Eccentricity Fault Diagnosis System in Three-Phase Permanent Magnet Synchronous Motor (PMSM) Based on the Deep Learning Approach

Chu, Kenny Sau Kang; Chew, Kuew Wai; Chang, Yoong Choon; Morris, Stella; Hoon, Yap; Chen, Chen

doi:10.3390/s25247416

This is an early access version, the complete PDF, HTML, and XML versions will be available soon.

Open AccessArticle

Eccentricity Fault Diagnosis System in Three-Phase Permanent Magnet Synchronous Motor (PMSM) Based on the Deep Learning Approach

by

Kenny Sau Kang Chu

^1,†

,

Kuew Wai Chew

^1,*,†

,

Yoong Choon Chang

¹

,

Stella Morris

¹

,

Yap Hoon

¹

and

Chen Chen

^2,3

¹

Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43200, Malaysia

²

School of Telecommunications Engineering, Xidian University, Xi’an 710071, China

³

Xidian Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Sensors 2025, 25(24), 7416; https://doi.org/10.3390/s25247416 (registering DOI)

Submission received: 5 November 2025 / Revised: 26 November 2025 / Accepted: 3 December 2025 / Published: 5 December 2025

(This article belongs to the Special Issue Sensor Data-Driven Fault Diagnosis Techniques)

Download Versions Notes

Abstract

Motor eccentricity faults, stemming from the misalignment of the rotor’s center and pivot point, lead to significant vibrations and noise, compromising motor reliability. This study emphasizes the need for an efficient diagnostic system to enable early detection and correction of these faults. Our research proposes a novel Eccentricity Fault Diagnosis Network (E-FDNet), designed for integration into a Motor Eccentricity Fault Diagnosis System (MEFDS), utilizing neural networks for detection. Evaluation tests reveal that a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture is ideal as the internal neural network within the E-FDNet. Key contributions of this research include (1) E-FDNet that stabilizes transition predictions among SEF/DEF/MEF; (2) a steady-state characteristic normalization (SSCN) improving feature consistency under dynamic responses; (3) an integrated physics–FEM–experiment pipeline for controlled analysis and validation; (4) approximately 98.86% accuracy/F1 outperforming classical and deep baselines; and (5) a non-invasive, current-only sensing design suited for deployment.

Keywords: motor eccentricity fault; fault diagnosis system; neural network

Share and Cite

MDPI and ACS Style

Chu, K.S.K.; Chew, K.W.; Chang, Y.C.; Morris, S.; Hoon, Y.; Chen, C. Eccentricity Fault Diagnosis System in Three-Phase Permanent Magnet Synchronous Motor (PMSM) Based on the Deep Learning Approach. Sensors 2025, 25, 7416. https://doi.org/10.3390/s25247416

AMA Style

Chu KSK, Chew KW, Chang YC, Morris S, Hoon Y, Chen C. Eccentricity Fault Diagnosis System in Three-Phase Permanent Magnet Synchronous Motor (PMSM) Based on the Deep Learning Approach. Sensors. 2025; 25(24):7416. https://doi.org/10.3390/s25247416

Chicago/Turabian Style

Chu, Kenny Sau Kang, Kuew Wai Chew, Yoong Choon Chang, Stella Morris, Yap Hoon, and Chen Chen. 2025. "Eccentricity Fault Diagnosis System in Three-Phase Permanent Magnet Synchronous Motor (PMSM) Based on the Deep Learning Approach" Sensors 25, no. 24: 7416. https://doi.org/10.3390/s25247416

APA Style

Chu, K. S. K., Chew, K. W., Chang, Y. C., Morris, S., Hoon, Y., & Chen, C. (2025). Eccentricity Fault Diagnosis System in Three-Phase Permanent Magnet Synchronous Motor (PMSM) Based on the Deep Learning Approach. Sensors, 25(24), 7416. https://doi.org/10.3390/s25247416

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.

Article Menu

Eccentricity Fault Diagnosis System in Three-Phase Permanent Magnet Synchronous Motor (PMSM) Based on the Deep Learning Approach

Abstract

Share and Cite

Article Metrics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI