A Systematic Review and Case Study on Integrating Trend Monitoring and Change Point Detection for Wind Turbines

Abstract

Wind turbines face significant operational challenges due to their complex electromechanical systems, exposure to harsh environmental conditions, and high maintenance costs. Reliable structural health monitoring and condition monitoring are therefore essential for early fault detection, minimizing downtime, and optimizing maintenance strategies. Traditional approaches typically rely on either Trend Monitoring (TM) or Change Point Detection (CPD). TM methods track the long-term behaviour of process parameters, using statistical analysis or machine learning (ML) to identify abnormal patterns that may indicate emerging faults. In contrast, CPD techniques focus on detecting abrupt changes in time-series data, identifying shifts in mean, variance, or distribution, and providing accurate fault onset detection. While each approach has strengths, they also face limitations: TM effectively identifies fault type but lacks precision in timing, while CPD excels at locating fault occurrence but lacks detailed fault classification. This review critically examines the integration of TM and CPD methods for wind turbine diagnostics, highlighting their complementary strengths and weaknesses through an analysis of widely used TM techniques (e.g., Fast Fourier Transform, Wavelet Transform, Hilbert–Huang Transform, Empirical Mode Decomposition) and CPD methods (e.g., Bayesian Online Change Point Detection, Kullback–Leibler Divergence, Cumulative Sum). By combining both approaches, diagnostic accuracy can be enhanced, leveraging TM’s detailed fault characterization with CPD’s precise fault timing. The effectiveness of this synthesis is demonstrated in a case study on wind turbine blade fault diagnosis. Results shows that TM–CPD integration enhances early detection through coupling vibration and frequency trend analysis with robust statistical validation of fault onset.

1. Introduction

Wind power has become the foundation of sustainable world power generation because of the increasing climatic peril. Wind power exhibited remarkable growth during the years 2010–2025 and global installed capacity has increased continuously from below 198 GW in 2010 to above 1152 GW in 2024 [1], as shown in Figure 1. It has experienced a constant growth rate of almost 13% per year, as shown in Figure 2, which underscores the key significance of this sector. Wind turbines are located in remote, harsh climatic conditions, and a sudden failure can result in a blackout as well as economic loss. Here, preventive maintenance comes in handy in reducing the challenge of operation through proactive detection of faults and their prevention before the onset of costly failures.

Imagine being in the control room of a large industrial plant. The machines hum steadily, and the displays suggest everything is operating normally—until a small but critical change occurs. It might be a slight vibration in a motor, a brief spike in temperature, or an unexpected delay in a process. Left unnoticed, such minor disturbances can quickly escalate into costly failures. A similar situation can be seen in financial markets, where numbers flash across screens and traders react in real time: a sudden dip in stock value may be dismissed as a random fluctuation or serve as the first signal of a major economic downturn [2].

In all these scenarios—industry [3], finance [4], environment [5], and beyond—the ability to detect change is more than just an academic pursuit. It functions as a tool for prevention, adaptation, and innovation. Researchers, engineers, and analysts devote their efforts to identifying these hidden transitions, aiming to distinguish meaningful signals from background noise. This brings us to two essential concepts: Change Point Detection (CPD) [6] and Trend Monitoring [7]. While CPD focuses on identifying specific moments when a system undergoes a transformation intervention [8,9], trend monitoring seeks to uncover the long-term patterns that define its behaviour [10,11]. Both play an essential role in understanding and predicting changes in complex systems, from manufacturing and finance to healthcare and artificial intelligence [12].

Accurate fault and damage detection in wind turbines presents several challenges due to the highly complex operational environment, the fluctuating nature of wind conditions, and the diversity of possible failure modes. Wind turbines experience extreme weather conditions, including strong winds, icing, temperature fluctuations [13], high humidity, and lightning strikes. All of these hasten the wear and tear, and thus, early fault detection is essential [14]. An additional serious concern is that a turbine has numerous mechanical and electrical systems, including rotor blades [15], bearings [16], the gearbox [17], the generator [18], and control electronics [19]. All these parts may be subject to various failures, including structural cracks in blades, gear misalignment, and electrical malfunctions. A combination of monitoring techniques is necessary as described in these articles [13,15,20].

Conventional fault detection of wind turbines is based either on Trend Monitoring [21] or Change Point Detection [22] individually; yet neither of them, in isolation, can offer a complete solution. TM and CPD offer complementary strengths, but when applied in isolation, each faces limitations that restrict both precise fault diagnosis and accurate fault prediction. TM techniques such as Fast Fourier Transform (FFT), Wavelet Transform (WT) [15], and Hilbert-Huang Transform (HHT) are excellent in identifying the different aspects of the turbine signals that have changed. These methods analyse vibration, acoustic emissions, and electrical signals to detect shifts in frequency, amplitude, or signal patterns, which often indicate faults [23]. However, TM on its own cannot provide accurate information about the exact timing of fault initiation. While it can reveal variations in frequency or other parameters over time, it lacks the ability to deliver a quantitative statistical assessment to distinguish whether these changes are within normal operating limits or indicative of an emerging fault [15,23]. As a result, TM can sometimes misinterpret transient variations as genuine faults, leading to false positives or false negatives. Moreover, TM methods struggle to distinguish between gradual degradations, such as bearing wear, and sudden failures, such as a gearbox crack. This limitation can leave operators uncertain about whether immediate intervention is necessary [7,12,24].

In the same way, CPD techniques, such as Bayesian Online Change Point Detection (BOCPD) [25,26,27,28], Kullback–Leibler (KL) Divergence, and CUSUM [29,30] are developed to identify the precise instant at which a change happens in a signal. These methods are mainly employed to determine abrupt changes in the turbine operation, e.g., an abrupt rise in vibration amplitude or unintended variations in the power output [31,32,33]. However, CPD on its own does not provide information about the nature of the change; it only identifies that a change has occurred and when it happened [34,35]. In addition, CPD methods are often sensitive to noise, which can cause them to misclassify non-critical variations as faults, leading to unnecessary maintenance actions and inspections.

Recently, various change-point detection methods have been investigated for wind turbine condition monitoring. These include change-point and structural break detection techniques [36,37,38], cumulative sum (CUSUM) techniques [29,39], Wilcoxon rank sum test-based technique [40], unit root and stationarity test-based technique [41]. These offer low-cost and computationally efficient solutions for reliable monitoring, thereby supporting early fault identification and reduced turbine downtime. Table 1 shows some combined hybrid models for the fault and damage detection for wind turbines.

Among the recently proposed approaches, a stationarity-based method [42] utilizes the Augmented Dickey–Fuller (ADF) test [43] to detect faults by identifying abrupt changes in the stationarity of SCADA signals over time. Unlike traditional techniques, this method does not depend on predefined behavioural models of the turbine. Instead, anomaly detection is carried out within a sliding data window framework, where accumulated variations in stationarity across the moving window allow anomalies to be effectively identified. Despite the progress of individual approaches, there is still no standardized method for fault detection, making it difficult to establish unified monitoring frameworks. To address this challenge, hybrid monitoring schemes that integrate Trend Monitoring and Change Point Detection with advanced data analytics and machine learning are increasingly seen as essential. Such integration has the potential to significantly improve the reliability of fault detection in wind turbines. However, it also demands substantial computational resources and specialized domain knowledge, underscoring the need for continued research and the development of new enabling technologies.

This review article focuses on the integration of TM-CPD schemes and their potential applications for fault and damage detection in wind turbines. A critical analysis of various TM and CPD tools is presented, highlighting their respective strengths, limitations, and complementarities. In addition, the study outlines potential directions for future research, offering insights into how TM-CPD integration can be further developed to improve wind turbine condition monitoring and diagnostic capabilities.

Table 1. Integrated hybrid models for the fault and damage detection for wind turbines.

Reference	Proposed Architecture	Application	Comparison & Accuracy
[44]	Phase Current Characteristics (PCCs) + Back Propagation Neural Network (BP NN)	Temperature rise of the generator bearing fault	High accuracy, strong reliability, and good applicability.
[45]	Kernel Principal Component Analysis (KPCA) + Support Vector Machine (SVM)	Rotor unbalance	Kernel Principal Component Analysis combined with Support Vector Machine achieves higher recognition accuracy compared to Support Vector Machine and Principal Component Analysis-Support Vector Machine.
[46]	Principal Component Analysis (PCA)—Hierarchical K-Nearest Neighbours (HKNN) + Euclidean Distance	Rotor unbalance, rotor misalignment	Reduces algorithm complexity while improving analysis accuracy.
[47]	Empirical Mode Decomposition (EMD) + Fuzzy K-Means Clustering (FK) + Convolutional Neural Network (CNN)	Wind turbine generator bearing fault	Enhances the signal-to-noise ratio, expanding the fault diagnosis capability.
[48]	Hilbert-Huang Transform (HHT) + Support Vector Machine (SVM) + Empirical Frequency Decomposition (EFD) + Particle Swarm Optimization (PSO)	Synchronous motor fault diagnosis using vibration acceleration data	Particle Swarm Optimisation achieves 95.83% accuracy, outperforming Genetic Algorithm (87.5% accuracy). Optimisation time: 2.091 s, evolution cycles: 30 iterations.
[49]	Principal Component Analysis (PCA) + Support Vector Machine (SVM)	Rotor bending, rotor unbalance	Average fault recognition rate: 94.5%.
[50]	Wavelet Transform (WT) + Dempster-Shafer Theory (D-S)	Wind Turbine Generator bearing fault	Enhances the signal-to-noise ratio and broadens the scope of fault detection.

Table 1. Integrated hybrid models for the fault and damage detection for wind turbines.

Reference	Proposed Architecture	Application	Comparison & Accuracy
[44]	Phase Current Characteristics (PCCs) + Back Propagation Neural Network (BP NN)	Temperature rise of the generator bearing fault	High accuracy, strong reliability, and good applicability.
[45]	Kernel Principal Component Analysis (KPCA) + Support Vector Machine (SVM)	Rotor unbalance	Kernel Principal Component Analysis combined with Support Vector Machine achieves higher recognition accuracy compared to Support Vector Machine and Principal Component Analysis-Support Vector Machine.
[46]	Principal Component Analysis (PCA)—Hierarchical K-Nearest Neighbours (HKNN) + Euclidean Distance	Rotor unbalance, rotor misalignment	Reduces algorithm complexity while improving analysis accuracy.
[47]	Empirical Mode Decomposition (EMD) + Fuzzy K-Means Clustering (FK) + Convolutional Neural Network (CNN)	Wind turbine generator bearing fault	Enhances the signal-to-noise ratio, expanding the fault diagnosis capability.
[48]	Hilbert-Huang Transform (HHT) + Support Vector Machine (SVM) + Empirical Frequency Decomposition (EFD) + Particle Swarm Optimization (PSO)	Synchronous motor fault diagnosis using vibration acceleration data	Particle Swarm Optimisation achieves 95.83% accuracy, outperforming Genetic Algorithm (87.5% accuracy). Optimisation time: 2.091 s, evolution cycles: 30 iterations.
[49]	Principal Component Analysis (PCA) + Support Vector Machine (SVM)	Rotor bending, rotor unbalance	Average fault recognition rate: 94.5%.
[50]	Wavelet Transform (WT) + Dempster-Shafer Theory (D-S)	Wind Turbine Generator bearing fault	Enhances the signal-to-noise ratio and broadens the scope of fault detection.

Table 2. Existing review article on the topic of structural health monitoring and condition monitoring of wind turbines.

