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Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 9735

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Special Issue Editors

Prof. Dr. Yongbo Li

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Guest Editor

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: information fusion; digital twin technology; structural health monitoring; fault diagnosis and prognosis; system reliability analysis; dynamic modeling of mechanical systems
Special Issues, Collections and Topics in MDPI journals

Dr. Teng Wang

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Guest Editor

ROSEN, 106460 Doyle Ave, Kelowna, BC V1Y 0C2, Canada
Interests: prognostic and health management; stochastic system; probability theory; digital twin

Dr. Khandaker Noman

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Guest Editor

School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710072, China
Interests: predictive maintenance; digital twin; signal processing; machine learning; system reliability analysis; remaining useful life prediction; time–frequency analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bing Li

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Guest Editor

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: metamaterials; metasurface; elastic wave; vibration attenuation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments and the reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by extensive applications ranging from the aerospace, automotive, transport, manufacturing, and processing industries to defense and the infrastructure industry. In view of the current state of the art and advances in this fast-growing discipline, in this Special Issue we are calling for papers related to all aspects of fault diagnostics, damage identification, and prognostics-based health management. A wide range of topics are covered, including new theories, methodologies, optimization, and applications in sensing, measurement, modeling, control, and prognostics. Topics include, but are not limited to:

Measuring techniques for condition monitoring;
Reliability analysis and design;
Signal processing of measured data;
Feature extraction of measured data;
Fault diagnosis for prognosis and health management (PHM);
Degradation modeling of measured data;
Measurement error analysis;
RUL prediction methods based on intelligent algorithms;
Maintenance strategy optimization;
Structural health monitoring (SHM);
Non-destructive testing (NDT).

Prof. Dr. Yongbo Li
Dr. Teng Wang
Dr. Khandaker Noman
Prof. Dr. Bing Li
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

prognostic and health management
structural health monitoring
condition-based maintenance
non-destructive testing and evaluation
reliability engineering

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Related Special Issue

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems in Sensors (12 articles)

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12 pages, 2016 KiB

Open AccessArticle

Machine Health Indicators and Digital Twins

by Tal Bublil, Roee Cohen, Ron S. Kenett and Jacob Bortman

Sensors 2025, 25(7), 2246; https://doi.org/10.3390/s25072246 - 2 Apr 2025

Viewed by 584

Abstract

Health indicators (HIs) are quantitative indices that assess the condition of engineering systems by linking sensor data with monitoring, diagnostic, and prognostic methods to estimate the remaining useful life (RUL). Digital twins (DTs), which serve as digital representations of physical assets, enhance system monitoring, diagnostics, and prognostics by operationalizing analytic capabilities derived from sensor data. This paper explores the integration of HIs and DTs, illustrating their roles in condition-based maintenance and structural health monitoring. The methodologies discussed span data-driven and physics-based approaches, emphasizing their applications in rotary machinery, including bearings and gears. These approaches not only detect anomalies but also predict system failures through advanced modeling and machine learning (ML) techniques. The paper provides examples of HIs derived from vibration analysis and soft sensors and maps future research directions for improving health monitoring systems through hybrid modeling and uncertainty quantification. It concludes by addressing the challenges of data labeling and uncertainties and the role of HIs in advancing performance engineering, making DTs a pivotal tool in predictive maintenance strategies. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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23 pages, 5463 KiB

Open AccessArticle

A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive Normalization Using Long Short-Term Memory Under Multi-Operating Conditions

