Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis

Abstract

Intelligent fault diagnosis based on deep learning (DL) has been widely used in various engineering practices. However, when confronting massive unlabeled industrial data, traditional data-driven intelligent fault diagnosis approaches cannot fully mine the correlation geometric structure information between data, and therefore cannot obtain good fault diagnosis results. To overcome this difficulty, an efficient fault diagnosis method based on deep hypergraph autoencoder embedding (DHAEE) is presented in this study. First, unlabeled vibration signals are converted into hypergraphs by applying the designed hypergraph construction method. Second, a hypergraph convolutional extreme learning machine autoencoder (HCELM-AE) is designed, which can mine the higher-order structural information and subspace structural information of the original unlabeled data by designing hypergraph convolutional and self-representation layers. Furthermore, by stacking multiple HCELM-AE modules in a DL framework, the DHAEE and its corresponding fault diagnosis method is constructed, which not only has the advantage of high computational efficiency of ELM-AE, but also has strong representational learning ability of DL methods. Finally, the effectiveness of the DHAEE based fault diagnosis method is verified by rolling bearing fault data and rotor fault data. Experimental results show that the presented fault diagnosis method has higher accuracy and lower computational complexity than other comparison methods, thus proving that DHAEE is an efficient intelligent massive unlabeled data processing approach for rotating machinery fault diagnosis.