Sr. No.	Author(s), Year	Brief Description	Focus Category	Ref. No.
1	García Márquez et al., 2012	A survey of diagnosis methods as well as signal processing algorithms for the condition monitoring of wind turbines, but only up to signal-based methods, and without a thorough literature survey that would cover the full potential and limitations of the present techniques	Trend Monitoring	[51]
2	Purarjomandlangrudi et al., 2013	A systematic but relatively brief literature review on signal-based diagnosis techniques used in wind turbine condition monitoring	Trend Monitoring	[52]
3	Schubel et al., 2013	A relatively brief review of damage diagnosis techniques, specifically for structural health and cure monitoring in wind turbine blades	Trend Monitoring	[53]
4	Kusiak et al., 2013	Survey of the wide range of issues involved in existing methodologies and techniques of wind speed forecast, system control, and the condition monitoring of wind turbines, whose primary drawback lies in being constrained to some signal-based techniques and lacking in providing an exhaustive literature survey on the specific subject of the condition monitoring of turbines	Trend Monitoring	[54]
5	Bindi et al., 2014	A review of commercially available supervisory control and data acquisition (SCADA) systems and related analysis software for SCADA-based condition monitoring and optimisation of the performance of wind turbines, but without a systematic review of the literature beyond the commercially available SCADA-based systems themselves	Trend Monitoring	[55]
6	Tchakoua et al., 2014	A discussion of the diagnosis techniques and maintenance tactics of the wind turbine, but the debate only involves the signal-based techniques, without adequate discussion of the failure processes of the different wind turbine parts	Trend Monitoring	[56]
7	Kaewniam et al., 2022	A review of the literature on diagnosis procedures, covering various signal processing techniques, sensor technologies, and NDT approaches used for WTB damage identification and monitoring.	Trend Monitoring	[57]
8	Welte and Wang, 2014	A brief account of methods and schemes of prognosis and life prediction of the parts of a wind turbine, which is relatively short, whose general, non-specialised-for-wind-turbines text is essential	Change Point Detection	[58]
9	Crabtree et al., 2014	A survey of commercially available condition monitoring systems for wind turbines, but it misses a comprehensive review of literature beyond the commercial condition monitoring systems alone	Trend Monitoring	[59]
10	Wang et al., 2014	A survey of SCADA-related condition monitoring methods in wind turbines, and the introduction of an intelligent system for the fault diagnosis and prognosis of wind turbines based on SCADA data, but confined to SCADA-related methods only, hence not providing an exhaustive review	Trend Monitoring	[60]
11	Antoniadou et al., 2015	A short list of some of the relevant signal processing and machine learning methods applied in structural and condition monitoring of wind turbines, with some examples of application, but by no means an exhaustive survey	Trend Monitoring	[61]
12	Wymore et al., 2015	A component-by-component analysis of a few diagnosis methods employed in the structural and condition monitoring of the key parts of a wind turbine, but covering only some of the signal-based methods	Trend Monitoring	[62]
13	Qiao and Lu, 2015	A relatively broad literature review in two parts: (1) failure modes and characteristics of essential parts/subsystems of the main wind turbine, and (2) diagnosis/prognosis technologies and necessary signal processing technologies used in the condition monitoring of the wind turbine, but mainly a summary of the popular signal-based diagnosis technologies, while the model-based technologies or non-destructive condition monitoring technologies are very concisely summarised	Trend Monitoring	[63]
14	Kandukuri et al., 2016	A review of diagnosis/prognosis techniques, which is mainly limited to some signal-based methods, and, in particular,, the condition monitoring of low-speed bearings and planetary gearboxes in wind turbines	Trend Monitoring	[64]
15	Azevedo et al., 2016	A review of diagnosis/prognosis techniques taking into consideration the technical, economic, and operational challenges, but that applies only to specific signal-based techniques, and namely, the condition monitoring of bearings in wind turbines	Trend Monitoring	[65]
16	Yang et al., 2017	A review of structural health monitoring techniques, which does not yet constitute a comprehensive review, is limited to some signal-based methods for wind turbine blades	Trend Monitoring	[66]
17	Uma Maheswari and Umamaheswari, 2017	A review of drivetrain condition monitoring in wind turbines, however, is limited to the vibration monitoring technique, including non-stationary signal processing algorithms, and specifically for drivetrain components	Trend Monitoring	[67]
18	Tautz-Weinert and Watson, 2017	A review of diagnosis techniques for wind turbine condition monitoring, however, is limited to SCADA-based techniques only		[68]
19	Marugán et al., 2018	A survey of the applications of artificial neural networks (ANNs) in wind energy systems for forecasting, design optimisation, fault diagnosis, and optimal control, but not a review of the literature further than the ANNs techniques and the condition monitoring in general	Trend Monitoring	[69]
20	Salameh et al., 2018	A review of diagnosis techniques for wind turbine condition monitoring, but it is restricted to some signal-based methods, and specifically, the condition monitoring of gearboxes in wind turbines	Trend Monitoring	[70]
21	Abid et al., 2018	A literature survey of prognosis methods applied to wind turbines, as well as an introduction to various prognosis stages, such as construction of health indicators, detection of degradation, and estimation of remaining useful life (RUL), but not the failure modes of various wind turbine parts, nor some crucial parts like rotor blades	Change Point Detection	[71]
22	Leite et al., 2018	A review of prognosis techniques and RUL estimation methods for the critical components of wind turbines, but it does not address the failure modes of different wind turbine components	Trend Monitoring	[72]
23	Wei et al., 2019	A review of the diagnosis and signal processing techniques of wind turbine condition monitoring, but only till some signal-related methods, that are, the condition monitoring of the gears, rotors, and bearings of the wind turbine	Trend Monitoring	[73]
24	Moeini et al., 2019	A review of diagnosis techniques for wind turbine condition monitoring, but it only includes some signal-based methods, and the majority of non-destructive condition monitoring technologies are simply missing	Trend Monitoring	[74]
25	Zhang and Lu, 2019	A review of the diagnosis technologies of wind turbine condition monitoring in three aspects of energy flow, information flow, and integrated O&M system, but no complete review, including some signal-based technologies without mentioning non-destructive technologies of condition monitoring	Trend Monitoring	[75]
26	Leahy et al., 2019	A data quality problem survey for the aid of SCADA-based condition monitoring of wind turbines, while limiting the discussion of data quality problems, SCADA-based methods only	Trend Monitoring	[76]
27	Habibi et al., 2019	A tutorial-style review on diagnosis techniques and fault-tolerant control methods used in wind turbines, which is limited to model-based techniques only	Change Point Detection	[77]
28	Liu and Zhang, 2020	A survey of the failure modes and diagnosis methods of the bearings of wind turbines; however, the survey was only up to some signal-based techniques and particularly for the bearings (main bearings, gearbox bearings, generator bearings, blade bearings, and yaw bearings)	Trend Monitoring	[78]
29	Márquez F and, Papaelias M, 2020	A survey of non-destructive condition monitoring technologies for the diagnosis of wind turbine blades, but the primary area of review, as its remit indicates, is the non-destructive technologies, and, in particular,, the wind turbine blades	Trend Monitoring	[79]
30	The current study	This study demonstrates how integrating tools from Trend Monitoring and Change Point Detection can improve condition monitoring and early fault detection of wind turbines	Trend Monitoring + Change Point Detection

provides a summary of existing review articles on fault diagnosis methods. However, these studies focus exclusively on either Trend Monitoring or Change Point Detection, without considering their integration. This review aims to address that gap by examining both approaches together and highlighting the benefits of their combined application.

Therefore, this article aims to review current trends in addressing the following research questions:

• Which key TM and CPD tools can be applied for fault diagnosis in wind turbines?
• Which combinations of TM and CPD tools are most effective for detecting different types of faults?

The structure of this article is illustrated in Figure 3. Section 2 describes the methodology used in this study. Section 3 covers the fundamentals of TM and CPD. Section 4 discusses the limitations of using TM or CPD alone for fault detection. Section 5 presents a case study applying the findings to wind turbine blade failure. Section 6 summarizes the conducted literature review, while Section 7 outlines potential directions for future research.

2. Materials and Methods

This review was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [80]. The detailed protocol of this review has been registered without any amendments and made publicly available in the Open Science Framework (OSF) [81]. PRISMA has a standardised framework intended to facilitate the clarity and transparency of systematic reviews and meta-analyses, as outlined in an exhaustive 27-item checklist. The approach involved three key stages: the development of research questions, a comprehensive literature search within databases, and the systematic inclusion and exclusion of research articles. Certain items (12, 13e, 13f, 14, 15, and 18–22) from the PRISMA checklist were omitted as they were not relevant to the scope of this particular review.

2.1. Literature Search Strategy

This review selectively focused on two key themes in work on wind turbines: recognising operation or structure changes, and system health monitoring. To accommodate the broad base of relevant work, the literature review employed two narrowly focused sets of keywords:

• “Trend Monitoring” OR “Change Point Detection” AND “Wind Turbines
• “Structural Health Monitoring” OR “Condition Monitoring” AND “Wind Turbine”

These questions were systematically implemented in key scientific databases, including Web of Science, ScienceDirect, ACM Digital Library, Scopus, and IEEE Xplore, providing a thorough and efficient retrieval of studies that align with the aims of this review.

2.2. Inclusion and Exclusion Criteria

To ensure that only high-quality and relevant research was included, predefined selection criteria were applied.

2.2.1. Inclusion Criteria

• Peer-reviewed journal articles and conference papers.
• Studies published within the last 15 years (2010–2025).
• Research focuses on TM and CPD methods for wind turbine fault detection.
• Studies discussing the integration of TM-CPD for predictive maintenance.

2.2.2. Exclusion Criteria

• Non-English publications.
• Studies focusing on unrelated condition monitoring techniques.
• Patents, books, and non-peer-reviewed materials (unless significant to the field).

2.3. Study Selection

We used Mendeley to compile and manage the database of all collected records. The selection process followed three screening stages in line with the PRISMA framework, with the quantitative workflow illustrated in the PRISMA diagram shown in Figure 4. Initially, 21,646 records were retrieved from Web of Science, ScienceDirect, ACM Digital Library, Scopus, and IEEE Xplore using the specified keywords. After removing 10,312 duplicates and 3222 inaccessible or irrelevant records, 8112 records remained for title and abstract screening. This step excluded 7846 records that were not directly relevant to wind turbine monitoring or diagnostic objectives. The remaining 266 articles underwent full-text review to assess depth and relevance. Studies were further excluded if they only superficially discussed key concepts or were flagged by Mendeley as retracted or ineligible. This rigorous process resulted in 226 studies, which form the evidence base for examining applications of trend monitoring, change point detection, structural health monitoring, and condition monitoring in wind turbine systems.

2.4. Analysis of Selected Articles

The publication trends can be divided into two distinct phases. Between 2010 and 2019, research on trend monitoring, change point detection, structural health monitoring, and condition monitoring of wind turbines grew gradually. Starting with about six articles in 2010, the number of publications rose steadily to 24 by 2019, reflecting a slow but consistent increase in interest. The second phase, covering 2020 to 2025, shows a much sharper rise, beginning with 13 articles in 2020 and reaching 39 by 2025. This surge highlights the growing recognition of the importance of advanced monitoring and diagnostic methods in wind turbine systems. The two-phase growth is illustrated in Figure 5.

3. The Evolution of Fault Diagnosis: From Traditional Methods to TM-CPD Integration

Wind turbines convert the kinetic energy of wind into electricity through a series of mechanical and electrical processes. The wind first acts on the blades, causing the rotor to spin and drive the low-speed shaft. This rotational energy is then transmitted through a gearbox to the high-speed shaft connected to the generator, where it is converted into electrical power. Additional components—such as the controller, brake system, and yaw mechanism—ensure safe and efficient operation by regulating speed, direction, and power output. Instruments like the anemometer and wind vane provide real-time data on wind conditions, enabling proper turbine alignment and optimizing overall performance [82]. Figure 6 shows the internal structure and main parts of a horizontal-axis wind turbine [83]. Traditional fault diagnosis techniques for wind turbines rely on model-based, signal-based, and knowledge-based approaches, as illustrated in Figure 7 along with their respective strengths and weaknesses [84,85,86,87]. While these methods provide valuable insights, they are often hindered by challenges such as high computational demands, susceptibility to sensor noise, and limited adaptability when confronted with previously unseen faults [85]. A comparative summary of these limitations is presented in

Table 3. What are the limitations of the traditional methods for fault diagnostics?

