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Sensor-Based Fault Diagnosis and Prognosis
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Sensor-Based Fault Diagnosis and Prognosis

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 805

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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

  • 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 (2 papers)

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Research

19 pages, 4184 KB  
Article
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
Viewed by 147
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 [...] Read more.
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  
Article
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
Viewed by 368
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, [...] Read more.
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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