Rotating machinery fault diagnosis based on KSLPP and RWKNN

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (8) : 219-223.

Wang Xuedong Zhao Rongzhen Deng Linfeng

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Abstract

Aiming at the questions high dimension and low precision of the recognition for rotating machinery fault diagnosis, a intelligent fault diagnosis methods based on kernel supervised locality preserving projection and K nearest neighbor weighted by feature selection ReliefF algorithm(RWKNN) was proposed. KSLPP can effectively extract nonlinear information in original feature data set, at the same time make full use of class information in dimension reduction projection, make the sample minimize the dispersion within class, maximum the separation between classes. Then ,the sensitive low dimension feature data set fed into K nearest neighbor weighted by feature selection ReliefF algorithm to recognize the fault type. RWKNN can highlight the contribution rate of different features for classification, strengthen the sensitive characteristics, weaken the irrelevant features, improve the classification accuracy and robustness. At last, the validity of the proposed method was verified by the typical fault vibration signal of rotor.

Key words

fault diagnosis / kernel supervised locality preserving projection(KSLPP) / feature selection ReliefF / weighted K-nearest neighbor

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Wang Xuedong Zhao Rongzhen Deng Linfeng. Rotating machinery fault diagnosis based on KSLPP and RWKNN[J]. Journal of Vibration and Shock, 2016, 35(8): 219-223

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