摘要
Fault diagnosis is vital in manufacturing system.However,the first step of the traditional fault diagnosis method is to process the signal,extract the features and then put the features into a selected classifier for classification.The process of feature extraction depends on the experimenters’experience,and the classification rate of the shallow diagnostic model does not achieve satisfactory results.In view of these problems,this paper proposes a method of converting raw signals into twodimensional images.This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process.And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network(CNN),which can automatically accomplish the process of the feature extraction and fault diagnosis.The effect of this method is verified by bearing data.The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed.The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.
Fault diagnosis is vital in manufacturing system.However,the first step of the traditional fault diagnosis method is to process the signal,extract the features and then put the features into a selected classifier for classification.The process of feature extraction depends on the experimenters’experience,and the classification rate of the shallow diagnostic model does not achieve satisfactory results.In view of these problems,this paper proposes a method of converting raw signals into twodimensional images.This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process.And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network (CNN),which can automatically accomplish the process of the feature extraction and fault diagnosis.The effect of this method is verified by bearing data.The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed.The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.
基金
co-supported by the National Natural Science Foundation of China(No.51775452)
Fundamental Research Funds for the Central Universities,China(Nos.2682019CX35 and 2018GF02)
Planning Project of Science&Technology Department of Sichuan Province,China(No.2019YFG0353).
