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Sensors
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Sensor-Based Fault Detection and Diagnosis in Mechatronic Systems
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Sensor-Based Fault Detection and Diagnosis in Mechatronic Systems

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 405

Special Issue Editors

Prof. Dr. Huimin Wang

E-Mail Website
Guest Editor
School of Electrical Engineering, Tiangong University, Tianjin 300387, China
Interests: design, optimization and control of permanent magnet machine
Dr. Liyan Guo

E-Mail Website
Guest Editor
School of Electrical Engineering, Tiangong University, Tianjin 300387, China
Interests: design and optimization for permanent magnet machine

Special Issue Information

Dear Colleagues,

This Special Issue aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of sensor-based fault detection and diagnosis in mechatronic systems.

Prof. Dr. Huimin Wang
Dr. Liyan Guo
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • current sensor fault
  • speed sensor fault
  • position sensor fault
  • rotor position detection
  • sensorless control

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Published Papers (1 paper)

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Research

20 pages, 2298 KiB  
Article
Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation
by Luis Miguel Moreno Haro, Adaiton Oliveira-Filho, Bruno Agard and Antoine Tahan
Sensors 2025, 25(7), 2175; https://doi.org/10.3390/s25072175 - 29 Mar 2025
Viewed by 268
Abstract
This paper presents a novel approach for detecting sensor failures using image-based feature representation and the Convolutional Variational Autoencoder (CVAE) model. Existing methods are limited when analyzing multiple failure modes simultaneously or adapting to diverse sensor data. This limitation may compromise decision-making and [...] Read more.
This paper presents a novel approach for detecting sensor failures using image-based feature representation and the Convolutional Variational Autoencoder (CVAE) model. Existing methods are limited when analyzing multiple failure modes simultaneously or adapting to diverse sensor data. This limitation may compromise decision-making and system performance, hence the need for more flexible and resilient models. The proposed approach transforms sensor data into image-based feature representations of statistics such as mean, variance, kurtosis, entropy, skewness, and correlation. The CVAE is trained on such image representations, and the corresponding reconstruction error leads to a Health Index (HI) for detecting multiple sensor failures. Moreover, the CVAE latent space is used to define a complementary HI and a convenient visualization tool, enhancing the interpretability of the proposed approach. The evaluation of the proposed detection approach with data comprising diverse configurations of faulty sensors showed encouraging results. The proposed approach is illustrated in an industrial case study emerging from the aeronautical domain, with data from a complex electromechanical system comprising nearly 80 sensor measurements at a 1 Hz sampling rate. The results demonstrate the potential of the proposed method in detecting multiple sensor failures. Full article
(This article belongs to the Special Issue Sensor-Based Fault Detection and Diagnosis in Mechatronic Systems)
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