Bearing fault identification by using deep convolution neural networks based on CNN-SVM

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Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (18) : 173-178.

JING Yunjian1,2, HU Xiaoyi2, SONG Zhikun1, HOU Yinqing 1

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Abstract

Considering that the traditional intelligent diagnosis methods rely too much on the signal processing and expert experience to extract fault features and are of poor model generalization ability, based on the deep learning theory, a deep convolution neural network algorithm combined with SVM classifier was proposed to build an improved fault diagnosis model for rolling bearings.Starting from the original measured bearing vibration signals, the model learns from each layer to achieve feature extraction and fault recognition, and introduces the batch normalization, Dropout processing and improved model classifier to improve the bearing fault recognition accuracy, model convergence speed and generalization ability.The experimental results show that the optimized deep learning model can quickly and accurately extract the characteristics of bearing faults.99% recognition accuracy can be achieved for bearings of different types and degrees of damages, and the model has strong generalization ability and enhanced learning ability.

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convolutional neural network / support vector machines / vibration signal / fault identification

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JING Yunjian1,2, HU Xiaoyi2, SONG Zhikun1, HOU Yinqing 1. Bearing fault identification by using deep convolution neural networks based on CNN-SVM[J]. Journal of Vibration and Shock, 2019, 38(18): 173-178

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