by Jiantao Lu, Kuangzhi Yang, Peng Zhang, Wei Wu and Shunming Li

Sensors 2025, 25(7), 2066; https://doi.org/10.3390/s25072066 - 26 Mar 2025

Viewed by 154

Abstract

Trend forecasting and early anomaly warnings are important for avoiding aircraft engine failures or accidents. This study proposes a trend forecasting method based on enhanced Slice-level Adaptive Normalization (SAN) using a Long Short-Term Memory (LSTM) neural network under multi-operating conditions. Firstly, a condition recognition technology is constructed to automatically identify the operating conditions based on the predetermined judgment conditions, and vibration signal features are adaptively divided into three typical operating conditions, namely, the idling operating condition, the starting operating condition and the utmost operating condition. The features of original signals are extracted to reduce the impacts of signal fluctuations and noise preliminarily. Secondly, enhanced SAN is used to normalize and denormalize the features to alleviate non-stationary factors. To improve prediction accuracy, an L₁ filter is adopted to extract the trend term of the features, which can effectively reduce the overfitting of SAN to local information. Moreover, the slice length is quantitatively estimated by the fixed points in L₁ filtering, and a tail amendment technology is added to expand the applicable range of enhanced SAN. Finally, an LSTM-based forecasting model is constructed to forecast the normalized data from enhanced SAN, serving as input during denormalization. The final results under different operating conditions are the output from denormalization. The validity of the proposed method is verified using the test data of an aircraft engine. The results show that the proposed method can achieve higher forecasting accuracy compared to other methods. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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20 pages, 9719 KiB

Open AccessArticle

Real-Time Evaluation of Ground Insulation Degradation and Fault Warning Method Under Multiple Operating Conditions for Train Traction Drive System

by Zhenglin Cheng, Kan Liu, Xueming Li, Shaolong Xu, Zhiwen Chen and Fengbing Jiang

Sensors 2025, 25(5), 1296; https://doi.org/10.3390/s25051296 - 20 Feb 2025

Viewed by 394

Abstract

Aiming at the problem that the main circuit grounding fault in the traction drive system of locomotives and high-speed trains can only be diagnosed under a single operating condition and cannot be warned about early, a mechanism and data-driven real-time evaluation and full operating condition fault warning method for ground insulation degradation is proposed. Firstly, based on the mechanism of grounding faults, the circuit characteristics of the main circuit of the traction transmission system under different grounding fault conditions are analyzed, and mathematical models are established for the detection of various grounding faults and sensor signals under different operating conditions, as well as for evaluating the degree of degradation of grounding faults. Secondly, based on engineering application experience, a feature index set that can accurately classify different types of grounding faults is extracted. Combined with on-site fault case data, a decision tree method is used to establish a classification model between the feature index set and typical grounding fault sources under different operating conditions, which is then converted into a fault diagnosis rule library. Finally, real-time collection of relevant sensor signals, based on the fault diagnosis rule library and the degradation degree evaluation model of grounding faults, enables real-time detection and warning of grounding faults under all operating conditions to ensure train safety and provide key information support for optimal degraded operation in the future. The test result based on controller hardware in the loop shows that the method proposed in this paper can achieve accurate detection and localization of grounding faults under different operating conditions and can provide real-time warning of the severity of grounding faults, which has good engineering application value. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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25 pages, 5731 KiB

Open AccessArticle

A Cross-Machine Intelligent Fault Diagnosis Method with Small and Imbalanced Data Based on the ResFCN Deep Transfer Learning Model

by Juanru Zhao, Mei Yuan, Yiwen Cui and Jin Cui

Sensors 2025, 25(4), 1189; https://doi.org/10.3390/s25041189 - 15 Feb 2025

Viewed by 559

Abstract

Intelligent fault diagnosis (IFD) for mechanical equipment based on small and imbalanced datasets has been widely studied in recent years, with transfer learning emerging as one of the most promising approaches. Existing transfer learning-based IFD methods typically use data from different operating conditions of the same equipment as the source and target domains for the transfer learning process. However, in practice, it is often challenging to find identical equipment to obtain source domain data when diagnosing faults in the target equipment. These strict assumptions pose significant limitations on the application of IFD techniques in real-world industrial settings. Furthermore, the temporal characteristics of time-series monitoring data are often inadequately considered in existing methods. In this paper, we propose a cross-machine IFD method based on a residual full convolutional neural network (ResFCN) transfer learning model, which leverages the time-series features of monitoring data. By incorporating sliding window (SW)-based data segmentation, network pretraining, and model fine-tuning, the proposed method effectively exploits fault-associated general features in the source domain and learns domain-specific patterns that better align with the target domain, ultimately achieving accurate fault diagnosis for the target equipment. We design and implement three sets of experiments using two widely used public datasets. The results demonstrate that the proposed method outperforms existing approaches in terms of fault diagnosis accuracy and robustness. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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24 pages, 2050 KiB

