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26 pages, 5624 KiB

Open AccessArticle

Anomaly Detection Method for Hydropower Units Based on KSQDC-ADEAD Under Complex Operating Conditions

by Tongqiang Yi, Xiaowu Zhao, Yongjie Shi, Xiangnan Jing, Wenyang Lei, Jiang Guo, Yang Meng and Zhengyu Zhang

Sensors 2025, 25(13), 4093; https://doi.org/10.3390/s25134093 (registering DOI) - 30 Jun 2025

Abstract

The safe and stable operation of hydropower units, as core equipment in clean energy systems, is crucial for power system security. However, anomaly detection under complex operating conditions remains a technical challenge in this field. This paper proposes a hydropower unit anomaly detection [...] Read more.

The safe and stable operation of hydropower units, as core equipment in clean energy systems, is crucial for power system security. However, anomaly detection under complex operating conditions remains a technical challenge in this field. This paper proposes a hydropower unit anomaly detection method based on K-means seeded quadratic discriminant clustering and an adaptive density-aware ensemble anomaly detection algorithm (KSQDC-ADEAD). The method first employs the KSQDC algorithm to identify different operating conditions of hydropower units. By combining K-means clustering’s initial partitioning capability with quadratic discriminant analysis’s nonlinear decision boundary construction ability, it achieves the high-precision identification of complex nonlinear condition boundaries. Then, an ADEAD algorithm is designed, which incorporates local density information and improves anomaly detection accuracy and stability through multi-model ensemble and density-adaptive strategies. Validation experiments using 14-month actual operational data from a 550 MW unit at a hydropower station in Southwest China show that the KSQDC algorithm achieves a silhouette coefficient of 0.64 in condition recognition, significantly outperforming traditional methods, and the KSQDC-ADEAD algorithm achieves comprehensive scores of 0.30, 0.34, and 0.23 for anomaly detection at three key monitoring points, effectively improving the accuracy and reliability of anomaly detection. This method provides a systematic technical solution for hydropower unit condition monitoring and predictive maintenance. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

18 pages, 67336 KiB

Open AccessArticle

An Interpretability Method for Broken Wire Detection

by Hailong Wu, Shaoqing Liu, Zhanghou Xu, Zhenshan Ji, Mengpeng Qian, Xiaolin Yuan and Yong Wang

Sensors 2025, 25(13), 4002; https://doi.org/10.3390/s25134002 (registering DOI) - 27 Jun 2025

Viewed by 177

Abstract

As an indispensable piece of production equipment in the industrial field, wire rope is directly related to personnel safety and the normal operation of equipment. Therefore, it is necessary to perform broken wire detection. Deep learning has powerful feature-learning capabilities and is characterized [...] Read more.

As an indispensable piece of production equipment in the industrial field, wire rope is directly related to personnel safety and the normal operation of equipment. Therefore, it is necessary to perform broken wire detection. Deep learning has powerful feature-learning capabilities and is characterized by high accuracy and efficiency, and the YOLOv8 object detection model has been adopted to detect wire breaks in electromagnetic signal images of wire rope, achieving better results. Nevertheless, the black box problem of the model brings a new trust challenge, and it is difficult to determine the correctness of the model’s decision and whether it has any potential problems, so an interpretability study needed to be carried out. In this work, a perturbation-based interpretability method—ESTC (Eliminating Splicing and Truncation Compensation)—is proposed, which distinguishes itself from other methods of the same type by targeting the signaling object instead of the ordinary object. ESTC is compared with other model-agnostic interpretable methods, LIME, RISE, and D-RISE, using the same model on the same test set. The results indicate that our proposed method is objectively superior to the others, and the interpretability analysis shows that the model predicts in a way that is consistent with the priori knowledge of the manual rope inspection. This not only increases the credibility of using the object detection model for broken wire detection but also has important implications for the practical application of using object detection model to detect wire breaks. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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28 pages, 10233 KiB

Open AccessArticle

Time-Series Forecasting Method Based on Hierarchical Spatio-Temporal Attention Mechanism

by Zhiguo Xiao, Junli Liu, Xinyao Cao, Ke Wang, Dongni Li and Qian Liu

Sensors 2025, 25(13), 4001; https://doi.org/10.3390/s25134001 - 26 Jun 2025

Viewed by 173

Abstract

In the field of intelligent decision-making, time-series data collected by sensors serves as the core carrier for interaction between the physical and digital worlds. Accurate analysis is the cornerstone of decision-making in critical scenarios, such as industrial monitoring and intelligent transportation. However, the [...] Read more.

