FAULT DIAGNOSIS MODEL BASED ON LEAST SQUARE SUPPORT VECTOR MACHINE OPTIMIZED BY LEAVE-ONE-OUT CROSS-VALIDATION

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Journal of Vibration and Shock ›› 2010, Vol. 29 ›› Issue (9) : 170-174.

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FAULT DIAGNOSIS MODEL BASED ON LEAST SQUARE SUPPORT VECTOR MACHINE OPTIMIZED BY LEAVE-ONE-OUT CROSS-VALIDATION

LI Feng；TANG Bao-Ping；ZHANG Guo-Wen

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Abstract

A new fault diagnosis model is proposed based on Least square support vector machine(LS-SVM) optimized by Leave-one-out cross-validation: firstly, fault vibration signals are decomposed into several stationary IMFs, then the instantaneous amplitude L2-norms of the IMFs which have fault modulation characteristics are computed and regarded as the input characteristic vector of lin-kernel LS-SVM optimized by Leave-one-out cross-validation for fault classification. EMD decomposition adaptively isolates fault modulation signals from original signals. The differences among instantaneous amplitude L2-norm vectors reflect different fault type. Adopting Leave-one-out cross-validation to optimize punish parameter can not only enhance fault prediction accuracy of LS-SVM which adopts lin-kernel, but also improve adaptive diagnosis capacity of LS-SVM. The deep groove ball bearings fault diagnosis example proves the effectivity of this new model.

Key words

Instantaneous amplitude L2-norm / Least square support vector machine / Leave-one-out cross-validation / Parameter optimization / Fault diagnosis

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LI Feng;TANG Bao-Ping;ZHANG Guo-Wen. FAULT DIAGNOSIS MODEL BASED ON LEAST SQUARE SUPPORT VECTOR MACHINE OPTIMIZED BY LEAVE-ONE-OUT CROSS-VALIDATION[J]. Journal of Vibration and Shock, 2010, 29(9): 170-174