An adaptive few-shot fault diagnosis method based on virtual samples generated by fault characteristics of rotating machines

Abstract

The existing intelligent diagnostic methods based on the machine learning achieve good diagnostic results under the condition of large amount of the failure data available. However, in most cases, the diagnosis model is difficult to be constructed under the few-shot problem, which only normal data of the equipment is available. Therefore, a novel Adaptive Diagnosis with Fault Characteristics (ADFC) method, which builds the diagnosis model by generating personalized virtual fault samples, is proposed in the paper. The fault common characteristics are obtained based on the failure mechanism and the law of transmission paths, and the personalized fault virtual samples are generated by combining health state data contain the individual characteristics of the equipment. Then, the frequency domain features reflecting the operating status of the equipment are extracted as training samples. Finally, the fault diagnosis model based on the Convolutional Neural Network (CNN) is constructed for the equipment. The ADFC method is validated by the rotating machinery data, including the public data, the laboratory data and the field application data. The results indicate that the ADFC method achieved an average diagnostic accuracy of 92.16%, and the accuracy has been improved by at least 0.78% compared to the comparison method.