In the field of intelligent decision-making, time-series data collected by sensors serves as the core carrier for interaction between the physical and digital worlds. Accurate analysis is the cornerstone of decision-making in critical scenarios, such as industrial monitoring and intelligent transportation. However, the inherent spatio-temporal coupling characteristics and cross-period long-range dependency of sensor data cause traditional time-series prediction methods to face performance bottlenecks in feature decoupling and multi-scale modeling. This study innovatively proposes a Spatio-Temporal Attention-Enhanced Network (TSEBG). Breaking through traditional structural designs, the model employs a Squeeze-and-Excitation Network (SENet) to reconstruct the convolutional layers of the Temporal Convolutional Network (TCN), strengthening the feature expression of key time steps through dynamic channel weight allocation to address the redundancy issue of traditional causal convolutions in local pattern capture. A Bidirectional Gated Recurrent Unit (BiGRU) variant based on a global attention mechanism is designed, leveraging the collaboration between gating units and attention weights to mine cross-period long-distance dependencies and effectively alleviate the gradient disappearance problem of Recurrent Neural Network (RNN-like) models in multi-scale time-series analysis. A hierarchical feature fusion architecture is constructed to achieve multi-dimensional alignment of local spatial and global temporal features. Through residual connections and the dynamic adjustment of attention weights, hierarchical semantic representations are output. Experiments show that TSEBG outperforms current dominant models in time-series single-step prediction tasks in terms of accuracy and performance, with a cross-dataset R² standard deviation of only 3.7%, demonstrating excellent generalization stability. It provides a novel theoretical framework for feature decoupling and multi-scale modeling of complex time-series data. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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20 pages, 4280 KiB

Open AccessArticle

A Spatio-Temporal Joint Diagnosis Framework for Bearing Faults via Graph Convolution and Attention-Enhanced Bidirectional Gated Networks

by Zhiguo Xiao, Xinyao Cao, Huihui Hao, Siwen Liang, Junli Liu and Dongni Li

Sensors 2025, 25(13), 3908; https://doi.org/10.3390/s25133908 - 23 Jun 2025

Viewed by 149

Abstract

In recent years, Academia and industry have conducted extensive and in-depth research on bearing-fault-diagnosis technology. However, the current modeling of time–space coupling characteristics in rolling bearing fault diagnosis remains inadequate, and the integration of multi-modal correlations requires further improvement. To address these challenges, [...] Read more.

In recent years, Academia and industry have conducted extensive and in-depth research on bearing-fault-diagnosis technology. However, the current modeling of time–space coupling characteristics in rolling bearing fault diagnosis remains inadequate, and the integration of multi-modal correlations requires further improvement. To address these challenges, this paper proposes a joint diagnosis framework integrating graph convolutional networks (GCNs) with attention-enhanced bidirectional gated recurrent units (BiGRUs). The proposed framework first constructs an improved K-nearest neighbor-based spatio-temporal graph to enhance multidimensional spatial–temporal feature modeling through GCN-based spatial feature extraction. Subsequently, we design an end-to-end spatio-temporal joint learning architecture by implementing a global attention-enhanced BiGRU temporal modeling module. This architecture achieves the deep fusion of spatio-temporal features through the graph-structural transformation of vibration signals and a feature cascading strategy, thereby improving overall model performance. The experiment demonstrated a classification accuracy of 97.08% on three public datasets including CWRU, verifying that this method decouples bearing signals through dynamic spatial topological modeling, effectively combines multi-scale spatiotemporal features for representation, and accurately captures the impact characteristics of bearing faults. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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32 pages, 3638 KiB

Open AccessArticle

Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing

by Suchuan Xing, Yihan Wang and Wenhe Liu

Sensors 2025, 25(11), 3396; https://doi.org/10.3390/s25113396 - 28 May 2025

Viewed by 380

Abstract

Modern data centers face increasing complexity with distributed microservice architectures, making anomaly detection and fault localization challenging yet critical. Traditional monitoring sensor tools struggle with heterogeneous metrics, temporal correlations, and precise root cause analysis in these environments. This paper proposes a dual-channel deep [...] Read more.

