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Intelligent Maintenance and Fault Diagnosis of Mobility Equipment
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Intelligent Maintenance and Fault Diagnosis of Mobility Equipment

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

Deadline for manuscript submissions: 25 July 2025 | Viewed by 1515

Special Issue Editors

Prof. Dr. Hongtao Xue

E-Mail Website
Guest Editor
School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212000, China
Interests: signal processing; online diagnosis; safety assessment
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Peng Chen

E-Mail Website
Guest Editor
Graduate School of Bioresources, Mie University, Tsu, Mie 514-8507, Japan
Interests: condition monitoring and diagnosis of machinery; diagnostic instrument and system for machinery; decision making of maintenance policy; diagnosis and inspection robot for plant machinery
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiqiang Liao

E-Mail Website
Guest Editor
Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524088, China
Interests: condition monitoring and fault diagnosis of ship power equipment; intelligent operation and maintenance of ships; intelligent signal processing

Special Issue Information

Dear Colleagues,

In recent years, mobility equipment has seen rapid technological advancements, making maintenance and fault diagnosis increasingly complex. Integrating intelligent systems and sensing technologies has brought significant improvements in mobility equipment maintenance and fault diagnosis.

This Special Issue invites original research that addresses key challenges in developing autonomous maintenance frameworks to enhance mobility equipment's reliability, safety, and performance. The aim is to explore the latest techniques in data-driven maintenance frameworks, predictive diagnostics, real-time fault detection, and fault response strategies using advanced sensors, cyber-physical systems, and machine learning models.

Topics of interest include, but are not limited to:

  • Next-generation sensor technologies;
  • Vibration and acoustic analysis;
  • Signal processing techniques;
  • Real-time fault detection;
  • Model-based diagnosis;
  • Fault-tolerant control strategies;
  • Intelligent control algorithms for vibration and noise;
  • Predictive maintenance;
  • Multimodal data and information fusion and transfer;
  • Other topics related to mobility equipment for fault diagnosis.

Prof. Dr. Hongtao Xue
Prof. Dr. Peng Chen
Dr. Zhiqiang Liao
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

  • mobility equipment
  • diagnostic robots
  • intelligent fault diagnosis
  • predictive maintenance
  • online monitoring
  • signal processing
  • vibration and noise control
  • fault-tolerant control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 28356 KiB  
Article
Identification of Defects in Low-Speed and Heavy-Load Mechanical Systems Using Multi-Fusion Analytic Mode Decomposition Method
by Yanlei Liu, Kun Zhang, Miaorui Yang, Xu Zhang and Yonggang Xu
Sensors 2025, 25(6), 1848; https://doi.org/10.3390/s25061848 - 16 Mar 2025
Viewed by 267
Abstract
In view of the higher requirements of modern machinery for multi-sensor information acquisition and fusion technology, this paper proposes a novel multi-fusion analytic mode decomposition (MFAMD) method to separate and demodulate fault features in signals. In low-speed and heavy-load equipment, the signals collected [...] Read more.
In view of the higher requirements of modern machinery for multi-sensor information acquisition and fusion technology, this paper proposes a novel multi-fusion analytic mode decomposition (MFAMD) method to separate and demodulate fault features in signals. In low-speed and heavy-load equipment, the signals collected by multiple sensors contain unknown and unequal fault features and interference. Quaternion-based frequency domain fusion technology and analytically based modal extraction technology can offer novel approaches to processing large data sets in parallel while handling lengthy signals and high sampling rates. The trend spectrum segmentation method based on quaternions optimizes the hysteresis of the binary frequency. The experimental signal verifies that the proposed method is suitable for low-speed and heavy-load bearing faults. Full article
(This article belongs to the Special Issue Intelligent Maintenance and Fault Diagnosis of Mobility Equipment)
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26 pages, 3217 KiB  
Article
Fault-Tolerant Collaborative Control of Four-Wheel-Drive Electric Vehicle for One or More In-Wheel Motors’ Faults
by Han Feng, Yukun Tao, Jianbo Feng, Yule Zhang, Hongtao Xue, Tiansi Wang, Xing Xu and Peng Chen
Sensors 2025, 25(5), 1540; https://doi.org/10.3390/s25051540 - 1 Mar 2025
Cited by 1 | Viewed by 828
Abstract
A fault-tolerant collaborative control strategy for four-wheel-drive electric vehicles is proposed to address hidden safety issues caused by one or more in-wheel motor faults; the basic design scheme is that the control system is divided into two layers of motion tracking and torque [...] Read more.
A fault-tolerant collaborative control strategy for four-wheel-drive electric vehicles is proposed to address hidden safety issues caused by one or more in-wheel motor faults; the basic design scheme is that the control system is divided into two layers of motion tracking and torque distribution, and three systems, including driving, braking, and front-wheel steering are controlled collaboratively for four-wheel torque distribution. In the layer of motion tracking, a vehicle model with two-degree-of-freedom is employed to predict the control reference values of the longitudinal force and additional yaw moment required; four types of sensors, such as wheel speed, acceleration, gyroscope, and steering wheel angle, are used to calculate the actual values. At the torque distribution layer, SSOD and MSCD distribution schemes are designed to cope with two operating conditions, namely sufficient and insufficient output capacity after local hub motor failure, respectively, focusing on the objective function, constraints, and control variables of the MSCD control strategy. Finally, two operating environments, a straight-line track, and a DLC track, are set up to verify the effectiveness of the proposed control method. The results indicate that, compared with traditional methods, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 12.9% and 5.88%, respectively, in the straight-line track environment. In the DLC track environment, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 6% and 4.5%, respectively. The proposed fault-tolerant controller ensures that the four-wheel-drive electric vehicle meets the requirements of handling stability and safety under one or more hub motor failure conditions. Full article
(This article belongs to the Special Issue Intelligent Maintenance and Fault Diagnosis of Mobility Equipment)
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