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Machines
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Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and...
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Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis, 2nd Edition

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 524

Special Issue Editors

Prof. Dr. Jinglong Chen

E-Mail Website
Guest Editor
State Key Laboratory for Manufacturing and Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: mechanical fault diagnosis; deep learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Tongyang Pan

E-Mail Website
Guest Editor
School of Traffic and Transportation Engineering, Central South University, Changsha 410083, China
Interests: mechanical fault diagnosis; deep learning; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electric motors and drives are important power systems in modern industrial equipment. Due to novel design concepts and advancements in new technologies such as sensing, manufacturing, communication, management, and systems integrity, electric motors and drives have become more sophisticated than ever, making performance prediction and fault diagnosis a challenging problem in ensuring their reliable operation. However, accurate and timely performance prediction and fault diagnosis are difficult due to the following factors: (i) performance prediction and fault diagnosis are coupled subjects that involve modeling analysis, sensing and monitoring, signal processing, and decision making and (ii) they rely on a comprehensive understanding and analysis of the working mechanisms interacting in varying environments.

This Special Issue welcomes the submission of new perspectives, theories, and algorithms that tackle the challenging problems of performance prediction and fault diagnosis in electric motors and drives. The research areas of these submissions may include, but are not limited to, the following topics:

  • Data cleaning and data quality improvement;
  • Advanced modeling techniques;
  • Signal processing and feature extraction;
  • Condition monitoring and health assessment;
  • Data-driven intelligent fault diagnosis and performance prediction;
  • Edge computation for fault diagnosis;
  • Digital-twin-based diagnosis and prediction.

Prof. Dr. Jinglong Chen
Dr. Tongyang Pan
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. Machines is an international peer-reviewed open access monthly 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 2400 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
  • condition monitoring
  • signal processing
  • deep learning
  • electric motors and drives

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Related Special Issue

Published Papers (1 paper)

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Research

19 pages, 11838 KiB  
Article
A Hierarchical Attention-Guided Data–Knowledge Fusion Network for Few-Shot Gearboxes’ Fault Diagnosis
by Xin Feng and Tianci Zhang
Machines 2025, 13(6), 486; https://doi.org/10.3390/machines13060486 - 4 Jun 2025
Viewed by 382
Abstract
To address the limited generalization capability of data-driven fault diagnosis models caused by scarce gearbox fault samples in engineering practice, this paper proposes a hierarchical attention-guided data–knowledge dual-driven fusion network for intelligent fault diagnosis under few-shot conditions. Distinct from traditional single data-driven paradigms, [...] Read more.
To address the limited generalization capability of data-driven fault diagnosis models caused by scarce gearbox fault samples in engineering practice, this paper proposes a hierarchical attention-guided data–knowledge dual-driven fusion network for intelligent fault diagnosis under few-shot conditions. Distinct from traditional single data-driven paradigms, this method breaks through the constraints of limited samples through the synergy of prior knowledge and monitoring data. First, domain knowledge of gearbox fault diagnosis is utilized to construct prior features of monitoring data. Second, a deep convolutional neural network is designed to hierarchically capture abstract features from monitoring data. Subsequently, a hierarchical attention module is proposed to realize adaptive fusion of prior features and abstract features through hierarchical feature weight allocation, generating highly discriminative fused features for accurate gearbox fault identification. Experimental results on gearbox fault data demonstrate that the proposed method achieves 0.9880 recognition accuracy with less than 10% of the training samples, significantly outperforming purely data-driven models such as MGAN and CNET, thus verifying its superior generalization ability to train despite data scarcity. This approach establishes a novel data–knowledge dual-driven fusion paradigm for intelligent fault diagnosis of mechanical equipment under few-shot conditions. Full article
(This article belongs to the Special Issue Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis, 2nd Edition)
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