Modern data centers face increasing complexity with distributed microservice architectures, making anomaly detection and fault localization challenging yet critical. Traditional monitoring sensor tools struggle with heterogeneous metrics, temporal correlations, and precise root cause analysis in these environments. This paper proposes a dual-channel deep learning framework that integrates Temporal Convolutional Networks with Variational Autoencoders to address these challenges. Our approach employs contrastive learning to create unified representations of diverse service metrics and incorporates causal inference mechanisms to trace fault propagation paths. We evaluated our framework using a semi-supervised learning approach that leveraged both labeled anomalies and abundant normal data, achieving 95.4% detection accuracy, 93.8% F1-score, and 87.6% precision in fault component localization. The system reduced the average troubleshooting time by 43% and false localization rates by 31% compared to state-of-the-art methods, while maintaining a computational efficiency suitable for real-time monitoring. These results demonstrate the effectiveness of our approach in identifying and precisely localizing anomalies in complex microservice environments through intelligent sensing of system metrics, enabling proactive maintenance strategies that minimize service disruptions. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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12 pages, 1630 KiB

Open AccessArticle

Reinforcing Deep Learning-Enabled Surveillance with Smart Sensors

by Taewoo Lee, Yumin Choi and Hyunbum Kim

Sensors 2025, 25(11), 3345; https://doi.org/10.3390/s25113345 - 26 May 2025

Viewed by 275

Abstract

It is critical to solidify surveillance in 3D environments with heterogeneous sensors. This study introduces an innovative deep learning-assisted surveillance reinforcement system with smart sensors for resource-constrained cyber-physical devices and mobile elements. The proposed system incorporates deep learning technologies to address the challenges [...] Read more.

It is critical to solidify surveillance in 3D environments with heterogeneous sensors. This study introduces an innovative deep learning-assisted surveillance reinforcement system with smart sensors for resource-constrained cyber-physical devices and mobile elements. The proposed system incorporates deep learning technologies to address the challenges of dynamic public environments. By enhancing the adaptability and effectiveness of surveillance in environments with high human mobility, this paper aims to optimize surveillance node placement and ensure real-time system responsiveness. The integration of deep learning not only improves accuracy and efficiency but also introduces unprecedented flexibility in surveillance operations. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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28 pages, 4628 KiB

Open AccessArticle

Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems

by Joung-Hwan Yoon, Chibuzo Nwabufo Okwuosa, Nnamdi Chukwunweike Aronwora and Jang-Wook Hur

Sensors 2025, 25(8), 2449; https://doi.org/10.3390/s25082449 - 13 Apr 2025

Viewed by 555

Abstract

The industrial application of artificial intelligence (AI) has witnessed outstanding adoption due to its robust efficiency in recent times. Image fault detection and classification have also been implemented industrially for product defect detection, as well as for maintaining standards and optimizing processes using [...] Read more.

The industrial application of artificial intelligence (AI) has witnessed outstanding adoption due to its robust efficiency in recent times. Image fault detection and classification have also been implemented industrially for product defect detection, as well as for maintaining standards and optimizing processes using AI. However, there are deep concerns regarding the latency in the performance of AI for fault detection in glossy and curved surface products, due to their nature and reflective surfaces, which hinder the adequate capturing of defective areas using traditional cameras. Consequently, this study presents an enhanced method for curvy and glossy surface image data collection using a Basler vision camera with specialized lighting and KEYENCE displacement sensors, which are used to train deep learning models. Our approach employed image data generated from normal and two defect conditions to train eight deep learning algorithms: four custom convolutional neural networks (CNNs), two variations of VGG-16, and two variations of ResNet-50. The objective was to develop a computationally robust and efficient model by deploying global assessment metrics as evaluation criteria. Our results indicate that a variation of ResNet-50, ResNet-50₂₂₄, demonstrated the best overall efficiency, achieving an accuracy of 97.97%, a loss of 0.1030, and an average training step time of 839 milliseconds. However, in terms of computational efficiency, it was outperformed by one of the custom CNN models, CNN₆-240, which achieved an accuracy of 95.08%, a loss of 0.2753, and an average step time of 94 milliseconds, making CNN₆-240 a viable option for computational resource-sensitive environments. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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22 pages, 1021 KiB

Open AccessArticle

State-Based Fault Diagnosis of Finite-State Vector Discrete-Event Systems via Integer Linear Programming

by Qinrui Chen, Mubariz Garayev and Ding Liu

Sensors 2025, 25(5), 1452; https://doi.org/10.3390/s25051452 - 27 Feb 2025

Viewed by 463

Abstract

This paper presents a state-based method to address the verification of

K

-diagnosability and fault diagnosis of a finite-state vector discrete-event system (Vector DES) with partially observable state outputs due to limited sensors. Vector DES models consist of an arithmetic additive structure in [...] Read more.

