A weak fault feature diagnosis method for electric drive systems based on a multi-source feature fusion selection mechanism
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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, 1998, -1(-1): .
@article{title="A weak fault feature diagnosis method for electric drive systems based on a multi-source feature fusion selection mechanism",
author="Cong YUE, Jingwen KANG, Yonghao ZHAO, Ping CHENG",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2600050"
}
%0 Journal Article
%T A weak fault feature diagnosis method for electric drive systems based on a multi-source feature fusion selection mechanism
%A Cong YUE
%A Jingwen KANG
%A Yonghao ZHAO
%A Ping CHENG
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
%P
%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2600050
TY - JOUR
T1 - A weak fault feature diagnosis method for electric drive systems based on a multi-source feature fusion selection mechanism
A1 - Cong YUE
A1 - Jingwen KANG
A1 - Yonghao ZHAO
A1 - Ping CHENG
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP - 0
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2600050
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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