Motor BearingFault Identification Based on Wavelet Singular Entropy and SOFM Neural Network

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (10) : 217-223.

HE Yan-song1,2 HUANG Yi2 XU Zhong-ming1,2ZHANG Zhi-fei2

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Abstract

A new modeling method combining wavelet singular entropy andSelf Organizing Feature Map(SOFM) neural network is proposed to identify the motor bearing faults.The end position of the faulty bearing can be identifiedby computing and comparing the wavelet singular entropies of the fault vibration signals collected at the drive and fan ends of the motor firstly.Then the SOFM neural networkmodel using the bottom node energies of the fault signals decomposed by wavelet packet as input feature vectorsis built to identify the pitting corrosiondamage locationin the faulty bearing.The faulty bearing end positionand its internal pitting corrosion location can be identified jointly by the combination of wavelet singular entropy and SOFM neural network.Through the modeling and identification to the bearings damaged by pitting corrosionatinner,outer raceway and rolling element respectively,the results show that this model can identify the faulty bearing end position and its internal pitting corrosiondamage locationeffectively;this model has a more accurate identification ability and is more robust than that of the traditional support vector machine and the BP neural networkidentification model,and is more suitable for fault identification of such a multi classifica-tion problem.

Key words

wavelet packet decomposition / wavlet singular entropy / self organizing feature map / fault identification

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HE Yan-song1,2 HUANG Yi2 XU Zhong-ming1,2ZHANG Zhi-fei2. Motor BearingFault Identification Based on Wavelet Singular Entropy and SOFM Neural Network[J]. Journal of Vibration and Shock, 2017, 36(10): 217-223

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