This paper presents a state-based method to address the verification of

K

-diagnosability and fault diagnosis of a finite-state vector discrete-event system (Vector DES) with partially observable state outputs due to limited sensors. Vector DES models consist of an arithmetic additive structure in both the state space and state transition function. This work offers a necessary and sufficient condition for verifying the

K

-diagnosability of a finite-state Vector DES based on state sensor outputs, employing integer linear programming and the mathematical representation of a Vector DES. Predicates are employed to diagnose faults in a Vector DES online. Specifically, we use three different kinds of predicates to divide system state outputs into different subsets, and the fault occurrence in a system is detected by checking a subset of outputs. Online diagnosis is achieved via solving integer linear programming problems. The conclusions obtained in this work are explained by means of several examples. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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21 pages, 3635 KiB

Open AccessArticle

Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance

by Qing Dong, Hong Pei, Changhua Hu, Jianfei Zheng and Dangbo Du

Sensors 2025, 25(4), 1218; https://doi.org/10.3390/s25041218 - 17 Feb 2025

Viewed by 696

Abstract

Predictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful life (RUL) prediction and a maintenance [...] Read more.

Predictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful life (RUL) prediction and a maintenance strategy. However, among the numerous studies that conducted maintenance and replacement activities based on the results of RUL prediction, little attention has been paid to the impact of preventive maintenance on sensor-based monitoring data, which further affects the RUL for repairable degrading devices. In this paper, an adaptive RUL prediction method is proposed for repairable degrading devices in order to improve the accuracy of prediction results and achieve adaptability to future degradation processes. Firstly, a phased degradation model based on an adaptive Wiener process is established, taking into account the impact of imperfect maintenance. Meanwhile, integrating the impact of maintenance activities on the degradation rate and state, the probability distribution of RUL can be derived based on the concept of first hitting time (FHT). Secondly, a method is proposed for model parameter identification and updating that incorporates the individual variation among devices, integrating maximum likelihood estimation and Bayesian inference. Finally, the effectiveness of the RUL prediction method is ultimately validated through numerical simulation and its application to repairable gyroscope degradation data. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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14 pages, 15017 KiB

Open AccessArticle

Maintenance Decision-Making Using Intelligent Prognostics Within a Single Spare Parts Support System

by Bowei Zhang, Changhua Hu, Jianfei Zheng and Hong Pei

Sensors 2025, 25(3), 837; https://doi.org/10.3390/s25030837 - 30 Jan 2025

Viewed by 889

Abstract

Health management is the foothold of remaining useful life (RUL) prediction, known as ‘prognostics’. However, sudden failures in complex systems can lead to increased downtime and maintenance costs, ultimately diminishing system health and availability. Considering intelligent prognostics of components, maintenance decision-making for spare [...] Read more.

Health management is the foothold of remaining useful life (RUL) prediction, known as ‘prognostics’. However, sudden failures in complex systems can lead to increased downtime and maintenance costs, ultimately diminishing system health and availability. Considering intelligent prognostics of components, maintenance decision-making for spare parts ordering and replacement is proposed within a spare parts support system. The decision-making process aims to minimize costs while maximizing availability as its primary objective. It considers spare parts ordering time and replacement time as key decision variables. By developing a maintenance decision-making model, it aims to determine the optimal time for ordering and replacing spare parts. This maintenance approach is designed to provide technical support for effective and rational equipment management decision-making. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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26 pages, 13903 KiB

Open AccessArticle

Triboelectric Nanogenerator-Embedded Intelligent Self-Aligning Roller Bearing with the Capability of Self-Sensing, Monitoring, and Fault Diagnosis

by Hao Shen, Yufan Lv, Yun Kong, Qinkai Han, Ke Chen, Zhibo Geng, Mingming Dong and Fulei Chu

Sensors 2024, 24(23), 7618; https://doi.org/10.3390/s24237618 - 28 Nov 2024

Viewed by 874

Abstract

Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has [...] Read more.

Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has an application in fault diagnosis. The designed I-SAB is compactly embedded with a novel sweep-type triboelectric nanogenerator (TENG). The TENG is realized within the proposed I-SAB using a comb–finger electrode pair and a flannelette triboelectric layer. A floating, sweeping, and freestanding mode is utilized, which can prevent collisions and considerably enhance the operational life of the embedded TENG. Experiments are subsequently conducted to optimize the output performance and sensing sensitivity of the proposed I-SAB. The results of a speed-sensing experiment show that the characteristic frequencies of triboelectric current and voltage signals are both perfectly proportional to the rotational speed, indicating that the designed I-SAB has the self-sensing capability for rotational speed. Additionally, as both the bias angle and rotational speed of the SAB increase, the envelope amplitudes of the triboelectric voltage signals generated by the I-SAB rise at a rate of 0.0057 V·deg⁻¹·rpm⁻¹. To further demonstrate the effectiveness of the triboelectric signals emitted from the designed I-SAB in terms of self-powered fault diagnosis, a Multi-Scale Discrimination Network (MSDN), based on the ResNet18 architecture, is proposed in order to classify the various fault conditions of the SAB. Using the triboelectric voltage and current signals emitted from the designed I-SAB as inputs, the proposed MSDN model yields excellent average diagnosis accuracies of 99.8% and 99.1%, respectively, indicating its potential for self-powered fault diagnosis. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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12 pages, 3089 KiB

Open AccessArticle

Effect of Dielectric Layer on Miniaturized Patch Antenna Sensor

by Caifeng Chen, Lei Zou, Chenglong Bi and Andong Wang

Sensors 2024, 24(23), 7608; https://doi.org/10.3390/s24237608 - 28 Nov 2024

Cited by 1 | Viewed by 1426

Abstract

Miniature patch antenna sensors have great potential in the field of structural health monitoring for crack propagation detection due to their small size and high sensitivity. A primary research focus has been achieving efficient miniaturization, with the performance of the dielectric layer playing [...] Read more.

Miniature patch antenna sensors have great potential in the field of structural health monitoring for crack propagation detection due to their small size and high sensitivity. A primary research focus has been achieving efficient miniaturization, with the performance of the dielectric layer playing a pivotal role. Studies have demonstrated that increasing the relative dielectric constant (ε_r) of the dielectric layer can reduce antenna size, but higher dielectric losses (tanδ) can lower radiation efficiency. This study identifies the optimal dielectric properties by examining the interplay between ε_r and tanδ to balance size reduction and radiation efficiency. Additionally, while increasing the dielectric layer’s thickness improves bandwidth and radiation efficiency, a thinner layer is preferred to maintain overall performance without compromising radiation efficiency. Furthermore, the resonant frequency of the smaller-sized patch antenna sensor exhibits greater detection sensitivity to crack propagation. These insights provide useful guidance for selecting effective dielectric layers and assist in the miniaturization design of antenna sensors. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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Review

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41 pages, 10236 KiB

Open AccessReview

Coaxial Cable Distributed Strain Sensing: Methods, Applications and Challenges

by Stephanie King, Gbanaibolou Jombo, Oluyomi Simpson, Wenbo Duan and Adrian Bowles

Sensors 2025, 25(3), 650; https://doi.org/10.3390/s25030650 - 22 Jan 2025

Viewed by 1562

Abstract

Distributed strain sensing is a powerful tool for in situ structural health monitoring for a wide range of critical engineering infrastructures. Strain information from a single sensing device can be captured from multiple locations simultaneously, offering a reduction in hardware, wiring, installation costs, [...] Read more.

Distributed strain sensing is a powerful tool for in situ structural health monitoring for a wide range of critical engineering infrastructures. Strain information from a single sensing device can be captured from multiple locations simultaneously, offering a reduction in hardware, wiring, installation costs, and signal analysis complexity. Fiber optic distributed strain sensors have been the widely adopted approach in this field, but their use is limited to lower strain applications due to the fragile nature of silica fiber. Coaxial cable sensors offer a robust structure that can be adapted into a distributed strain sensor. They can withstand greater strain events and offer greater resilience in harsh environments. This paper presents the developments in methodology for coaxial cable distributed strain sensors. It explores the two main approaches of coaxial cable distributed strain sensing such as time domain reflectometry and frequency domain reflectometry with applications. Furthermore, this paper highlights further areas of research challenges in this field, such as the deconvolution of strain and temperature effects from coaxial cable distributed strain sensor measurements, mitigating the effect of dielectric permittivity on the accuracy of strain measurements, addressing manufacturing challenges with the partial reflectors for a robust coaxial cable sensor, and the adoption of data-driven analysis techniques for interrogating the interferogram to eliminate concomitant measurement effects with respect to temperature, dielectric permittivity, and signal-to-noise ratio, amongst others Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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