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Sensor-Based Fault Diagnosis and Prognosis

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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: 31 July 2026 | Viewed by 5871

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Special Issue Editors

Prof. Dr. Ying Zheng

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Guest Editor

Institute of Artificial Intelligence and Automation , Huazhong University of Science and Technology, Wuhan, China
Interests: fault diagnosis; industrial manufacturing process monitoring; industrial data analysis; process control

Prof. Dr. Ke Zhang

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Guest Editor

School of Automation, Chongqing University, Chongqing, China
Interests: intelligent control and fault diagnosis; system safety

Prof. Dr. Lina Yao

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Guest Editor

School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
Interests: fault diagnosis and prognostics; fault tolerant control; stochastic distribution control

Dr. Yang Wang

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Guest Editor

Department of Data Science, City University of Hong Kong, Hong Kong, China
Interests: fault detection and diagnosis; industrial data analysis; data-driven modeling and diagnosis

Special Issue Information

Dear Colleagues,

With the rapid development of advanced sensors and intelligent monitoring technologies, fault diagnosis and prognosis have entered a new era of precision and reliability. Modern sensors enable continuous data acquisition, real-time analysis, and predictive decision-making, which are critical for ensuring the safety, efficiency, and sustainability of modern engineering systems. This Special Issue seeks to highlight novel theory, methodologies, and applications of sensor-based approaches for fault diagnosis and prognosis. Topics of interest include, but are not limited to, the following: novel sensor technologies; signal processing and feature extraction; machine learning and artificial intelligence for fault detection and prognosis; prognostics and health management; industrial process monitoring; robust and interpretable models; and industrial case studies. Contributions in theoretical development, simulation studies, experimental validation, and engineering applications are all welcome.

Prof. Dr. Ying Zheng
Prof. Dr. Ke Zhang
Prof. Dr. Lina Yao
Dr. Yang Wang
Guest Editors

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Keywords

fault diagnosis
prognostics and health management (PHM)
intelligent sensors
industrial process monitoring
signal processing and feature extraction
predictive maintenance
industrial IoT
condition monitoring

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Published Papers (10 papers)

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Research

24 pages, 12181 KB

Open AccessArticle

Bio-Inspired Internal Representations of Tactile Sensation, Pain, and Damage for Artificial Skin Using Spatio-Temporal Anomaly Detection

by Shinnosuke Fukagawa and Mitsuharu Matsumoto

Sensors 2026, 26(10), 3125; https://doi.org/10.3390/s26103125 - 15 May 2026

Abstract

In recent years, the deployment of robots in human-centric environments has necessitated the development of artificial skins that integrate safety and durability. Traditional damage detection often relies on raw signal thresholds, lacking the functional integration of touch, pain, and damage found in biological systems. This study proposes a bio-inspired artificial skin model that separately evaluates these three states through a spatio-temporal anomaly detection framework. We developed an unsupervised model combining a Convolutional Autoencoder (CAE) and Convolutional LSTM (ConvLSTM) to learn the latent representations of tactile maps from intact skin. By quantifying spatial reconstruction and temporal prediction errors, the system generates individual scores for touch, pain, and damage. Pain is defined as an abstract signal of instantaneous abnormality, while damage is identified as a persistent structural deviation. We implemented a dynamic thresholding mechanism mimicking biological sensitization and recovery, with damage detection gated by a pain-flag constraint to minimize false positives. Experimental results across various conditions—including incisions (3–6 cm) and abrasions (10–30 times)—demonstrate that the model can distinguish between momentary noxious stimuli and sustained structural degradation. Quantitative evaluation shows that the proposed model achieves an Area Under the Curve (AUC) of 0.653, outperforming a threshold-based baseline and maintaining zero false positives under strong, non-damaging contact. Specifically, the system successfully mimics biological aftereffects and the pain-gating mechanism, where damage is only assessed in the presence of a pain-related trigger. This research provides a scalable, software-driven foundation for robot self-protection that overcomes the implementation constraints of hardware-dependent neuromorphic systems. Full article

(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)

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24 pages, 6247 KB

Open AccessArticle

Sensor-Based Fault Diagnosis and Prognosis of Neurophysiological States: A Transformer Autoencoder Approach to EEG Monitoring

by Jesús Jaime Moreno Escobar, Mauro Daniel Castillo Pérez, Erika Yolanda Aguilar del Villar and Hugo Quintana Espinosa