Fault Diagnosis Technique	Description	Limitations of the Traditional Approach	Ref. No.
Model-Based	Uses mathematical models to simulate wind turbine behaviour and detect deviations indicating faults.	Highly complex models require significant computational power, making real-time applications particularly challenging.	[77]
Signal-Based	Analyses real-time signals (vibration, temperature, electromagnetic) and compares them with healthy reference signals.	Sensitive to sensor placement and noise, making it prone to false positives.	[89]
Knowledge-Based	Leverages historical failure data and machine learning classifiers to detect anomalies in turbine performance.	Requires extensive labelled failure data for training; lacks adaptability to unseen faults.	[90]

.

To overcome the above limitations, integrating TM with CPD has emerged as a promising solution. TM techniques capture key fault-related features from signals, while CPD methods provide statistical validation of the exact point at which a fault occurs. When combined, these approaches enhance fault detection by enabling real-time monitoring, improving diagnostic accuracy, and reducing false alarm rates [91].

3.1. Trend Monitoring: The Art of Reading the Future

Trend monitoring focuses on tracking how a system evolves over time [92]. Instead of detecting abrupt changes, it identifies gradual, long-term variations that characterize system behaviour. For example, trends can be observed in the stock market [2], or environmental changes [93]. By monitoring such patterns, it becomes possible to anticipate, adapt to, and respond to gradual shifts before they reach critical tipping points. Examples include detecting rising global temperatures [13,94], observing the spread of a new social media trend, or identifying early indicators of economic recessions [95]. Ultimately, recognizing patterns is central to intelligent decision-making. Trend monitoring can be classified in various ways; in this work, a broad classification is presented in Figure 8.

3.1.1. Machine Learning Methods

Machine learning (ML) techniques for the fault diagnosis of wind turbines have been employed widely, such as the k-nearest neighbour (KNN) algorithm [96], random forest (RF) algorithm [97], support vector machine (SVM) [98], cointegration [99] and artificial neural networks (ANN) [100]. Dao (2023) [99] introduced the novel use of cointegration analysis as a fault detection method for wind turbines using SCADA data. This approach identifies long-term equilibrium relationships among multiple variables to uncover operational changes. A key strength of the method is its ability to handle non-stationary data, making it effective for detecting hidden faults at an early stage. However, its limitations include the reliance on domain expertise for appropriate variable selection and the high computational demands associated with large-scale data processing. Kankar et al. [101] compared the performance of ANNs and SVM for fault diagnosis in rotor bearing systems. Their results showed that ANNs achieved higher classification accuracy than SVM. Samanta et al. [102] investigated fault diagnosis using time-domain features as inputs to three types of ANNs: multi-layer perceptron (MLP), radial basis function neural network (RBFNN), and probabilistic neural network (PNN). The performance of these models was tested and compared. Simulation results for bearing fault diagnosis demonstrated that, when the genetic algorithm (GA) was employed as a feature selection tool, the accuracy of fault classification reached up to 100%. The authors in [103] applied a backpropagation (BP) neural network to model the generator temperature under normal operating conditions. Experimental results showed that when the predicted temperature exceeded a preset threshold, the system successfully triggered an alarm. However, this machine learning model functions as a shallow classifier with limited learning capacity, making it less effective for handling complex pattern recognition problems.

Table 4. Summary of ML methods applications in wind turbines.

Methods	Input Data	Advantages	Disadvantages	Monitoring Components	Articles
Support Vector Machine (SVM)	Vibration signal, SCADA	Higher diagnostic accuracy with fewer samples.	Kernel function selection is critical, as it may converge to a local minimum.	Bearings, Gears, Blades	[104,105,106]
Decision Tree (DT)	Vibration signal, generator current signal	Good global optimisation and generalisation capabilities.	Easily overfits; cannot build active networks.	Gears, Generation system	[107,108]
Bayesian Methods (Bayes)	Vibration signal, SCADA	Deepens fault understanding; needs minimal data processing.	Sensitive to error categories; relies on hypothetical models.	Gearbox, Blades, Bearings	[109,110,111]
Hidden Markov Model (HMM)	Vibration signal	Fast fault diagnosis with low computational complexity.	Cannot fully utilise historical data; ambiguous topology in diagnosis.	Bearings	[112,113,114]
Random Forest (RF)	Vibration signal	Robust and not sensitive to outliers.	Complex training; high computational cost.	Bearings, Gearbox	[115,116]
Classification Algorithms (CA)	Vibration signal, SCADA	Handles massive data efficiently.	Requires predefined categories; relies on cluster selection.	Bearings, Gearbox	[117,118]
Back Propagation Neural Network (BPNN)	Vibration signal	Self-learning with fault tolerance.	Slow convergence speed; may overfit.	Gearbox, Transmission chain, Generator fault	[119,120]
Extreme Learning Machine (ELM)	Vibration signal, SCADA	Fast learning speed and adapts to new situations.	Limited learning due to a single hidden layer.	Gearbox, Transmission chain	[121,122]
Radial Basis Function Neural Network (RBFNN)	Vibration signal	Strong nonlinear fitting and fast convergence.	Performance depends on data quality and sample selection.	Blades, Actuators	[123,124]
Self-Organising Map (SOM)	Vibration signal, SCADA	Visualisation support with simple implementation.	High training cost.	Bearings, Gearbox	[125,126]
Adaptive Resonance Theory (ART)	Vibration signal	Learn new problems without prior data.	Losing information may occur.	Bearings, Gearbox	[127,128]
Convolutional Neural Network (CNN)	Vibration signal	Simplifies network complexity and avoids overfitting.	Requires large datasets, high computational cost, and fixed input length.	Bearings, Gearbox	[129,130]
Deep Belief Network (DBN)	Vibration signal, SCADA	Versatile, handles nonlinear high-dimensional data.	Handles only one-dimensional data; long computation time.	Gearbox	[131,132,133]
Stacked Autoencoder (SAE)	Vibration signal	No need for large datasets; overcomes gradient diffusion.	Long training time; risk of overfitting.	Bearings, Gearbox	[134,135]
Recurrent Neural Network (RNN)	Vibration signal	Diagnoses slow-developing faults and solves gradient disappearance.	No clear rules for selecting hidden neurons.	Bearings, Gearbox	[103,136]
Transfer Learning (TL)	Vibration signal, SCADA	High diagnostic accuracy under variable conditions; adaptive to new faults.	Negative transfer learning may occur.	Bearings, Gearbox	[137,138]

presents further studies on the application of machine learning methods for fault diagnosis across different wind turbine components.

3.1.2. Signal Processing

Signal-based fault diagnosis can be classified into the time-domain, frequency-domain, and time-frequency techniques [139]. These methods extract features either from the time domain or the frequency spectrum and make diagnostic decisions by analyzing characteristics such as amplitude, mean, and standard deviation of the system’s input or output. Because wind turbines are complex systems operating under variable conditions, fault signals often exhibit nonlinear and non-stationary behavior. For this reason, time-frequency analysis is generally more effective for diagnosing faults in generators and other turbine components. In this review, we focus on widely used time-frequency analysis techniques—including the Fourier transform (FFT), wavelet transform (WT), and Hilbert-Huang transform (HHT)—because wind turbines generate vibration [26,140], acoustic, and electrical signals [16] whose characteristics can change in the presence of faults [31].

• Fast Fourier Transform (FFT);
• Wavelet Transform (WT);
• Hilbert-Huang Transform (HHT);
• Empirical Mode Decomposition (EMD).

Fast-Fourier Transform (FFT)

Accurate fault detection in wind turbines relies on analyzing vibration signals to identify abnormal operating conditions. Among the various signal-processing methods, the Fourier Transform (FT) is one of the most widely adopted time-frequency conversion techniques [141]. By converting time-domain signals into the frequency domain, FT enables the identification of characteristic fault signatures, making it a highly valuable tool for wind turbine condition monitoring.

The FFT is an optimised algorithm to compute the DFT efficiently with $O\left(NlogN\right)$ complexity instead of $O\left(N^2\right)$.

The FFT follows the same DFT formula but computes it efficiently using techniques like divide and conquer and bit-reversal ordering, as shown in Equation (1).

$$X\left(k\right)={\sum }_{n=0}^{N-1}x\left(n\right)e^{-j2\pi kn/N}$$

(1)

The Fast Fourier Transform (FFT), a computationally efficient implementation of the Discrete Fourier Transform (DFT) [142,143], is a valuable tool for fault diagnosis, as it converts time-domain waveforms into their corresponding frequency-domain representations. Frequency-domain techniques, including amplitude spectra and power spectra, are commonly used to detect bearing faults through vibration frequency patterns. Compared to time-domain analysis, these methods offer a deeper understanding of fault characteristics, enabling more precise fault localization. However, they are best suited for stationary signals, which limits their effectiveness under the dynamic operating conditions typical of wind turbines.

The DFT converts a discrete-time signal from the time domain into the frequency domain as shown in Equation (2).

$$X\left(k\right)={\sum }_{n=0}^{N-1}x\left(n\right)e^{-j2\pi kn/N}, k=0,1,\dots ,N-1$$

(2)

where

• $X\left(k\right)$ is the DFT coefficient at frequency index $k$.
• $x\left(n\right)$ is the time-domain signal sampled at $n$.
• $N$ is the total number of samples.
• $j$ is the imaginary unit $(j^2= -1)$.
• $e^{-j2\pi kn/N}$ represents the complex exponential basis function, capturing periodicities in the signal.

The Inverse DFT (IDFT) reconstructs the time-domain signal as shown below in Equation (3):

$$x\left(n\right)={\displaystyle \frac{1}{N}}{\sum }_{k=0}^{N-1}X\left(k\right)e^{-j2\pi kn/N}$$

(3)

When faults, such as bearing damage, occur, the vibration frequency distribution deviates from the standard Gaussian pattern, indicating early-stage deterioration [144,145]. One key advantage of frequency-domain analysis over time-domain methods is its ability to distinguish distinct frequency components, enabling the early detection of faults well before a catastrophic failure occurs [146].

Short-Time Fourier Transform (STFT) has shown remarkable promise for fault detection, especially in wind turbines. Some examples are an STFT-based inverter fault detection technique presented in [147] and an STFT-based spectral examination technique for detecting wind power converter open-circuit faults in [141]. Avdakovic et al. (2024) [148] surveyed the Short-Time Fourier Transform (STFT) for wind turbine fault diagnosis through examination of time-variant frequency components. Through localized frequency analysis, STFT enhances fault detection, making it a valuable tool for real-time condition monitoring and predictive maintenance of wind turbines.

The STFT extends the conventional Fourier Transform by analyzing localized time-frequency components, making it particularly effective for non-stationary signals, as illustrated in Equation (4).

$${STFT}_x\left(m,k\right)={\sum }_{-\infty }^{+\infty }x\left(n\right)w(n-m)e^{-j2\pi kn/N}$$

(4)

where

• ${STFT}_x\left(m,k\right)$ is the time-frequency representation of $x\left(n\right)$.
• $w\left(n\right)$ is the window function (e.g., Hamming, Gaussian) that segments the signal.
• $m$ represents the time shift of the window.
• $k$ represents the frequency bin.

