CLC number: TN912.3
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 2
Clicked: 5112
JIAO Wei-dong, YANG Shi-xi, Wu Zhao-tong. Extracting invariable fault features of rotating machines with multi-ICA networks[J]. Journal of Zhejiang University Science A, 2003, 4(5): 595-601.
@article{title="Extracting invariable fault features of rotating machines with multi-ICA networks",
author="JIAO Wei-dong, YANG Shi-xi, Wu Zhao-tong",
journal="Journal of Zhejiang University Science A",
volume="4",
number="5",
pages="595-601",
year="2003",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2003.0595"
}
%0 Journal Article
%T Extracting invariable fault features of rotating machines with multi-ICA networks
%A JIAO Wei-dong
%A YANG Shi-xi
%A Wu Zhao-tong
%J Journal of Zhejiang University SCIENCE A
%V 4
%N 5
%P 595-601
%@ 1869-1951
%D 2003
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2003.0595
TY - JOUR
T1 - Extracting invariable fault features of rotating machines with multi-ICA networks
A1 - JIAO Wei-dong
A1 - YANG Shi-xi
A1 - Wu Zhao-tong
J0 - Journal of Zhejiang University Science A
VL - 4
IS - 5
SP - 595
EP - 601
%@ 1869-1951
Y1 - 2003
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2003.0595
Abstract: This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault modes. By the use of ICA, invariable features embedded in multi-channel vibration measurements under different operating conditions (rotating speed and/or load) can be captured together. Thus, stable MLP classifiers insensitive to the variation of operation conditions are constructed. The successful results achieved by selected experiments indicate great potential of ICA in health condition monitoring of rotating machines.
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