Journal of Zhejiang University

Journal of Zhejiang University SCIENCE A

Accepted manuscript available online (unedited version)

A weak fault feature diagnosis method for electric drive systems based on a multi-source feature fusion selection mechanism

Author(s): Cong YUE, Jingwen KANG, Yonghao ZHAO, Ping CHENG
Affiliation(s): Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
Corresponding email(s): Cong YUE, yuecong234@163.com
Key Words: Particle Velocity Signal, Weak Fault Diagnosis, Multi-source Fusion Feature Selection Mechanism, Enhanced Feature Extraction

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Cong YUE, Jingwen KANG, Yonghao ZHAO, Ping CHENG. A weak fault feature diagnosis method for electric drive systems based on a multi-source feature fusion selection mechanism[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2600050

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author="Cong YUE, Jingwen KANG, Yonghao ZHAO, Ping CHENG",
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%T A weak fault feature diagnosis method for electric drive systems based on a multi-source feature fusion selection mechanism
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%A Jingwen KANG
%A Yonghao ZHAO
%A Ping CHENG
%J Journal of Zhejiang University SCIENCE A
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A1 - Yonghao ZHAO
A1 - Ping CHENG
J0 - Journal of Zhejiang University Science A
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Y1 - in press
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doi="https://doi.org/10.1631/jzus.A2600050"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Electric drive systems (EDS) feature high integration and strong nonlinearity, which renders weak fault identification a challenging task. Acoustic particle Velocity Signals employed for noncontact fault monitoring are prone to contamination by noise and redundant features. This contamination seriously interferes with weak fault extraction and reduces diagnostic stability. To overcome the limitations of conventional single or multiple feature selection strategies, this paper proposes an information entropy-based multisource feature fusion selection (IE-MSFS) method. The proposed method can effectively eliminate redundant information and enhance the characterization ability of weak fault features. Based on the EDS acoustic particle Velocity Signals collected in the laboratory, a comparative analysis with vibration signals is carried out on three typical weak fault types through machine-learning evaluation. The results verify that the CIR-PS scheme exhibits outstanding and stable weak fault recognition performance, with a diagnostic accuracy exceeding 95.2%. Further tests demonstrate that the established scheme has favorable robustness and generalization ability for EDS weak Fault Diagnosis.

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On-line Access: 2026-06-15

Received: 2026-01-23

Revision Accepted: 2026-05-25

Crosschecked: 0000-00-00

Cited: 0

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Citations: Bibtex RefMan EndNote GB/T7714

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