Cross-domain fault diagnosis network based on attributes and features transfer with dual classifier under limited and unbalanced datasets

Abstract

Abstract Deep learning (DL)-based methods have demonstrated significant success in fault diagnosis owing to their robust feature extraction and non-linear fitting capabilities. Meanwhile, their remarkable performance is accompanied by constant operating conditions and sufficient monitoring data. However, in real engineering environments, variable working conditions or limited and unbalanced data are common, which can widen the gap between fault diagnosis methods and real industrial applications. In this paper, we proposed a cross-domain fault diagnosis network based on a dual classifier (CFDNet) with input being limited and unbalanced data to learn attributes and features for unsupervised domain adaptation. We found that the diagnostic performance is commonly bounded by the underlying knowledge, especially feature extraction from original data. Therefore, we designed a new feature encoder with features and relationships, i.e. using a convolutional neural network and graph convolutional network, which improves extraction efficiency while retaining valuable information. Then, we discovered that enforced feature transfer can lead to negative transfer. To mitigate this, we present a feature and attribute transfer framework, which not only achieves features transfer but also enables attributes transfer. Furthermore, it was noted that limited and unbalanced datasets can introduce label bias and lead to biased model training. Hence, we designed dual classifiers to improve the probability of high-confidence final prediction by synthesizing diagnostic results. Comprehensive experiments conducted on three case studies demonstrate the effectiveness and superiority of our method for cross-domain fault diagnosis under limited and unbalanced datasets, which outperforms state-of-the-art methods in this study.