DC-NNMN: Across Components Fault Diagnosis Based on Deep Few-Shot Learning

Abstract

In recent years, deep learning has become a popular topic in the intelligent fault diagnosis of industrial equipment. In practical working conditions, how to realize intelligent fault diagnosis in the case of the different mechanical components with a tiny labeled sample is a challenging problem. That means training with one component sample but testing with another component sample has not been resolved. In this paper, we propose a deep convolutional nearest neighbor matching network (DC-NNMN) based on few-shot learning. The 1D convolution embedding network is constructed to extract the high-dimensional fault feature. The cosine distance is merged into the K-Nearest Neighbor method to model the distance distribution between the unlabeled sample from the query set and labeled sample from the support set in high-dimensional fault features. The multiple few-shot learning fault diagnosis tasks as the testing dataset are constructed, and then the network parameters are optimized through training in multiple tasks. Thus, a robust network model is obtained to classify the unknown fault categories in different components with tiny labeled fault samples. We use the CWRU bearing vibration dataset, the bearing vibration data selected from the Lab-built experimental platform, and another gearing vibration dataset for across components experiment to prove the proposed method. Experimental results show that the proposed method can achieve fault diagnosis accuracy of 82.19% for gearing and 82.63% for bearings with only one sample of each fault category. The proposed DC-NNMN model provides a new approach to solve the across components fault diagnosis in few-shot learning.