Open AccessArticle

An Autoregressive-Based Motor Current Signature Analysis Approach for Fault Diagnosis of Electric Motor-Driven Mechanisms

by Roberto Diversi, Alice Lenzi, Nicolò Speciale and Matteo Barbieri

Sensors 2025, 25(4), 1130; https://doi.org/10.3390/s25041130 - 13 Feb 2025

Viewed by 604

Abstract

Maintenance strategies such as condition-based maintenance and predictive maintenance of machines have gained importance in industrial automation firms as key concepts in Industry 4.0. As a result, online condition monitoring of electromechanical systems has become a crucial task in many industrial applications. Motor current signature analysis (MCSA) is an interesting noninvasive alternative to vibration analysis for the condition monitoring and fault diagnosis of mechanical systems driven by electric motors. The MCSA approach is based on the premise that faults in the mechanical load driven by the motor manifest as changes in the motor’s current behavior. This paper presents a novel data-driven, MCSA-based CM approach that exploits autoregressive (AR) spectral estimation. A multiresolution analysis of the raw motor currents is first performed using the discrete wavelet transform with Daubechies filters, enabling the separation of noise, disturbances, and variable torque effects from the current signals. AR spectral estimation is then applied to selected wavelet details to extract relevant features for fault diagnosis. In particular, a reference AR power spectral density (PSD) is estimated using data collected under healthy conditions. The AR PSD is then continuously or periodically updated with new data frames and compared to the reference PSD through the Symmetric Itakura–Saito spectral distance (SISSD). The SISSD, which serves as the health indicator, has proven capable of detecting fault occurrences through changes in the AR spectrum. The proposed procedure is tested on real data from two different scenarios: (i) an experimental in-house setup where data are collected during the execution of electric cam motion tasks (imbalance faults are emulated); (ii) the Korea Advanced Institute of Science and Technology testbed, whose data set is publicly available (bearing faults are considered). The results demonstrate the effectiveness of the method in both fault detection and isolation. In particular, the proposed health indicator exhibits strong detection capabilities, as its values under fault conditions exceed those under healthy conditions by one order of magnitude. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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15 pages, 5709 KiB

Open AccessArticle

Compound Fault Diagnosis of Wind Turbine Gearbox via Modified Signal Quality Coefficient and Versatile Residual Shrinkage Network

by Weixiong Jiang, Guanhui Zhao, Zhan Gao, Yuanhang Wang and Jun Wu

Sensors 2025, 25(3), 913; https://doi.org/10.3390/s25030913 - 3 Feb 2025

Viewed by 644

Abstract

Wind turbine gearbox fault diagnosis is critical to guarantee working efficiency and operational safety. However, the current diagnostic methods face enormous restrictions in handling nonlinear noise signals and intricate compound fault patterns. Herein, a compound fault diagnosis method based on modified signal quality coefficient (MSQC) and versatile residual shrinkage network (VRSN) is proposed to resolve these issues. In detail, the MSQC is designed to remove the noise components irrelevant to wind turbine operation status, and it has the ability to balance the denoised effect and signal fidelity. The VRSN is constructed for compound fault diagnosis, and it consists of two heterogeneous residual shrinkage networks. The former is designed to count the number of faults, and the latter is adopted to identify the single or compound fault pattern. Finally, a self-built wind turbine gearbox compound fault test rig is adopted to verify the proposed method’s effectiveness. The results demonstrate that the proposed method is competitive in terms of compound fault diagnosis accuracy. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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16 pages, 5063 KiB

Open AccessArticle

Imbalance Fault Detection of Marine Current Turbine Based on GLRT Detector

by Milu Zhang, Jutao Chen, Liu Yang and Christophe Claramunt

Sensors 2025, 25(3), 874; https://doi.org/10.3390/s25030874 - 31 Jan 2025

Viewed by 562

Abstract

Marine Current Turbines (MCTs) play a critical role in converting the kinetic energy of water into electricity. However, due to the influence of marine organisms, marine current equipment often experiences imbalance faults. Additionally, affected by the underwater environment, the fault characteristics are submerged in disturbances such as waves and turbulence. Against the background of the above problems, this article proposes a fault detection strategy based on a Generalized Likelihood Ratio Test (GLRT) detector. Firstly, a simulation model of the MCT system is established to obtain prior knowledge. Then, combining the Matrix Pencil Method (MPM) for calculating instantaneous frequency, imbalance fault metrics are selected based on the proposed GLRT detector. At the end, the marine current turbine experimental platform is established, which can simulate imbalanced faults and environmental disturbances, helping to verify the effectiveness of the proposed strategy. The experimental results indicate that the proposed strategy can detect imbalanced faults in complex underwater environments. Imbalance faults are the main manifestation of blade attachments. Thus, it is very meaningful to accomplish fault detection in order to maintain the working order of the MCT system. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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25 pages, 7034 KiB