Sensors 2026, 26(9), 2913; https://doi.org/10.3390/s26092913 - 6 May 2026

Viewed by 595

Abstract

This study presents a sensor-based condition monitoring framework for the diagnosis and prognosis of neurophysiological states using electroencephalographic (EEG) signals. Leveraging a comparative deep learning architecture, we evaluate a baseline Variational Autoencoder against a Transformer-based Autoencoder to model latent representations of EEG dynamics across three therapeutic phases: pre-intervention, during intervention, and post-intervention. The proposed methodology aligns with sensor-based fault diagnosis principles by treating deviations from stable neurophysiological states as diagnostic indicators and temporal phase transitions as markers of therapeutic stage progression. Using a dataset of 94 EEG sessions from six subjects with diverse neurological conditions, we demonstrate that the Transformer Autoencoder, through its self-attention mechanism, captures cross-band spectral relationships more effectively than the VAE, resulting in denser within-phase clusters and improved separation between therapeutic stages. Quantitative evaluation reveals small but statistically significant effects between pre- and during-intervention phases (

η_{partial}^{2} = 0.0388

) and pre- and post-intervention phases (

η_{partial}^{2} = 0.0470

), predominantly driven by delta, theta, beta, and gamma rhythms. These findings illustrate how sensor-based latent state monitoring can provide interpretable, data-driven insights for condition assessment and phase transition assessment between sessions in complex dynamic systems, with potential applicability beyond clinical domains to industrial condition monitoring and fault diagnosis tasks. The framework confirms that it offers qualitative indicators, rather than predictive clinical outputs. Full article

(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)

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22 pages, 3709 KB

Open AccessArticle

A Metric-Driven Evaluation Framework for Remaining Useful Life Prognosis with Quantified Uncertainty

by Govind Vashishtha, Sumika Chauhan and Merve Ertarğın

Sensors 2026, 26(7), 2230; https://doi.org/10.3390/s26072230 - 3 Apr 2026

Viewed by 404

Abstract

This paper introduces a novel metric-driven evaluation framework for Remaining Useful Life (RUL) prognosis in rotating machinery, featuring robust uncertainty quantification. Accurate RUL prediction is vital for optimizing maintenance and preventing failures, but existing methods often struggle with complex nonlinear degradation or lack reliable uncertainty estimates. Our proposed framework integrates a probabilistic Deep State Space Model (DSSM) with a variational inference approach to model complex, non-linear degradation trends and inherent aleatoric uncertainty. A key innovation is the use of the Slime Mold Algorithm (SMA) for efficient hyperparameter optimization, ensuring maximum accuracy. Furthermore, an online adaptation mechanism, governed by a heuristic reinforcement learning agent, allows the model to continuously update its knowledge and adapt to concept drift in real-time. Experimental validation on the IMS bearing dataset demonstrates superior RUL prediction accuracy, evidenced by the lowest Root Mean Square Error (RMSE) of 8.1829 cycles, and a PICP of 0.59416. This dual capability makes the framework highly suitable for real-world predictive maintenance, enhancing safety and reliability. Full article

(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)

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26 pages, 2184 KB

Open AccessArticle

Performance Analysis of Advanced Feature Extraction Methods for Manufacturing Defect Detection via Vibration Sensors in CNC Milling Machines

by Gürkan Bilgin

Sensors 2026, 26(7), 2195; https://doi.org/10.3390/s26072195 - 2 Apr 2026

Viewed by 687

Abstract

This study investigates the effectiveness of various feature extraction methods applied to vibration signals for the automatic detection of production defects in CNC (Computerised Numerical Control) milling machines. A dataset consisting of real-world data collected from CNC machines equipped with accelerometers was used. The objective of the study is to compare three main groups of techniques: time-domain analysis (TDA), frequency-domain analysis (FDA), and time–frequency-domain analysis (TFA). The findings indicate that basic TDA features lack the necessary sensitivity to accurately distinguish between Good Processing (GP) and Bad Processing (BP) states. Frequency-domain methods, such as the Fast Fourier Transform (FFT), median frequency calculation, and the Welch periodogram, provide better insights but still have limitations. The most effective results are obtained with TFA methods, particularly Empirical Mode Decomposition (EMD) and the Hilbert–Huang Transform (HHT), which reveal deeper signal characteristics. Following the feature optimisation studies, it was determined that a combination of four features—FMED, IMF2, IMF5 and WPT26—yielded the optimal performance, with an accuracy of 91.48%. The incorporation of a fifth feature resulted in information saturation within the model and did not improve performance. This study makes a novel contribution to literature by conducting an in-depth investigation into the most effective feature extraction and selection techniques for achieving robust discrimination between GP and BP productions using vibration signals in CNC milling processes. Conclusively, TFA features, supported by advanced signal processing, offer a strong basis for reliable, automated defect detection in CNC milling operations. Full article

(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)

