Machinery fault diagnosis based on shift invariant CNN
ZHU Huijie1,2,3,WANG Xinqing4, RUI Ting4, ZHANG Yubao2, LI Yanfeng5
1. Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035,China;
2. The First Engineering Scientific Research Institute, Wuxi 214035, China;
3. Automation Department, Shanghai Jiao Tong University, Shanghai 200240, China;
4. College of Field Engineering, PLA Army Engineering University, Nanjing 210007, China;
5. Automobile NCO Academy, Army Military Transportation University, Bengbu 230001, China
Abstract：Traditional machinery fault diagnostic method usually requires multiple processing steps including signal preprocessing, feature extraction, feature selection and pattern recognition to lead to complicated process and poor universal application. Convolutional neural network (CNN) is a deep neural network with strong self-learning and anti-interference abilities. To simplify steps and improve efficiency, here, a novel machinery fault diagnosis method based on CNN was proposed directly utilizing raw data measured with sensors to identify faults. Due to features of mechanical vibration signals having a typical time shift property, CNN needed a lot of data to self-learn this property. Combining fault signals’ impact feature and shortcomings of CNN, the strategies of weight summing and large-scale maximum value pooling were proposed to effectively solve CNN’s shift invariance to enhance small samples’ generalization ability. Through diagnosing single point fault and multi-point faults of bearings, the effectiveness of the shift invariant CNN was verified. Compared with traditional fault diagnosis approaches and other machine learning algorithms, it was shown that the shift invariant CNN not only has a higher accuracy but also is simple to use, and it provides a new way for fault diagnosis.
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