The Inverse STFT (ISTFT) reconstructs the original signal as shown below in Equation (5):

$$x(n)={\sum }_m{\sum }_k{STFT}_x(m,k)e^{-{\displaystyle \frac{j2\pi kn}{N}}}w(n-m)$$

(5)

Wavelet Transform

The Wavelet Transform (WT) is an effective time-frequency method that decomposes a signal into different frequency components, allowing analysis from coarse to fine resolution [149]. Unlike traditional frequency-domain methods, WT provides both time and frequency information, making it well suited for detecting time-varying faults in wind turbines [150]. A key feature of WT is its multi-resolution capability: by appropriately selecting translation and scale factors, it can apply a telescopic window function that offers high temporal resolution for high-frequency components and high frequency resolution for low-frequency components. This enables the detection of transient faults in rotating parts such as bearings, gears, and shafts as damage develops. As a result, WT has been widely applied for vibration monitoring and fault detection in wind turbine converters [75].

In contrast, time-domain analysis assesses system health by calculating statistical indicators, such as waveform features. While these time-domain features can signal the presence of a fault, they typically cannot identify the fault’s type, location, or severity, which limits their effectiveness for precise diagnostics. WT overcomes these limitations by offering a time-frequency representation, which explicitly decomposes variations in the signals [151]. By separating the signal into different frequency bands, WT can effectively detect anomalies arising from misalignment, bearing wear, gear faults, and structural imbalances.

Scientists have used WT for the detection of gear and bearing failures [97,152] due to its capability for emphasising early-stage faults before extensive damage. Moreover, an FFT-based amplitude estimate of wavelet coefficients has been utilised for differentiating standard components from faulty ones, further improving diagnostic performance [153]. Researchers are advised to optimise window parameters for enhanced accuracy in diagnostics for turbines. Mathematical representation of Continuous Wavelet Transform (CWT) of a signal $x\left(t\right)$ is defined in Equation (6):

$$W_x\left(a,b\right)={\int }_{-\infty }^{+\infty }{x\left(t\right)\psi }^{*}\left({\displaystyle \frac{t-b}{a}}\right)d$$

(6)

where

• $W_x(a, b)$ is the wavelet coefficient, representing how much the signal correlates with a wavelet at a given scale and position.
• $x\left(t\right)$ is the original signal (e.g., vibration signals from a wind turbine).
• $\psi \left(t\right)$ is the mother wavelet, a predefined function localised in time and frequency.
• $a$ is the scale factor, controlling the frequency resolution of the wavelet.
• $b$ is the translation factor, determining the time localisation of the wavelet.
• ∗ represents the complex conjugate of the wavelet function.

In practice, the application uses the Discrete Wavelet Transform (DWT), where the scale $a$ and translation $b$ are discretised. It is typically implemented with the dyadic scale (powers of two) and shift based on the scale.

Mathematical representation of the DWT of a signal $x\left(t\right)$ is expressed in Equation (7):

$$W_x\left(m,n\right)={\sum }_{-\infty }^{+\infty }x(k){\psi }_{m,n}^{*}(k)$$

(7)

where

• $W_x\left(m,n\right)$ is the wavelet coefficient, representing how much the signal correlates with the wavelet at scale $m$ and position $n$.
• $x\left(k\right)$ is the discrete signal, typically sampled from the original continuous-time signal $x\left(t\right)$.
• ${\psi }_{m,n}\left(k\right)$ is the discrete wavelet function, given by Equation (8):

$${\psi }_{m,n}\left(k\right)={\displaystyle \frac{1}{\sqrt{2^m}}}\psi \left({\displaystyle \frac{k-n{2}^m}{2^m}}\right)$$

(8)

where

• $m$ controls the scale (frequency resolution).
• $n$ controls the translation (time shift).
• $\psi \left(t\right)$ is the mother wavelet.

Hilbert-Huang Transform (HHT)

Traditional signal processing methods rely on assumptions of linearity and stationarity, which limits their effectiveness in handling the nonlinear and non-stationary characteristics of wind turbine fault signals. To overcome these challenges, Huang et al. [154] proposed the Hilbert-Huang Transform (HHT), an adaptive time-frequency analysis technique specifically designed to address such complexities. Unlike spectrograms, wavelet analysis, or the Wigner-Ville Distribution, HHT does not rely on a predefined functional basis. This adaptability makes it particularly effective for revealing fault-related characteristics in complex, nonlinear signals [155].

HHT is carried out in two main stages:

(a)

Empirical Mode Decomposition (EMD)—This step decomposes the signal into a set of Intrinsic Mode Functions (IMFs).

(b)

Hilbert Spectral Analysis (HSA)—Each IMF is then analysed to compute its instantaneous amplitude and frequency, producing a detailed time-frequency representation.

Compared with the Wavelet Transform (WT) and Fourier Transform (FT), HHT is highly adaptive, making it particularly well-suited for analysing nonlinear, non-stationary, transient, and time-dependent faults [156,157]. It is reported in research works [158,159,160,161] that HHT is one of the best Non-Destructive Testing (NDT) methods for processing signals in wind turbine fault diagnosis.

Empirical Mode Decomposition (EMD)

EMD is the first stage of the HHT. It enables the decomposition of complex vibration signals into a series of IMFs [156]. Unlike traditional frequency-based approaches, EMD is highly adaptive and particularly effective for capturing non-stationary signals, making it well suited for diagnosing wind turbine faults. Ahmar et al. (2010) [162] examined the application of EMD for identifying faults in wind turbine systems by breaking down complicated signals into a collection of intrinsic mode functions, which are simpler. The research highlights the promise of applying EMD with the aid of complementary denoising mechanisms.

Each IMF must satisfy two key conditions:

(a)

The number of extrema and zero-crossings should be equal or differ by no more than one.

(b)

The mean value of the envelope defined by the local maxima and minima must be zero.

For a given signal x(t), the EMD algorithm proceeds through the following steps:

(a)

Identify all local maxima and minima.

(b)

Generate the upper and lower envelopes by applying cubic spline interpolation.

(c)

Compute the mean envelope using Equation (9):

$$m\left(t\right)={\displaystyle \frac{e_{max}\left(t\right)+e_{min}\left(t\right)}{2}}$$

(9)

(d)

Extract the detail component:

$$d\left(t\right)=x\left(t\right)-m\left(t\right)$$

(e)

Repeat the process on $\mathrm{m}\left(\mathrm{t}\right)$ until the signal is fully decomposed into IMFs.

A sifting process further refines the decomposition to ensure that each IMF satisfies the zero-mean property. This iterative procedure continues until only a residual trend remains, as defined by Equations (10) and (11):

$$x(t)={\sum }_{i=1}^N{C}_i(t)+r(t)$$

(10)

$$IO={\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{j=i=1}^n{C}_i(t)C_j(t)}{{\sum }_{i=1}^n{C}_i^2(t)}}$$

(11)

A lower $IO$ value indicates a more precise decomposition.

Hilbert Transform

After decomposing the signal with EMD, the Hilbert Transform (HT) is applied to obtain the instantaneous amplitude and frequency. This information is essential for detecting faults in rotating components of wind turbines.

For a given time-series signal $\mathrm{x}\left(\mathrm{t}\right)$, the Hilbert Transform $\mathrm{y}\left(\mathrm{t}\right)$ is defined as in Equation (12):

$$y\left(t\right)={\displaystyle \frac{1}{\pi }}P{\int }_{-\infty }^{+\infty }{\displaystyle \frac{x\left(\tau \right)}{t-\tau }}d\tau$$

(12)

where

$\mathrm{a}\left(\mathrm{t}\right)$ is instantaneous amplitude, representing how the signal’s energy varies over time:

$$a(t)=\sqrt{x^2(t)+y^2(t)}$$

$\mathsf{\phi }\left(\mathrm{t}\right)$ is instantaneous phase, describing the signal’s evolution in time:

$$\mathsf{\phi }\left(\mathrm{t}\right)=\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{t}\mathrm{a}\mathrm{n}{\displaystyle \frac{y\left(t\right)}{x\left(t\right)}}$$

A key property of the Hilbert Transform is that for a mono-component signal, the derivative of the instantaneous phase provides the instantaneous frequency:

$$\omega \left(t\right)={\displaystyle \frac{d\varphi \left(t\right)}{dt}}$$

Hilbert Spectral Analysis (HHT)

Huang et al. [154] extended this approach by applying the Hilbert Transform to each Intrinsic Mode Function (IMF) extracted using EMD. The original signal can then be expressed as Equation (13):

$$x\left(t\right)={\sum }_{j=1}^n{a}_j\left(t\right)e^{i\int wj\left(t\right)dt}$$

(13)

where each IMF’s instantaneous frequency and amplitude can be visualised in a three-dimensional representation, known as the Hilbert-Huang Spectrum $\left(\mathrm{H}\right(\mathsf{\omega },\mathrm{t}\left)\right)$.

From this, the marginal spectrum $\mathrm{h}\left(\mathsf{\omega }\right)$ is calculated by Equation (14):

$$h(\omega )={\int }_0^{T}H(\omega ,t)dt$$

(14)

where $\mathrm{h}\left(\mathsf{\omega }\right)$ measures the contribution of each frequency component, while instantaneous energy E(t) provides insight into the time-varying energy distribution of the signal.

The Hilbert-Huang Transform (HHT)—a combination of EMD and Hilbert Transform—has shown great success in detecting wind turbine faults, particularly in generator current signals [163].

3.1.3. Statistical Methods

Statistical techniques play a key role in trend monitoring by analyzing historical data to detect potential faults in wind turbines. Methods such as moving averages, regression analysis [38], and principal component analysis (PCA) [164] help reveal underlying trends and identify anomalies, making them valuable tools for predictive maintenance. These techniques are well established and rely on key statistical features—such as histogram analysis [165], mean [166], standard deviation [167], root mean square [168], skewness [169], and kurtosis [170]—characterise different fault modes in wind turbines, spanning both electrical and mechanical components.

Tan et al. [166] proposed a correlation-based feature approach—using mean and covariance measures—for wind turbine fault detection, demonstrating strong performance under variable wind conditions due to its low computational cost and fast calculation time. Additionally, variations in signal distortion and envelope patterns from mechanical and electrical components have been explored as features for fault detection [171,172]. In contrast, principal component energy and proportionally related signals have been employed to develop fault indicators [173]. Despite their advantages, statistical analysis-based methods are sensitive to noise, load fluctuations, and varying operating conditions, which can affect their effectiveness in detecting early-stage faults and assessing the long-term durability of wind turbines. Over the past few years, cointegration theory—initially developed in econometrics and statistics—has garnered significant attention in the fields of structural health monitoring and condition monitoring. Its key advantage is its ability to account for or eliminate trends arising from variations in environmental and operational conditions, allowing for more reliable detection of damage and faults.

Cointegration Analysis

Let $Y_t=(y_{1t},y_{2t},\dots ,y_{nt}{)}^T$ be an $(n\times 1)$ vector of non-stationary time series. It is said to be linearly cointegrated if there is a vector $\beta =({\beta }_1,{\beta }_2,\dots ,{\beta }_n{)}^T$ such that

$${\beta }^T{Y}_t={\beta }_1{y}_{1t}+{\beta }_2{y}_{2t}+\dots +{\beta }_n{y}_{nt}$$

(15)

is stationary. The linear combination, defined as $u_t={\beta }^T{Y}_t+c$, where $c$ is an arbitrary constant, is termed a cointegration residual, indicating a long-run equilibrium relationship amongst cointegrated time series [174,175]. The vector $\beta$ is termed the cointegrating vector. Practically, it is convenient to employ a normalised cointegrating vector $\beta$, with the form shown as Equation (16).

$$\beta =(1,-{\beta }_2,\dots ,-{\beta }_n{)}^T$$

(16)

Using this normalised cointegrating vector, the cointegration relationship in Equation (17) will have a new form

$${\beta }^T{Y}_t=y_{1t}-{\beta }_2{y}_{2t}-\dots -{\beta }_n{y}_{nt}$$

(17)

Then, the forming of a cointegration residual (i.e., $u_t={\beta }^T{Y}_t+c$) can be understood as projecting $n$ vectors of time series in $Y_t$ On a non-normalised integrating vector $\beta$.