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28 pages, 2499 KB

Open AccessArticle

Cross-Bonded Cable Circuits Identification Based on Deep Embedded Clustering of Sheath Current Sensing

by Hang Wang, Zhi Li, Wenfang Ding, Jing Tu, Liqiang Wang and Jun Chen

Sensors 2026, 26(5), 1591; https://doi.org/10.3390/s26051591 - 3 Mar 2026

Viewed by 459

Abstract

Online identification of HV cable circuits is vital for routine inspection and maintenance, yet existing passive electromagnetic wave injection methods are limited to offline operations. To fill the gap and achieve the online identification of HV cable circuits, an online circuit identification methodology based on sheath current temporal characteristics and deep embedded clustering is proposed. First, an equivalent circuit model of the multi-circuit cross-bonded cable sheath was built to deduce the temporal similarity of sheath currents within the same circuit, establishing the identification criterion. Second, the robustness of the temporal similarity under various operating conditions was verified via simulation based on the Dynamic Time Warping (DTW) distance. Then, a combined model of Temporal Convolutional Network Autoencoder (TCN-AE) and K-medoids was established to transform circuit identification into a temporal clustering problem of sheath currents, realizing circuit determination by synchronously monitoring the time-series sheath current data of multi-circuit HV cross-bonded cables. The method was verified on a full-scale 110 kV cable test platform. The results show that the identification accuracy reached 95.37%, and the proposed method can effectively identify the circuits of cross-bonded cables with high robustness against the domain gap, having significant engineering application value. Full article

(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)

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20 pages, 6711 KB

Open AccessArticle

RUL Prediction Based on xLSTM–Transformer Neural Network for Rolling Element Bearings Under Different Working Conditions

by Runzhong Jiang, Ziqi Li, Haiyu Lu, Weizhong Mo, Wei Huang and Minmin Xu

Sensors 2026, 26(5), 1578; https://doi.org/10.3390/s26051578 - 3 Mar 2026

Viewed by 583

Abstract

Remaining useful life (RUL) prediction of rolling bearings is a crucial issue in intelligent predictive maintenance, thereby ensuring equipment safety and reducing maintenance costs. To address the challenge that traditional deep learning models struggle to simultaneously capture local temporal features and global degradation trends when processing degradation health indicators (HI), this paper proposes a hybrid RUL prediction model based on extended Long Short-Term Memory (xLSTM) and Transformer. The model employs an encoder–decoder architecture, integrating the Multi-Head Attention mechanism with the xLSTM module. This design simultaneously enhances the modeling capability of short-term dynamic features and effectively captures long-term degradation patterns. Validation was conducted on the XJTU-SY and PHM2012 datasets. The proposed model outperformed the comparative models across evaluation metrics such as Root Mean Square Error (RMSE), Coefficient of Determination (R²) and the Score, achieving a significant improvement in prediction accuracy and multi-dataset generalization capability. The proposed network provides a more accurate and generalizable solution for bearing health assessment and remaining useful life prediction and demonstrates significant potential for intelligent health management of industrial equipment. Full article

(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)

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22 pages, 7815 KB

Open AccessArticle

Phase Selection Method for 10 kV Three-Core Cables Under Single-Phase Grounding Fault Transient Based on Surface Magnetic Field Sensing

by Hang Wang, Tianhu Weng, Wenfang Ding, Shuai Yang, Zheng Xiao, Hang Li and Jun Chen

Sensors 2026, 26(3), 1016; https://doi.org/10.3390/s26031016 - 4 Feb 2026

Viewed by 352

Abstract

Single-phase grounding is the dominant fault type in urban power distribution networks. Because the total magnetic flux would not change around the cable under a single-phase grounding fault, ferromagnetic zero-sequence current sensors cannot distinguish the faulted phase of belted cables, which are the main type in 10 kV distribution networks. To fill this gap, a two-step methodology is proposed using an annular TMR magnetic sensor to measure the magnetic field intensity at six points on the cable surface and to distinguish the faulted phase using the magnetic field intensity differences between the TMRs. The first step is calculating the rotation angles between the six magnetic sensors and the three cable cores after installation. A differential evolution algorithm is used to calculate the rotation angles in the sensing model. The second step is to detect the fault phase under a single-phase grounding fault transient, with the magnetic field intensity difference taken as the criterion. The methodology is verified through simulation and experiment. The results show that the relative errors of the rotation angles are all less than 1%. Under a single-phase grounding fault, the faulted phase can be accurately identified. The proposed method can effectively identify the faulted phase of 10 kV three-core cables under single-phase grounding and has significant engineering application value. Full article

(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)

