Fault Diagnosis method based on Sensitive Feature Selection and Manifold learning Dimension reduction

Su Zuqiang;Tang Baoping;Yao Jinbao

Journal of Vibration and Shock ›› 2014, Vol. 33 ›› Issue (3) : 70-75.

PDF(1487 KB)
PDF(1487 KB)
Journal of Vibration and Shock ›› 2014, Vol. 33 ›› Issue (3) : 70-75.
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Fault Diagnosis method based on Sensitive Feature Selection and Manifold learning Dimension reduction

  • Su Zuqiang, Tang Baoping, Yao Jinbao

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Abstract

Fault diagnosis method based on feature selection (FS) and linear local tangent space alignment (LLTSA) is proposed, aiming to solve the problem of non-sensitive features and the high dimension of the feature set. An improved kernel distance measurement feature selection method (IKMD-FS) is proposed, which considers both the distance between classes and the dispersion within class, and the selected sensitive features are weighted by their sensitive-values. The weighted sensitive feature subset is compressed through LLTSA to reduce dimension and get the compressed more sensitive feature subset. Then, the feature subset is fed into weighted k nearest neighbor classifier (WKNNC), whose recognition accuracy is more stable compared with k nearest neighbor classification (KNNC), to recognize the fault type. At last, the validity of the proposed method is verified by the instance of the fault diagnosis of a rolling bearing.



Key words

fault diagnosis / feature selection / improved kernel distance measurement / linear local tangent space alignment / weighted k nearest neighbor classifier

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Su Zuqiang;Tang Baoping;Yao Jinbao. Fault Diagnosis method based on Sensitive Feature Selection and Manifold learning Dimension reduction [J]. Journal of Vibration and Shock, 2014, 33(3): 70-75
PDF(1487 KB)

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