So, the primary issue for the cointegration methodology is to estimate appropriate normalised cointegrating vectors to obtain stationary residuals. Johansen’s cointegration methodology [176], an iterative algorithm based on the framework of maximum likelihood estimation, is popularly used for that purpose. The theoretical framework of that methodology is complex and is therefore not included herein. Additional theoretical details may be obtained from the original article [176], whereas its more straightforward explanation is given in [177,178].

The cointegration theory, originally developed within econometrics and statistics [175,176], has shown promising applications in the area of SHM and damage detection [177,178,179,180,181,182,183,184,185,186]. Recent studies also highlight its potential for condition monitoring and fault detection in wind turbines, as reflected in the current literature. In [187,188,189], a cointegration-based condition monitoring tool was studied and tested using a SCADA dataset provided from a 2 MW wind turbine drivetrain subject to changing environmental and operational conditions. The data included a gearbox fault event. The results confirmed the effectiveness of the proposed methodology in eliminating nonlinear trends from the data, enabling continuous monitoring of the wind turbine and precise identification of the gearbox fault. A further cointegration-based method was established in [190] for effectively monitoring the abnormal operation of the generator and gearbox, enabling early fault identification. SCADA data registered from a 1.5 MW wind turbine working under environmental and operational variations were utilised in building a model using cointegration for identifying a certain number of gearbox fault data in [191]. In research [192], vibration data were analysed using cointegration analysis to determine damage in a wind turbine blade due to environmental conditions.

Cointegration was found to detect damage even in scenarios where direct discrimination between damage and environmental effects was complex. In [193], a Bayesian multivariate cointegration model for vibration-based damage identification was proposed to determine the slow degradation in a wind turbine blade. Both ML and cointegration analysis have been suggested for wind turbine monitoring and fault detection, as stated in [194]. Very recently, research in [195] suggested a monitoring scheme—using a combination of the sparse cointegration analysis and independent component analysis—to monitor faults in wind turbines. Recently, research in [196] proposed a damage evaluation method based on frequency domain decomposition and cointegration analysis for offshore platforms under wind and wave loads. Due to the significance of monitoring and eliminating nonlinear trends that emerge between wind turbine parameters, as well as between these parameters and environmental parameters like wind speed and air temperature, research in [197] formulated a homoscedastic nonlinear cointegration methodology for operational state monitoring and wind turbine fault detection.

3.2. Overview of CPD and Its Methods

CPD is the process of identifying moments when a system’s intrinsic properties undergo sudden or gradual changes [22]. The concept dates back to the 1950s, when Page [198] developed statistical methods to detect shifts in industrial production lines. Early research focused on identifying abrupt changes in the mean of independent and identically distributed (i.i.d.) Gaussian variables [199], primarily for manufacturing quality control [3]. With the advent of increased computational power and enhanced data collection capabilities, CPD has expanded far beyond its industrial origins. Today, it is widely applied across diverse fields, including Speech Processing [200,201,202,203], Financial Markets [204,205,206,207], Bioinformatics [208], Climatology [5,10,209], and Network Security [210,211]. CPD methods can be categorised into the following broad types as shown in Figure 9 [34]:

In this review paper, only statistical distribution-based CPD [212,213], cumulative sum-based CPD [29,30], and likelihood-based CPD [43] are introduced, since wind turbine faults cause drastic distribution changes in sensor data [21,214,215], which these methods can effectively identify [213].

(1)

Kernel Density Estimation (KDE);

(2)

Kullback–Leibler (KL) Divergence;

(3)

Jensen-Shannon Divergence (JSD);

(4)

Bayesian Online Change Point Detection (BOCPD);

(5)

CUSUM.

3.2.1. Kernel Density Estimation (KDE)

The KDE is a non-parametric estimate of a Probability Density Function (PDF) for a random variable [216]. It is a standard tool for fault detection in wind turbines, where the distribution of vibration signals is inspected to identify anomalies. Letzgus (2020) [36] presented a kernel-based change-point detection method appropriate for SCADA data for wind turbine condition monitoring. It identifies abnormalities in standard behaviour models with nonlinear kernel functions. Saravanan et al. (2024) [217] presented the Kernel-based Cumulative Sum (KCUSUM) algorithm for real-time adaptive sampling and change-point detection problems. It utilises kernel density estimates for improved detection of subtle variations in data distribution. KCUSUM is suitable for nonlinear and high-dimensional data, as it dynamically adapts to the data patterns embedded within the data.

The KDE estimate of a probability density function $f\left(x\right)$ from a set of $n$ samples $x_i$ is given by Equation (18):

$$\widehat{f}\left(x\right)={\displaystyle \frac{1}{nh}}{\sum }_{i=1}^n\left({\displaystyle \frac{x-x_i}{h}}\right)$$

(18)

The kernel function $K\left(u\right)$ must satisfy the properties of a probability density function (PDF), i.e., it must integrate to 1.

The most common version of the Gaussian Kernel is given by Equation (19):

$$K\left(u\right)={\displaystyle \frac{1}{\sqrt{2\pi }}}{e}^{-{\displaystyle \frac{1}{2}}{u}^2}$$

(19)

Epanechnikov Kernel is given by Equation (20):

$$K(u)=\left\{\begin{array}{cc}{\displaystyle \frac{3}{4}}\left(1-u^2\right),\hfill & if \left|u\right|\le 1\hfill \\ \hfill 0, \hfill & Otherwise\hfill \end{array}\right.$$

(20)

Uniform Kernel is given by Equation (21):

$$K(u)=\left\{\begin{array}{ll}{\displaystyle \frac{1}{2}},& if \left|u\right|\le 1\\ 0, & Otherwise\end{array}\right.$$

(21)

(a)

$\widehat{f}\left(x\right)$ is the estimated probability density function (PDF) at point $x$.

(b)

$n$ is the number of data points.

(c)

$h$ is the bandwidth (smoothing parameter) that controls the smoothness of the density estimate.

(d)

Small $h$: KDE captures more details but may lead to overfitting (high variance).

(e)

Large $h$: KDE becomes smoother, but essential features might be lost (high bias).

(f)

Optimal $h$: Often selected using Silverman’s rule of thumb: $h=1.06\sigma n^{-1/5}$

(g)

$K(\cdot )$ is the kernel function, which determines the shape of the contribution of each data point.

(h)

$\sigma$ is the standard deviation of the data.

3.2.2. KL Divergence

The Kullback–Leibler (KL) divergence is a measure of how one probability distribution $P\left(x\right)$ differs from a second reference probability distribution $Q\left(x\right)$ [218,219]. It is widely used in fault detection for wind turbines to compare normal and faulty signal distributions.

The KL divergence is defined by Equation (22):

$$D_{KL} (P||Q)={\sum }_{x}P\left(x\right)log{\displaystyle \frac{P\left(x\right)}{Q\left(x\right)}}$$

(22)

Or it can be written in the continuous form as shown in Equation (23):

$$D_{KL} (P||Q)=\int P\left(x\right)log{\displaystyle \frac{P\left(x\right)}{Q\left(x\right)}}dx$$

(23)

where

(a)

$P\left(x\right)$ represents the actual probability distribution of the signal (e.g., normal operating condition).

(b)

$Q\left(x\right)$ represents the approximate or reference distribution (e.g., a distribution under a faulty condition).

(c)

The logarithm is typically base 2 (bits) or natural log (nats).

KL divergence is not symmetric, meaning:

$$D_{KL} \left(P\right|\left|Q\right)\ne D_{KL} \left(P\right|\left|Q\right)$$

3.2.3. Jensen-Shannon Divergence (JSD)

The Jensen-Shannon Divergence (JSD) is a symmetrised and smoothed form of the Kullback–Leibler (KL) divergence. It can be used for comparing the similarity between two probability distributions. For wind turbine fault detection, JSD can be used for: (1) Comparing normal and faulty conditions’ distributions of vibration signals. (2) Determining power output distribution variations for generator faults. (3) Determining temperature variations for gearboxes, bearings, or lubricating systems. Since KL divergence is directional and possibly infinite in the case of distributions with zero probabilities, JSD provides a stabler, more interpretable distributional difference measure.

Given two probability distributions $P\left(x\right)$ and $Q\left(x\right)$, the Jensen-Shannon Divergence is defined by Equation (24):

$$J_{SD} (P||Q)= {\displaystyle \frac{1}{2}}{D}_{KL} (P|\left|M\right)+{\displaystyle \frac{1}{2}}{D}_{KL} (Q|\left|M\right)$$

(24)

where

• $D_{KL} (P||Q)$ is the Kullback–Leibler divergence.
• $M$ is the mixture distribution and can be written as in Equation (25):

$$M\left(x\right)={\displaystyle \frac{1}{2}}(P\left(x\right)+Q\left(x\right))$$

(25)

The KL divergence terms are written as in Equations (26) and (27):

$$D_{KL} (P||M)={\sum }_{x}P\left(x\right)log{\displaystyle \frac{P\left(x\right)}{M\left(x\right)}}$$

(26)

$$D_{KL} (Q||M)={\sum }_{x}P\left(x\right)log{\displaystyle \frac{Q\left(x\right)}{M\left(x\right)}}$$

(27)

For the continuous case, JSD is expressed as in Equation (28):

$$D_{JS} (P||Q)={\displaystyle \frac{1}{2}}\int P\left(x\right)log{\displaystyle \frac{P\left(x\right)}{M\left(x\right)}}dx+ {\displaystyle \frac{1}{2}}\int Q\left(x\right)log{\displaystyle \frac{Q\left(x\right)}{M\left(x\right)}}dx$$

(28)

Since JSD is symmetric, we have:

$$D_{JS} \left(P\right|\left|Q\right)= D_{JS} \left(Q\right|\left|P\right)$$

Unlike KL divergence, JSD is always finite and is bounded between:

$$0\le D_{JS} \left(P\right|\left|Q\right)\le log2$$

3.2.4. Bayesian Online Change Point Detection (BOCPD)

Bayesian Online Change Point Detection (BOCPD) is a probabilistic approach for identifying abrupt changes in time-series data [220]. In the context of wind turbine fault detection, BOCPD can detect deviations from normal operational behavior caused by faults or physical degradation [27]. In 2024, Tsaknaki et al. [221] extended this approach through Bayesian Autoregressive Online Change-Point Detection with time-dependent parameters, using Bayesian inference to dynamically adapt autoregressive models to evolving conditions. This method is particularly well suited for non-stationary data, providing accurate real-time updates of model parameters.

The goal of BOCPD is to determine a run length $r_t$, which represents the time elapsed since the last detected change.

• Step 1: Compute the Run-Length Probability

The probability distribution over the run length is updated recursively as shown in Equation (29):

$$P(r_t\mid x_{1:t})\propto P(x_t\mid r_t,x_{t-r_{t:t-1}})P(r_t\mid r_{t-1})$$

(29)

where

• $r_t$ is the run length at time $t$.
• $x_t$ is the observed data (e.g., vibration amplitude, temperature).
• $P(r_t\mid r_{t-1})$ is the transition probability, typically modelled as a hazard function ${H(r}_t)$.
• $P(x_t\mid r_t,x_{t-r_{t:t-1}})$ is the likelihood of the new data given the current run length.