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32 pages, 3731 KB

Open AccessArticle

A Comparative Study of RQA-Guided Attention Mechanisms with LSTM Autoencoder for Bearing Anomaly Detection

by Ayşenur Hatipoğlu and Ersen Yılmaz

Sensors 2026, 26(3), 1015; https://doi.org/10.3390/s26031015 - 4 Feb 2026

Cited by 1 | Viewed by 751

Abstract

Accurate anomaly detection in rotating machinery under noisy conditions remains challenging in Prognostics and Health Management (PHM). Existing deep learning autoencoders and attention mechanisms rely primarily on data-driven similarity measures and fail to explicitly incorporate nonlinear dynamical characteristics of degradation. In this study, we propose a Recurrence Quantification Analysis-Aware Attention (RQAA) framework that systematically injects chaos-theoretic descriptors into the attention mechanism of LSTM-based autoencoders for unsupervised anomaly detection. Specifically, RQA metrics including recurrence rate, determinism, laminarity, entropy, and trapping time are computed at the window level and embedded into the query-key-value attention scoring to guide the model toward dynamically informative temporal patterns. Three attention variants are developed to investigate different fusion strategies between learned representations and RQA-driven structural cues. The proposed framework is evaluated on three widely used bearing vibration datasets, which are IMS, CWRU, and HUST. Experimental results demonstrate that RQAA consistently outperforms conventional LSTM autoencoders and classical attention-based models, achieving up to 99.85% F1-score and 99.00% AUC while exhibiting superior robustness in low signal-to-noise scenarios. Further analysis reveals that explicit dynamical guidance enhances anomaly separability and reduces false alarms, particularly in early-stage fault detection. These findings indicate that integrating nonlinear dynamical information directly into attention scoring offers a principled and effective pathway for advancing unsupervised anomaly detection in rotating machinery and safety-critical industrial systems. Full article

(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)

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19 pages, 4184 KB

Open AccessArticle

Bearing Anomaly Detection Method Based on Multimodal Fusion and Self-Adversarial Learning

by Han Liu, Yong Qin and Dilong Tu

Sensors 2026, 26(2), 629; https://doi.org/10.3390/s26020629 - 17 Jan 2026

Cited by 1 | Viewed by 701

Abstract

In the context of bearing anomaly detection, challenges such as imbalanced sample distribution and complex operational conditions present significant difficulties for data-driven deep learning models. These issues often result in overfitting and high false positive rates in complex real-world scenarios. This paper proposes a strategy that leverages multimodal fusion and Self-Adversarial Training (SAT) to construct and train a deep learning model. First, the one-dimensional bearing vibration time-series data are converted into Gramian Angular Difference Field (GADF) images, and multimodal feature fusion is performed with the original time-series data to capture richer spatiotemporal correlation features. Second, a composite data augmentation strategy combining time-domain and image-domain transformations is employed to effectively expand the anomaly samples, mitigating data scarcity and class imbalance. Finally, the SAT mechanism is introduced, where adversarial samples are generated within the fused feature space to compel the model to learn more generalized and robust feature representations, thereby significantly enhancing its performance in realistic and noisy environments. Experimental results demonstrate that the proposed method outperforms traditional baseline models across key metrics such as accuracy, precision, recall, and F1-score in abnormal bearing anomaly detection. It exhibits exceptional robustness against rail-specific interferences, offering a specialized solution strictly tailored for the unique, high-noise operational environments of intelligent railway maintenance. Full article

(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)

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19 pages, 4790 KB

Open AccessArticle

Hierarchical Fuzzy Adaptive Observer-Based Fault-Tolerant Consensus Tracking for High-Order Nonlinear Multi-Agent Systems Under Actuator and Sensor Faults

by Lei Zhao and Shiming Chen

Sensors 2026, 26(1), 252; https://doi.org/10.3390/s26010252 - 31 Dec 2025

Cited by 1 | Viewed by 644

Abstract

This paper investigates the consensus tracking problem for a class of high-order nonlinear multi-agent systems subject to actuator faults, sensor faults, unknown disturbances, and model uncertainties. To effectively address this problem, a hierarchical fault-tolerant control framework with fuzzy adaptive mechanisms is proposed. First, a distributed output predictor based on a finite-time differentiator is constructed for each follower to estimate the leader’s output trajectory and to prevent fault propagation across the network. Second, a novel state and actuator-fault observer is designed to reconstruct unmeasured states and detect actuator faults in real time. Third, a sensor-fault compensation strategy is integrated into a backstepping procedure, resulting in a fuzzy adaptive consensus-tracking controller. This controller guarantees the uniform boundedness of all closed-loop signals and ensures that the tracking error converges to a small neighborhood of the origin. Finally, numerical simulations validate the effectiveness and robustness of the proposed method in the presence of multiple simultaneous faults and disturbances. Full article

(This article belongs to the Special Issue Sensor-Based Fault Diagnosis and Prognosis)

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