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.
Fault Diagnosis method based on Sensitive Feature Selection and Manifold learning Dimension reduction
Su Zuqiang, Tang Baoping, Yao Jinbao
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.
fault diagnosis / feature selection / improved kernel distance measurement / linear local tangent space alignment / weighted k nearest neighbor classifier {{custom_keyword}} /
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