• Step 2: Define the Hazard Function${H(r}_t)$

The hazard function ${H(r}_t)$ defines the probability of a new change point occurring by using Equation (30):

$$P(r_t-0|r_{t-1})={H(r}_t)$$

(30)

where

• A constant hazard function (e.g., ${H(r}_t)=1/100$) assumes a fixed probability of change at each time step.
• A time-varying hazard function adapts based on external conditions (e.g., increasing failure rate over time in wind turbines).

• Step 3: Compute the Predictive Distribution

Given a statistical model $f\left(x_t\right|\theta )$, where $\theta$ represents parameters of the underlying distribution (e.g., Gaussian for vibration data), we compute the predictive distribution using Equation (31):

$$P\left(x_t∣r_t,x_{t-r_{t:t-1}}\right)= \int f\left(x_t|\theta \right)\mathrm{P}\left(\theta |x_{t-r_{t:t-1}}\right)d\theta$$

(31)

This helps in detecting deviations from the expected behaviour.

• Step 4: Update the Evidence

The total probability of a change point is computed by Equation (32):

$$P\left(x_t|x_{1:t-1}\right)={\sum }_{r_t}P\left(x_t∣r_t,x_{t-r_{t:t-1}}\right)P\left(x_t|x_{1:t-1}\right)$$

(32)

Using Bayesian inference, we update our belief about the presence of a change point.

3.2.5. CUSUM

The CUSUM control chart is a sequential change detection algorithm for identifying small changes in system behaviour over time [29]. In wind turbine fault detection, CUSUM is also used for tracking operating parameter deviations in vibration, power production, temperature, etc., to help detect faults early [29]. CUSUM is particularly well-suited when (1) faults develop gradually rather than abruptly. (2) Even a minimal deviation in the system tends to accumulate over time, leading to a breakdown. (3) One needs a real-time, computationally simple monitoring system. CUSUM detects changes by accumulating deviations of incoming data points from a reference value, typically the mean under normal operating conditions. It is commonly implemented in two main forms: Standard CUSUM and Stopping Criterion.

(a)

Standard CUSUM (One-Sided)

CUSUM maintains two cumulative sums:

(1)

Upper CUSUM $S_t^{-}$: Detects an increase in mean.

(2)

Lower CUSUM $S_t^{+}$: Detects a decrease in mean.

The updated Equations (33) and (34) are given below:

$$S_t^{-}=\mathrm{m}\mathrm{a}\mathrm{x}(0,S_{t-1}^{+}+\left(x_t{-\mu }_0-k\right))$$

(33)

$$S_t^{+}=\mathrm{m}\mathrm{a}\mathrm{x}(0,S_{t-1}^{-}+\left({\mu }_0-x_t-k\right))$$

(34)

where

• $S_t^{-}, S_t^{+}$ are the upper and lower cumulative sums at time $t$.
• $x_t$ is the observed value at time $t$ (e.g., vibration level, temperature).
• ${\mu }_0$ is the baseline mean value under normal conditions.
• $k$ is the drift threshold, which defines the minimum shift in mean that should be detected (typically $k= \delta /2$, where $\delta$ is the desired detectable shift).
• The max (0, …) ensures the cumulative sum resets when the deviation is within normal limits.

(b)

Stopping Criterion (Threshold $h$)

A change is detected when:

$$S_t^{-}>h or S_t^{+}>h$$

where $h$ is a predefined decision threshold, if the cumulative sum exceeds $h$, an alarm is triggered, indicating a potential fault.

4. The Need for Integration of TM and CPD

Effective structural health monitoring and condition monitoring in wind turbines is essential for preventing mechanical failures, optimizing maintenance schedules, and ensuring long-term operational efficiency. Researchers have investigated a variety of diagnostic approaches, focusing particularly on vibration-based techniques and advanced signal processing methods, to enhance fault detection performance. Guo et al. [47] successfully diagnosed generator bearing faults using EMD combined with fast spectral warping analysis of generator speed signals. While this approach outperformed conventional fault diagnosis methods, it suffered from endpoint effects, including mode mixing, which reduced diagnostic accuracy. To further improve fault detection, Wang et al. [48] employed wavelet transforms for data preprocessing and utilized a SVM optimized with particle swarm and genetic algorithms, achieving higher classification accuracy than traditional methods. Similarly, Li et al. [49] applied Kernel Principal Component Analysis (KPCA) for eigenvector extraction and combined it with a SVM optimized via particle swarm optimization to diagnose asymmetrical motor faults, demonstrating strong capability in nonlinear pattern recognition. In later work, Li et al. [222] employed KPCR to integrate nonlinear features, improving fault detection accuracy while reducing computational time. Their approach combined current and vibration signals, effectively addressing the limitations of single-input models. Zhang et al. [223] proposed a comprehensive approach that incorporates wavelet transform, feature inspection, judgment, and backpropagation neural networks for fault diagnosis in wind turbine systems. Their approach enhanced data divergence with wavelet-based reconstruction and decomposition, providing refined signals into a neural network for improved classification. Heibati et al. (2023) [224] used the Fourier Transform (FT) to analyze frequency-domain variations in wind turbine signals for fault detection, particularly in gears and bearings. FT effectively identifies periodic components in stationary signals, providing a straightforward and useful tool for condition monitoring. However, its limited ability to handle non-stationary signals—a common feature in wind turbines operating under variable conditions—underscores the importance of combining FT with other techniques to improve diagnostic reliability. Ogaili et al. (2024) [140] demonstrated the use of WT combined with eXtreme Gradient Boosting (XGBoost) to detect faults in wind turbine blades based on non-stationary vibration signals. The strength of WT lies in its high time-frequency resolution, enabling precise identification of transient faults. The study highlights WT as a highly effective tool for fault detection, particularly when integrated with advanced deep learning models. Jin et al. (2021) [20] focused on the characterization of SCADA data for monitoring wind turbine generator performance. Their approach combined statistical and signal processing techniques to detect anomalies in operational parameters. This method is versatile, capable of identifying both gradual and abrupt changes in turbine behavior, making it widely applicable in condition monitoring. Wang et al. (2014) [60] employed SCADA data for vibration-based condition monitoring of wind turbines. Their method combined statistical and frequency-domain techniques to detect abnormal vibration patterns. The approach is notable for its ease of implementation and its effectiveness in identifying mechanical faults.

4.1. Why Are Change Point Detection (CPD) or Trend Monitoring Alone Not Enough?

Traditional fault detection approaches in wind turbine monitoring often employ either TM or CPD, but each method has inherent limitations when used independently.

4.1.1. Limitations of Trend Monitoring (TM)

TM-based techniques, such as Fast Fourier Transform, Wavelet Transform [8,15,23], and Hilbert-Huang Transform [225,226], analyse signal variations to detect mechanical imbalances, gear misalignment, or bearing wear. These methods are highly effective at capturing changes in a signal’s frequency, amplitude, and overall trend. However, they have limitations in pinpointing the exact time at which a fault occurs. For example, FFT can reveal the presence of high-frequency components indicative of mechanical wear, but it cannot determine when the damage started. This makes it difficult to distinguish between gradual wear, which may allow for planned maintenance, and abrupt failures, which require immediate intervention. Moreover, TM-based methods are prone to both false positives and false negatives because they often lack statistical validation to distinguish significant changes from normal operational variations. For example, wavelet analysis can detect transient fluctuations in a signal, but without an appropriate statistical threshold, it is difficult to determine whether these changes indicate a genuine fault. Additionally, TM techniques are not inherently designed for real-time monitoring, which can limit their effectiveness in providing early warnings and reducing turbine downtime and maintenance costs.

4.1.2. Limitations of Change Point Detection (CPD)

In contrast, CPD-based methods, such as Kernel Density Estimation [227,228,229,230,231], Kullback-Leibler Divergence [218,219,232,233,234,235,236,237], Jensen-Shannon Divergence [201,202,203,238,239,240,241], Bayesian Online Change Point Detection [25,26,27,28,242], and Cumulative Sum [29,30,243,244,245,246,247], are specifically designed to detect abrupt changes in wind turbine operational data. By monitoring statistical variations, CPD methods can accurately pinpoint the exact time or location at which these changes occur, providing precise identification of anomalies in turbine performance. However, CPD alone does not convey the physical characteristics of the detected change. For example, while a method like BOCPD may signal the onset of a fault, it does not provide information about its cause, which could be a frequency shift, an increase in vibration amplitude, or a new transient variation. Because CPD lacks the ability to classify faults, it cannot serve as a standalone diagnostic tool. Furthermore, CPD is highly sensitive to operational variations and noise, which are common in wind turbine environments. Sensor data can fluctuate due to external factors such as wind speed changes, load variations, and environmental conditions, increasing the risk of false alarms. Without contextual analysis provided by TM, CPD may misinterpret minor fluctuations as faults, leading to unnecessary maintenance and higher operational costs. Additionally, CPD typically relies on aggregated statistical models rather than raw sensor signals, which means that valuable frequency-domain information captured by TM can be overlooked.

4.2. How Does Combining TM & CPD Improve Fault Detection?

By combining TM and CPD, fault detection in wind turbines can be significantly improved due to the complementary strengths of the two approaches. TM techniques, such as FFT, WT, and HHT, provide physical insights into how a turbine’s operational behavior deviates from normal patterns, revealing indicators of mechanical faults [248,249,250]. Methods like HHT and Adaptive Filtering Denoising (AFD) capture amplitude variations in vibration signals, while WT is effective for detecting sudden or drastic changes in the signal [243,251]. By leveraging these complementary capabilities, TM and CPD together enable both precise detection of change points and a deeper understanding of the underlying fault characteristics. While CPD methods provide statistical evidence for the timing of fault occurrences, their approaches differ in focus. Methods such as BOCPD, Kullback–Leibler Divergence (KL Divergence), and Kernel Density Estimation (KDE) aim to pinpoint the exact moment a fault begins [218,219,235]. In contrast, techniques like CUSUM and Jensen-Shannon Divergence (JSD) monitor cumulative deviations, making them more suitable for detecting gradually developing faults [29,30,239,240,241]. Integrating TM and CPD enables early, accurate, and reliable fault detection, helping to minimize downtime, optimize maintenance schedules, and prevent severe failures.

Table 5. Trend Monitoring Meets Change-Point Detection for Fault Diagnosis.

Feature	Trend Monitoring (TM)	Change Point Detection (CPD)	Integrated TM + CPD Approach
Detects What Changed?	Yes. TM methods analyse frequency shifts, amplitude changes, and transient fluctuations in wind turbine signals.	No. CPD does not describe what changed, only that a change has occurred.	Best of both. TM identifies what kind of fault is occurring, and CPD validates the significance of the change.
Detects When It Happened?	No. TM identifies trends over time but lacks precise timing accuracy.	Yes. CPD pinpoints the exact moment of change.	TM detects the nature of change, and CPD determines the exact time of occurrence.
Distinguishes Fault from Normal Variations?	No. TM methods may incorrectly interpret minor operational fluctuations as faults, resulting in false positives.	Yes. CPD applies statistical thresholds to confirm whether a change is significant.	TM identifies trends, while CPD quantifies whether the change is a genuine fault or just normal variation.
Useful for Real-Time Monitoring?	Not always. Some TM methods (e.g., FFT) require post-processing, which makes them less ideal for real-time fault detection.	Yes. CPD methods, such as BOCPD and CUSUM, operate in real-time, making them suitable for continuous condition monitoring.	TM identifies potential faults, and CPD confirms them in real-time, enabling faster maintenance response.
Works for Both Sudden and Gradual Faults?	No. TM works well for sudden changes (e.g., FFT, WT) but struggles with gradual deterioration.	Yes. CPD methods, such as CUSUM, can track slowly developing faults over time.	TM detects changes, and CPD helps differentiate sudden failures from progressive degradation.
Helps in Fault Classification?	Yes. TM methods, such as the Wavelet Transform and HHT, help distinguish between blade damage, gear misalignment, and bearing faults.	No. CPD only detects when a fault occurs, without explaining its nature.	TM provides fault classification, while CPD adds statistical validation, improving diagnostic accuracy.
Reduces False Alarms?	No. TM alone can misinterpret external environmental changes (such as wind variations and temperature fluctuations) as faults.	Yes. CPD ensures only statistically significant changes are flagged.	TM identifies anomalies, and CPD filters out false alarms, making fault detection more reliable.
Helps Predict Future Failures?	No. TM detects ongoing changes but does not forecast when failure might happen.	Yes. CPD (CUSUM, BOCPD) tracks fault progression and helps predict failures before they become critical.	TM provides trending insights, and CPD enables predictive maintenance, preventing unexpected failures.

summarizes the strengths and limitations of each approach, highlighting how their combination creates a highly effective system for detecting faults in wind turbines.