Open AccessArticle

Diagnosis of Reverse-Connection Defects in High-Voltage Cable Cross-Bonded Grounding System Based on ARO-SVM

by Yuhao Ai, Bin Song, Shaocheng Wu, Yongwen Li, Li Lu and Linong Wang

Sensors 2025, 25(2), 590; https://doi.org/10.3390/s25020590 - 20 Jan 2025

Viewed by 797

Abstract

High-voltage (HV) cables are increasingly used in urban power grids, and their safe operation is critical to grid stability. Previous studies have analyzed various defects, including the open circuit in the sheath loop, the flooding in the cross-bonded link box, and the sheath grounding fault. However, there is a paucity of research on the defect of the reverse direction between the inner core and the outer shield of the coaxial cable. Firstly, this paper performed a theoretical analysis of the sheath current in the reversed-connection state and established a simulation model for verification. The outcomes of the simulation demonstrate that there are significant variations in the amplitudes of the sheath current under different reversed-connection conditions. Consequently, a feature vector was devised based on the amplitude of the sheath current. The support vector machine (SVM) was then applied to diagnose the reversed-connection defects in the HV cable cross-bonded grounding system. The artificial rabbits optimization (ARO) algorithm was adopted to optimize the SVM model, attaining an impressively high diagnostic accuracy rate of 99.35%. The effectiveness and feasibility of the proposed algorithm are confirmed through the analysis and validation of the practical example. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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21 pages, 5645 KiB

Open AccessArticle

Study on Few-Shot Fault Diagnosis Method for Marine Fuel Systems Based on DT-SViT-KNN

by Shankai Li, Liang Qi, Jiayu Shi, Han Xiao, Bin Da, Runkang Tang and Danfeng Zuo

Sensors 2025, 25(1), 6; https://doi.org/10.3390/s25010006 - 24 Dec 2024

Cited by 1 | Viewed by 677

Abstract

The fuel system serves as the core component of marine diesel engines, and timely and effective fault diagnosis is the prerequisite for the safe navigation of ships. To address the challenge of current data-driven fault-diagnosis-based methods, which have difficulty in feature extraction and low accuracy under small samples, this paper proposes a fault diagnosis method based on digital twin (DT), Siamese Vision Transformer (SViT), and K-Nearest Neighbor (KNN). Firstly, a diesel engine DT model is constructed by integrating the mathematical, mechanism, and three-dimensional physical models of the Medium-speed diesel engines of 6L21/31 Marine, completing the mapping from physical entity to virtual entity. Fault simulation calculations are performed using the DT model to obtain different types of fault data. Then, a feature extraction network combining Siamese networks with Vision Transformer (ViT) is proposed for the simulated samples. An improved KNN classifier based on the attention mechanism is added to the network to enhance the classification efficiency of the model. Meanwhile, a Weighted-Similarity loss function is designed using similarity labels and penalty coefficients, enhancing the model’s ability to discriminate between similar sample pairs. Finally, the proposed method is validated using a simulation dataset. Experimental results indicate that the proposed method achieves average accuracies of 97.22%, 98.21%, and 99.13% for training sets with 10, 20, and 30 samples per class, respectively, which can accurately classify the fault of marine fuel systems under small samples and has promising potential for applications. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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25 pages, 4001 KiB

