Rotating machinery fault diagnosis based on feature extraction via an unsupervised graph neural network

Abstract

Fault diagnosis is an essential process for the health maintenance of rotating machinery. With the development of AI technology, many deep learning-based methods have been applied to fault diagnosis to enhance the intelligence level of equipment maintenance. Such methods normally need a large amount of labeled data for model training. However, label acquisition is a difficult task that requires extensive human labor. To address these issues, a fault diagnosis method based on feature extraction via an unsupervised graph neural network is proposed in this paper. In the proposed method, the K-nearest neighbor approach is adopted to construct a fault graph from the collected signals, thereby providing extra relationship information for fine feature mining. Then, the GraphSAGE model is trained on the constructed graph in an unsupervised way, that is, it does not need labeled data, to extract features of each signal sample. Based on the extracted features, some traditional classifiers are adopted to identify the fault types. The proposed model is evaluated on a rolling bearing dataset provided by the University of Paderborn and a motor rotor dataset collected by a constructed motor rotor system. Compared with some traditional deep learning-based fault diagnosis methods, the proposed model can achieve more accurate diagnoses even when there are only a few labeled samples.