4.3. Selecting the Optimal TM-CPD Combination for Wind Turbine Fault Detection

Wind turbines are subjected to varying loads, weather conditions, and environmental stresses, making adaptability, precision, and computational efficiency critical for effective fault detection. A well-designed TM-CPD hybrid framework leverages the strengths of both techniques to deliver early, accurate, and reliable detection of faults. Achieving an optimal integration requires careful consideration of several factors: (1) the type of fault (sudden versus gradual), (2) the nature of available data (historical versus real-time), and (3) trade-offs between sensitivity and accuracy. Additionally, aspects such as computational cost, interpretability, and robustness under varying operating conditions must be addressed to ensure efficient and practical monitoring.

4.3.1. Trade-Offs in TM-CPD Methods

Different TM and CPD methods involve inherent trade-offs. Some approaches offer high accuracy but come with significant computational costs, limiting their suitability for real-time monitoring, whereas others are computationally efficient but provide lower accuracy.

Table 6. Performance Evaluation of TM and CPD Techniques in Wind Turbine Fault Monitoring.

Method	Category	Computational Cost	Real-Time Efficiency	Accuracy	Best Used For
Fast Fourier Transform (FFT)	TM—Frequency Analysis	Low	High	Moderate	Detecting frequency shifts in wind turbine vibrations.
Wavelet Transform (WT)	TM—Time-Frequency Analysis	High	Moderate	High	Identifying transient faults and multi-frequency changes.
Hilbert-Huang Transform (HHT)	TM—Instantaneous Frequency Analysis	High	Low	High	Tracking nonlinear and real-time frequency variations.
Empirical Mode Decomposition (EMD)	TM—Signal Decomposition	High	Moderate	High	Decomposing complex signals into intrinsic mode functions for analysing non-stationary faults.
Adaptive Filtering Denoising (AFD)	TM—Signal Smoothing	Low	High	Moderate	Preprocessing and noise reduction before fault detection.
Kernel Density Estimation (KDE)	CPD—Statistical Distribution	High	Low	High	Detecting statistical shifts in vibration distributions.
Kullback–Leibler Divergence (KL Divergence)	CPD—Statistical Divergence	High	Low	High	Measuring how much two distributions differ for fault detection.
Jensen-Shannon Divergence (JSD)	CPD—Statistical Divergence	Moderate	Moderate	High	Capturing statistical changes in turbine operation over time.
Bayesian Online Change Point Detection (BOCPD)	CPD—Bayesian Real-Time Detection	High	High	High	Detecting real-time change points for sudden faults.
Cumulative Sum (CUSUM)	CPD—Cumulative Sum-Based Detection	Low	High	Moderate	Tracking gradual long-term changes in turbine components.

summarises the key characteristics of various TM and CPD techniques, including their computational cost, real-time performance, and accuracy.

4.3.2. Type of Fault (Sudden or Gradual)

Faults in wind turbines can occur as either sudden failures or gradual degradations, requiring tailored TM-CPD strategies for effective detection and mitigation. Sudden faults—such as gearbox misalignment, blade cracks, or mechanical impacts—produce immediate changes in vibration amplitude, torque, or frequency. High-resolution TM methods, such as WT and HHT, are employed to capture these time-frequency variations and detect significant jumps. However, TM alone cannot confirm whether an observed anomaly represents a genuine fault. To ensure accurate detection, real-time statistical validation using methods like BOCPD and Kullback–Leibler (KL) Divergence is applied, so that maintenance actions are triggered only by actual faults. In contrast, slow-developing faults—such as bearing wear, rotor imbalance, and material fatigue—evolve gradually over extended periods, often producing low-frequency variations that become prominent only after significant damage has occurred. Time-domain monitoring techniques like EMD and Adaptive Filtering are effective for tracking these long-term signal trends. Because early detection is challenging, statistical tools such as CUSUM and Jensen-Shannon Divergence (JSD) are employed to monitor cumulative changes and long-term variations, supporting predictive, schedule-based maintenance to prevent unexpected failures and reduce downtime. By integrating TM methods for fault identification with CPD methods for statistical validation, wind turbine monitoring systems achieve higher accuracy, reliability, and proactiveness, ultimately lowering maintenance costs and ensuring efficient, uninterrupted operation.

4.3.3. Data Availability (Real-Time vs. Historical Data)

Another important criterion in selecting TM-CPD methods is whether fault detection needs to be performed in real-time for continuous monitoring, on historical data for offline analysis, or for post-failure investigation. In real-time monitoring, faults must be identified and classified immediately to allow prompt corrective action, which is particularly crucial in wind farms where unexpected failures can cause costly downtime and structural damage. For such applications, TM methods like FFT and HHT are preferred due to their ability to process sensor data quickly. However, TM alone cannot provide time-bound maintenance decisions, as normal operational variations may trigger false alarms. To address this, BOCPD serves as an effective CPD method for real-time monitoring, continuously updating fault probabilities as new data arrives. Additionally, CUSUM is highly efficient for tracking gradual changes, ensuring that slowly developing faults are detected without being overlooked.

On the other hand, historical data analysis plays a crucial role in trend monitoring, performance evaluation, and predictive maintenance. Long-term datasets allow wind farm operators to forecast component failure rates and plan optimized maintenance schedules. For such applications, TM-based techniques like WT and EMD are particularly useful, as they provide high-resolution insight into the evolution of signal components over time. CPD methods such as KL Divergence and JSD are well-suited for historical data analysis because they can quantify changes in probability distributions over extended periods, helping to detect slowly developing faults that may be missed by real-time monitoring based solely on current data.

4.3.4. Sensitivity & Accuracy (Balancing False Alarms and Missed Faults)

A major challenge in wind turbine fault detection is balancing system sensitivity with the need to minimize false alarms. Overly sensitive systems may trigger unnecessary maintenance, increasing costs, while under-sensitive systems risk missing critical faults, potentially leading to unexpected failures. TM-based techniques such as WT and HHT are well-suited for high-sensitivity detection because they can capture subtle variations in vibration signals. However, TM alone cannot distinguish whether an observed anomaly is a genuine fault or merely normal operational noise. To address this, CPD-based methods like KL Divergence and JSD provide robust statistical validation, ensuring that only significant deviations from normal operation are identified as actual faults.

However, certain wind turbine components naturally exhibit variability due to changing wind conditions, temperature fluctuations, and load variations. An over-sensitive fault detection system can generate false alarms, leading to unnecessary turbine shutdowns and costly repairs. To reduce false alarms, TM methods such as Adaptive Filtering and FFT are effective, as they filter out stochastic variations while preserving meaningful signal changes. In CPD, methods like BOCPD and CUSUM help minimize false alarms by continuously updating fault probability scores, ensuring that only sustained deviations trigger alerts.

Table 7. Framework for Selecting TM and CPD Methods Under Different Conditions.

Condition	Best TM Methods	Best CPD Methods	Why This Works?
Sudden Faults (e.g., gearbox misalignment, blade cracks)	WT, HHT	BOCPD, KL Divergence	Detects transient changes; CPD confirms real-time faults.
Gradual Faults (e.g., bearing wear, rotor imbalance)	EMD, AFD	CUSUM, JSD	Tracks long-term degradation; CPD identifies cumulative trends.
Real-Time Monitoring	FFT, HHT	BOCPD, CUSUM	Fast processing; CPD validates immediate and gradual faults.
Historical Fault Analysis	WT, EMD	KL Divergence, JSD	Monitors frequency evolution; CPD quantifies statistical shifts.
High Sensitivity	WT, HHT	KL Divergence, JSD	Captures subtle changes; CPD enhances statistical robustness.
Low False Alarms	AFD, FFT	BOCPD, CUSUM	Reduces noise and unnecessary alerts; CPD filters meaningful trends.
Computational Efficiency	FFT, AFD	BOCPD, CUSUM	Lightweight processing with effective real-time detection.
Harsh Environments	WT, EMD	BOCPD, JSD	Adapts to noise; CPD dynamically updates fault probabilities.
Easy Interpretation	FFT, CUSUM	CUSUM, BOCPD	Simple analysis; CPD adds statistical validation.

summarises the most effective TM-CPD pairings under different operating conditions, providing accurate fault detection, reducing false alarms, and supporting efficient maintenance scheduling.

4.4. Best TM-CPD Combinations in Real-World Wind Turbine Monitoring

For robust and comprehensive wind turbine fault detection, careful selection of TM and CPD methods is essential. Different faults generate unique vibrational and electrical signatures, necessitating tailored analysis techniques.

Table 8. Recommended TM and CPD Methods for Fault-Specific Detection in Wind Turbines.

Fault Type	Best TM Method	Best CPD Method	Why This TM Method?	Why This CPD Method?	Detection Sensitivity	Components	References
Blade Crack Fault	WT	BOCPD	WT captures the frequency spikes.	BOCPD confirms abrupt changes in vibration trends.	High, may generate false positives in noisy conditions.	Blade Crack	[15,25,26,27,151,221,223,249,250,252]
Blade Imbalance Fault	FFT	KLD	FFT isolates imbalance-specific frequency components.	KLD detects statistical shifts in frequency distributions.	Moderate, effective for cyclic imbalance patterns.	Blade Imbalance	[218,219,234,236,250,253]
Blade Erosion Fault	WT	BOCPD	WT captures the irregularities in the frequency.	BOCPD finds the abrupt changes.	High, may generate false positives in noisy conditions.	Blade Erosion	[15,26,27,151,223,252,254]
Blade Twist Fault	FFT	CUSUM	FFT isolates twist-specific frequency components.	CUSUM detects the cumulative deviations.	Moderate to high, in detecting twist pattern.	Blade Twist	[29,66,124,192,255,256]
Gearbox Failure	WT	BOCPD	WT isolates gearbox-specific vibrations while filtering noise.	BOCPD detects abrupt shifts in vibration, confirming wear or misalignment.	High, effective gearbox-specific frequency bands.	Gearbox	[97,107,129,143,247,250,257,258]
Electrical Malfunction	FFT	KLD	FFT identifies abnormal distortions & electrical signals.	KLD tracks shift in power signal distributions.	High, useful for early-stage electrical failures.	Generator	[218,234,250]
Rotor Misalignment	EMD	JSD	EMD separates rotor-specific vibration components.	JSD detects minor statistical deviations in signal behaviour.	Moderate, well-suited for progressive rotor misalignment.	Rotor	[100,115,154,239,259]
Bearing Failure	AFD	CUSUM	AFD isolates bearing frequency bands.	CUSUM tracks cumulative deviations.	High, effective for early bearing wear detection.	Bearing	[29,243,260,261]
Torque Fluctuations	HHT	KL Divergence	HHT identifies fluctuations in torque components.	KLD quantifies statistical differences in power variations.	High, helpful in detecting generator load inconsistency.	Main Shaft	[155,157,218]
Pitch System Failure	FFT	BOCPD	FFT detects irregularities in pitch actuator signals.	BOCPD confirms sudden variations in pitch response time.	High, crucial for maintaining optimal aerodynamic efficiency.	Pitch System	[27,111,221,250,262,263]
Loose Bolts or Structural Instability	WT	CUSUM	WT captures transient, impact-like frequency signals.	CUSUM tracks cumulative shifts in vibrational consistency.	High, crucial for detecting progressive structural looseness.	Loose Bolts	[8,15,29,243,250]
Yaw System Malfunction	EMD	KL Divergence	EMD separates yaw-specific signal variations.	KL D identifies statistical deviations in yaw data.	Moderate, effective for detecting yaw misalignment over time.	Yaw System	[115,154,218,264,265,266]
Tower Foundation Instability	WT	JSD	WT captures long-term shifts in ground vibrations.	JSD detects evolving statistical variations.	High, useful for monitoring structural health.	Tower	[97,241,267,268]

presents an optimized set of TM-CPD pairings designed to effectively detect both abrupt failures and gradual degradations across various wind turbine components.