Open AccessArticle

CASSAD: Chroma-Augmented Semi-Supervised Anomaly Detection for Conveyor Belt Idlers

by Fahad Alharbi, Suhuai Luo, Abdullah Alsaedi, Sipei Zhao and Guang Yang

Sensors 2024, 24(23), 7569; https://doi.org/10.3390/s24237569 - 27 Nov 2024

Cited by 2 | Viewed by 944

Abstract

Idlers are essential to conveyor systems, as well as supporting and guiding belts to ensure production efficiency. Proper idler maintenance prevents failures, reduces downtime, cuts costs, and improves reliability. Most studies on idler fault detection rely on supervised methods, which depend on large labelled datasets for training. However, acquiring such labelled data is often challenging in industrial environments due to the rarity of faults and the labour-intensive nature of the labelling process. To address this, we propose the chroma-augmented semi-supervised anomaly detection (CASSAD) method, designed to perform effectively with limited labelled data. At the core of CASSAD is the one-class SVM (OC-SVM), a model specifically developed for anomaly detection in cases where labelled anomalies are scarce. We also compare CASSAD’s performance with other common models like the local outlier factor (LOF) and isolation forest (iForest), evaluating each with the area under the curve (AUC) to assess their ability to distinguish between normal and anomalous data. CASSAD introduces chroma features, such as chroma energy normalised statistics (CENS), the constant-Q transform (CQT), and the chroma short-time Fourier transform (STFT), enhanced through filtering to capture rich harmonic information from idler sounds. To reduce feature complexity, we utilize the mean and standard deviation (std) across chroma features. The dataset is further augmented using additive white Gaussian noise (AWGN). Testing on an industrial dataset of idler sounds, CASSAD achieved an AUC of 96% and an accuracy of 91%, surpassing a baseline autoencoder and other traditional models. These results demonstrate the model’s robustness in detecting anomalies with minimal dependence on labelled data, offering a practical solution for industries with limited labelled datasets. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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19 pages, 5357 KiB

Open AccessArticle

Planetary Gearboxes Fault Diagnosis Based on Markov Transition Fields and SE-ResNet

by Yanyan Liu, Tongxin Gao, Wenxu Wu and Yongquan Sun

Sensors 2024, 24(23), 7540; https://doi.org/10.3390/s24237540 - 26 Nov 2024

Cited by 3 | Viewed by 759

Abstract

The working conditions of planetary gearboxes are complex, and their structural couplings are strong, leading to low reliability. Traditional deep neural networks often struggle with feature learning in noisy environments, and their reliance on one-dimensional signals as input fails to capture the interrelationships between data points. To address these challenges, we proposed a fault diagnosis method for planetary gearboxes that integrates Markov transition fields (MTFs) and a residual attention mechanism. The MTF was employed to encode one-dimensional signals into feature maps, which were then fed into a residual networks (ResNet) architecture. To enhance the network’s ability to focus on important features, we embedded the squeeze-and-excitation (SE) channel attention mechanism into the ResNet34 network, creating a SE-ResNet model. This model was trained to effectively extract and classify features. The developed method was validated using a specific dataset and achieved an accuracy of about 98.1%. The results demonstrate the effectiveness and reliability of the developed method in diagnosing faults in planetary gearboxes under strong noise conditions. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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Review

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34 pages, 409 KiB

Open AccessReview

Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities

by Denis Leite, Emmanuel Andrade, Diego Rativa and Alexandre M. A. Maciel

Sensors 2025, 25(1), 60; https://doi.org/10.3390/s25010060 - 25 Dec 2024

Cited by 5 | Viewed by 2537

Abstract

Integrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). This paper conducts a comprehensive literature review of ML-based RT-FDD. Out of 805 documents, 29 studies were identified as noteworthy for presenting innovative methods that address the complexities and challenges associated with fault detection. While ML-based RT-FDD offers different benefits, including fault prediction accuracy, it faces challenges in data quality, model interpretability, and integration complexities. This review identifies a gap in industrial implementation outcomes that opens new research opportunities. Future Fault Detection and Diagnosis (FDD) research may prioritize standardized datasets to ensure reproducibility and facilitate comparative evaluations. Furthermore, there is a pressing need to refine techniques for handling unbalanced datasets and improving feature extraction for temporal series data. Implementing Explainable Artificial Intelligence (AI) (XAI) tailored to industrial fault detection is imperative for enhancing interpretability and trustworthiness. Subsequent studies must emphasize comprehensive comparative evaluations, reducing reliance on specialized expertise, documenting real-world outcomes, addressing data challenges, and bolstering real-time capabilities and integration. By addressing these avenues, the field can propel the advancement of ML-based RT-FDD methodologies, ensuring their effectiveness and relevance in industrial contexts. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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