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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 2026 | Viewed by 3434

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 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. 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 (4 papers)

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Research

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24 pages, 8487 KB  
Article
SCADA-Based Stator-Winding Prognostics: A Temperature- Weighted Work Index for Industrial Motor Health Monitoring
by Omar Khaled, Malek Rekik, Yingjie Tang and Matthew Albert Franchek
Machines 2026, 14(4), 425; https://doi.org/10.3390/machines14040425 - 11 Apr 2026
Viewed by 152
Abstract
Industrial predictive maintenance programs often rely on SCADA historian signals characterized by low-frequency sampling and asynchronous reporting intervals. These data constraints, specifically non-uniform scan rates and inter-tag time misalignment, limit the applicability of high-resolution or sensor-intensive prognostic models. This study proposes a lightweight, [...] Read more.
Industrial predictive maintenance programs often rely on SCADA historian signals characterized by low-frequency sampling and asynchronous reporting intervals. These data constraints, specifically non-uniform scan rates and inter-tag time misalignment, limit the applicability of high-resolution or sensor-intensive prognostic models. This study proposes a lightweight, physics-informed health proxy, the temperature-weighted work (TWW) index, designed to monitor motor stator-winding degradation within these industrial limitations. The TWW index accumulates mechanical work derived from torque and speed measurements, weighted by an adaptive exponential temperature-emphasis function that penalizes operation at elevated temperatures. The formulation is inspired by practical thermal-aging heuristics such as Montsinger’s rule in the qualitative sense that higher temperatures are treated as disproportionately more damaging, but it is not intended as a direct implementation of a fixed absolute-temperature life law. Instead, it is designed as a lightweight adaptive index suitable for online SCADA-based implementation. To address SCADA-specific irregularities, the framework incorporates data synchronization and resampling techniques to align heterogeneous tags, alongside power-thresholding to isolate degradation-relevant load periods. The resulting cumulative index is mapped to a normalized health/RUL proxy using failure-referenced thresholds identified from historical events. Validation using field data from industrial three-phase motors demonstrates that the TWW index provides a monotonic degradation profile that is consistent with documented winding-related failures and proactive removals. Case studies confirm that the model enabled proactive maintenance interventions by signaling the terminal phase of insulation life before catastrophic breakdown, offering a hardware-free and scalable solution for real-time asset management. Full article
(This article belongs to the Special Issue Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis, 2nd Edition)
27 pages, 3220 KB  
Article
A Novel Load-Dependent Multimodal Vibration Signal Enhancement and Fusion Framework (LD-MVSEFF) for Load-Specific Condition Monitoring
by Shahd Ziad Hejazi and Michael Packianather
Machines 2026, 14(4), 372; https://doi.org/10.3390/machines14040372 - 27 Mar 2026
Viewed by 396
Abstract
This paper presents a Load-Dependent Multimodal Vibration Signal Enhancement and Fusion Framework (LD-MVSEFF) for load-specific condition monitoring, building on the Customised Load Adaptive Framework (CLAF). The proposed approach enhances the classification of CLAF load-dependent subclasses, namely, Healthy, Mild, Moderate, and Severe, by integrating [...] Read more.
This paper presents a Load-Dependent Multimodal Vibration Signal Enhancement and Fusion Framework (LD-MVSEFF) for load-specific condition monitoring, building on the Customised Load Adaptive Framework (CLAF). The proposed approach enhances the classification of CLAF load-dependent subclasses, namely, Healthy, Mild, Moderate, and Severe, by integrating complementary information from raw vibration signals and encoded signal representations. Three input channels are employed, combining time–frequency domain features with Continuous Wavelet Transform (CWT) and Gramian Angular Difference Field (GADF) image encodings, with each channel independently trained and evaluated to identify its most effective classifiers. To address the reduced separability of the Mild and Moderate fault subclasses under varying load conditions, a weighted decision-fusion strategy is introduced, assigning classifier contributions according to their class-specific strengths. Experimental evaluation over five runs demonstrates high and stable performance, with the best configuration achieving an overall accuracy of 99.04% ± 0.22% and an average training time of 18 min and 30 s. The results confirm the effectiveness of LD-MVSEFF as a robust multimodal methodology for load-specific condition monitoring. 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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19 pages, 11838 KB  
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 1345
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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Review

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23 pages, 2515 KB  
Review
AI-Enabled End-of-Line Quality Control in Electric Motor Manufacturing: Methods, Challenges, and Future Directions
by Jernej Mlinarič and Gregor Dolanc
Machines 2026, 14(2), 149; https://doi.org/10.3390/machines14020149 - 28 Jan 2026
Viewed by 1031
Abstract
End-of-Line (EoL) quality control plays a crucial role in ensuring the reliability, safety, and performance of electric motors in modern industrial production. Increasing product complexity, tighter manufacturing tolerances, and rising production quantities have exposed the limitations of conventional EoL inspection systems, which rely [...] Read more.
End-of-Line (EoL) quality control plays a crucial role in ensuring the reliability, safety, and performance of electric motors in modern industrial production. Increasing product complexity, tighter manufacturing tolerances, and rising production quantities have exposed the limitations of conventional EoL inspection systems, which rely primarily on manually crafted features, expert-defined thresholds, and rule-based decision logic. In recent years, artificial intelligence (AI) techniques, including machine learning (ML), deep learning (DL), and transfer learning (TL), have emerged as promising solutions to overcome these limitations by enabling data-driven, adaptive, and scalable quality inspection. This paper presents a comprehensive and structured review of the latest advances in intelligent EoL quality inspection for electric motor production. It systematically surveys the non-invasive measurement techniques that are commonly employed in industrial environments and examines the evolution from traditional signal processing-based inspection to AI-based approaches. ML methods for feature selection and classification, DL models for raw signal-based fault detection, and TL strategies for data-efficient model adaptation are critically analyzed in terms of their effectiveness, robustness, interpretability, and industrial applicability. Furthermore, this work identifies key challenges that prevent the widespread adoption of AI-based EoL inspection systems, including dependence on expert knowledge, limited availability of labeled fault data, generalization between motor variants and production condition, and the lack of standardized evaluation methodologies. Based on the identified research gaps, this review outlines research directions and emerging concepts for developing robust, interpretable, and data-efficient intelligent inspection systems suitable for real-world manufacturing environments. By synthesizing recent advances and highlighting open challenges, this review aims to support researchers and experts in designing next-generation intelligent EoL quality control systems that enhance production efficiency, reduce operational costs, and improve product reliability. 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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