5. Case Study: Applying Findings on Wind Turbine Blade Failure

Wind turbines are sophisticated machines that operate under fluctuating wind loads, harsh environmental conditions, and constant mechanical stress, making them susceptible to gradual wear, fatigue-related failures, and sudden breakdowns. These failures are typically classified into three categories: mechanical failures (e.g., blade damage, gearbox faults, bearing failures, brake malfunctions, tower fissures), electrical failures (e.g., generator faults, converter malfunctions, power electronics issues, pitch system faults, yaw misalignment), and electrical disturbances such as lightning strikes. Among them, blade failure is particularly critical, as it directly impacts both aerodynamic efficiency and overall power generation. Statistics show that blade failures contribute to nearly 23% of all wind turbine accidents [267], making early detection essential to prevent structural collapse, costly repairs, and extended downtime. Blade replacement is particularly expensive, often accounting for 15–20% of the total turbine cost, with cracks being one of the most prevalent failure types [269].

Vibration-based monitoring is a well-established and widely used approach for condition monitoring of rotating machinery. It is particularly effective for detecting wind turbine blade failures, as vibration responses directly capture structural changes. Given the non-stationary and nonlinear characteristics of vibration signals, advanced frequency-domain techniques such as WT and EMD are frequently applied. For instance, EMD decomposes vibration signals into IMFs, which are highly sensitive to crack initiation and progression, making it a powerful tool for the early detection of blade cracks [265].

To validate and assess the feasibility of the diagnostic approach discussed in this review, two representative datasets were selected from the experimental study by Ahmed et al. [253], entitled “Wind turbine blades fault diagnosis based on vibration dataset analysis.” The experiments were conducted on a laboratory-scale wind turbine (Edibon EEEC unit) equipped with fiber-reinforced polymer (FRP) blades, each 300 mm long with a solid core, designed to closely mimic commercial turbine blades. The aerogenerator, with a diameter of 510 mm, was capable of producing approximately 60 W of power. The system was tested in a wind tunnel operating at velocities between 1.3 and 5.3 m/s, providing realistic aerodynamic conditions.

During healthy operation, the blades were set at a 60° pitch angle, and vibration signals were recorded to establish a baseline for comparison with fault conditions. Data were collected using a PCB Piezotronics 352C65 uniaxial accelerometer mounted on the nacelle near the hub, connected to an NI USB 4431 DAQ system. The system operated at a 1000 Hz sampling rate, with a minimum of 500 samples acquired for each condition. For the case study, two fault scenarios were examined: a mass imbalance fault at a wind speed of 1.3 m/s and a blade crack fault at 4.5 m/s.

(a)

Mass imbalance fault (at 1.3 m/s wind speed):

• Only one imbalance defect was applied, and data were collected under 1.3 m/s wind speed conditions.
• The defect alters the inertial distribution, producing periodic vibrations characteristic of rotor asymmetry.
• Simulated by attaching a 5 g mass at 18 cm from the blade root of one blade.

(b)

Blade crack fault (at 4.5 m/s wind speed):

• Simulated by introducing a crack on the blade body, representing foreign object damage during operation.
• While the dataset does not provide explicit numerical dimensions (depth or length), the crack fault reflects a realistic degradation mode that weakens stiffness and alters vibration responses.
• One crack defect was applied, and data were collected under 4.5 m/s wind speed conditions.

5.1. FFT-Based Detection of Blade Imbalance Fault at (1.3 m/s)

In this case, the results are shown in Figure 10a,b and are discussed below.

• In Figure 10a, the dominant frequency peaks primarily appear at 0 Hz, 50 Hz, and 150 Hz, indicating that the wind turbine is operating normally without disturbances. These frequencies correspond to the fundamental vibration frequencies of the turbine, confirming smooth and defect-free operation. As shown in the figure, the frequency plot exhibits a relatively simple and stable distribution, with most of the energy concentrated near the central vibration frequency.
• Blade imbalance disrupts the smooth operation of the turbine, generating vibrations at higher frequencies. In the FFT spectrum of the imbalance fault, frequency peaks appear at 0 Hz, 50 Hz, 100 Hz, 150 Hz, 250 Hz, 350 Hz, and 450 Hz. These additional peaks at higher frequencies represent elevated harmonic frequencies, reflecting the resonant effects caused by the imbalance.
• The FFT plot for the imbalance fault appears more complex, with an increased number of distinct peaks, indicating that the fault has introduced irregularities into the vibration patterns. Such complexity typically reflects stronger resonant frequencies or harmonics, caused by the uneven mass distribution on the blade.

5.2. KL Divergence-Based Detection of Blade Imbalance Fault at 1.3 m/s

• KL Divergence measures how much one probability distribution differs from another. In this case, it quantifies the difference between the frequency distributions of the healthy condition and the imbalance fault condition.
• The KL Divergence between the frequency distributions of the healthy blade and the imbalance fault blade at a blade speed of 1.3 m/s is 0.3942.
• A KLD value of 0.3942 indicates a significant deviation in the vibration signal’s frequency content between the healthy and faulty states. As the KLD increases, the distributions become more distinct.
• This KLD value confirms that the imbalance fault introduces measurable changes in the vibration pattern, demonstrating that the fault can be reliably detected using FFT combined with KL Divergence.

5.3. WT-Based Detection of Blade Crack Fault at 4.5 m/s

Figure 11b presents the frequency content of the vibration signal for a healthy wind turbine blade at a wind speed of 4.5 m/s. In the wavelet transform plot, colors represent the signal magnitude, with warmer colors indicating higher values. The plot illustrates the evolution of the vibration signal over both time and frequency. For the healthy blade, the frequency content remains stable, with no sharp peaks or sudden jumps, indicating smooth and steady turbine operation. This pattern reflects the inherent vibrational characteristics of a healthy blade.

Figure 11b shows the frequency content of the vibration signal for a wind turbine blade with a crack at a wind speed of 4.5 m/s. Compared to the healthy blade, the cracked blade exhibits distinct features, including varied frequencies, increased magnitudes, and noticeable changes in the vibration pattern. This indicates that the crack alters the vibration profile of the turbine, introducing higher resonant frequencies. The WT effectively captures these changes, making it a powerful tool for detecting such faults.

5.4. BOCPD-Based Detection of Blade Crack Fault at (4.5 m/s)

The results are presented in Figure 12. The red arrows indicate detected change points—moments where the system’s behavior, as reflected in the vibration data, undergoes significant alteration. In Figure 12a, the result for the healthy blade appears stable, with no notable shifts at the detected change points. This is expected, as the blade is in good condition and the system operates smoothly. The absence of abrupt changes confirms that the turbine is functioning predictably, without any malfunctions.

In Figure 12b, which depicts the cracked blade condition, the result shows noticeably larger fluctuations compared to the healthy blade. This indicates that the crack significantly alters the vibration pattern, causing substantial shifts in the data. The detected change points highlight the moments when the crack begins to have a pronounced effect on the vibration signal, making them key indicators of fault development.

6. Conclusions

This review has examined the integration of TM and CPD as a promising strategy for enhancing fault diagnosis in wind turbines. Individually, TM is effective in identifying the type of fault through long-term trend analysis, while CPD excels at precisely determining the time of fault onset. When combined, the complementary strengths of both methods yield more reliable and timely diagnostics.

Our analysis of widely used TM techniques (e.g., FFT, Wavelet Transform, Hilbert–Huang Transform, Empirical Mode Decomposition) and CPD methods (e.g., Bayesian Online Change Point Detection, Kullback–Leibler Divergence, Cumulative Sum) confirms that their synthesis improves diagnostic accuracy. The case study on blade imbalance and crack detection further demonstrated that TM–CPD integration enhances early detection: FFT coupled with KLD was effective for imbalance detection, while WT combined with BOCPD reliably identified blade crack-related anomalies. These findings underscore the value of hybrid monitoring approaches for mitigating downtime and extending turbine lifespan.

However, several challenges remain. Current TM–CPD frameworks face computational burdens, limiting their adaptability in real-time applications. Moreover, most existing methods are primarily diagnostic rather than predictive, offering limited support for predictive maintenance strategies. The integration of TM and CPD also requires optimization to reduce false alarms and improve robustness under varying operational and environmental conditions.

Future research should focus on developing lightweight, real-time TM–CPD algorithms, integrating them with advanced machine learning and digital twin technologies. Additionally, efforts should be directed toward predictive maintenance frameworks that leverage TM–CPD synergy to not only detect but also forecast faults, ultimately supporting more resilient and cost-effective wind turbine operations.

7. Future Research Directions for TM-CPD in Wind Turbine Monitoring

As wind energy technology advances, the integration of TM and CPD must also evolve to meet the growing demands of accuracy, scalability, and real-time deployment. While this review and case study highlight the potential of TM–CPD synthesis for fault detection, several promising directions for future research can be identified:

• Exploration of Novel TM–CPD Combinations: Beyond the combinations demonstrated in this review (e.g., FFT–KLD for imbalance detection, WT–BOCPD for crack detection), other TM–CPD pairings merit systematic evaluation. Comparative studies across diverse fault modes and operating conditions could establish best-practice frameworks for different turbine subsystems (e.g., gearbox, generator, blades).
• AI-Enhanced Hybrid Algorithms: Future work should embed TM–CPD approaches within advanced machine learning (ML) and deep learning architectures. Hybrid models could combine feature extraction from TM with adaptive thresholds from CPD, potentially incorporating reinforcement learning to continuously improve fault detection and reduce false alarms.
• Real-Time and Edge Computing Solutions: Current TM–CPD implementations often face computational bottlenecks. Leveraging edge computing and lightweight algorithm design would enable real-time monitoring directly on turbine controllers, reducing latency and supporting immediate fault response.
• Integration with Digital Twins: Digital twin models of wind turbines can serve as virtual testbeds for validating TM–CPD methods under controlled conditions. Combining physical models with real-time SCADA data through digital twins would allow TM–CPD systems to adapt dynamically to evolving operating states and predict degradation trends.
• Towards Predictive Maintenance: While current TM–CPD methods are largely diagnostic, future research should extend their application toward predictive frameworks. Integrating time-to-failure estimation, probabilistic forecasting, and remaining useful life (RUL) modelling with TM–CPD could shift monitoring from reactive to proactive strategies, enhancing turbine resilience and reducing maintenance costs.

In summary, advancing TM–CPD research will require combining novel algorithmic development, computational efficiency, and integration with digital technologies such as AI and digital twins. These directions hold the potential to transform TM–CPD from a primarily diagnostic tool into a predictive and self-adaptive framework, aligning with the long-term vision of autonomous, cost-effective, and sustainable wind turbine health monitoring.