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| Bearing Fault Diagnosis Method Based on Histogram Equalization and Convolutional Neural Network |
| CHEN Jian1,2,SUN Tai-hua1,HUANG Kai-xuan1,KAN Dong1,CAO Kun-ming1,ZHANG Lei1,CHENG Ming1 |
1. Institute of Sound and Vibration Research, Hefei University of Technology, Hefei, Anhui 230009, China
2. Automotive NVH Engineering & Technology Research Center Anhui Province, Hefei, Anhui 230009, China |
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Abstract To solve the problem that traditional fault diagnosis methods of rolling bearing rely on artificial feature extraction and expert experience, and it is difficult to self-adapt to extract weak fault features of strong noise signals, an intelligent diagnosis method combining histogram equalization and convolutional neural network (CNN) is proposed. First, the one-dimensional vibration signal collected by the sensor is transformed into a two-dimensional vibration image that is easy to be recognized by the model through Transversal interpolation. Histogram equalization technology is used to stretch the dynamic range of gray value difference between pixels, highlight texture details and contrast, and enhance periodic fault characteristics. Then, a deep CNN model is constructed, and the optimization technology is used to reduce the model parameters, and the complex mapping relationship between monitoring data and fault state is learned layer by layer. The experimental results show that this method has a high accuracy of more than 99%, and still has a high identification accuracy and generalization ability for fault signals under different loads.
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Received: 31 December 2020
Published: 18 July 2022
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2022, Vol. 43 
