A novel intelligent multicross domain fault diagnosis of servo motor-bearing system based on Domain Generalized Graph Convolution Autoencoder

Abstract

The data measured by the servo motor-bearing system under complex working conditions will present problems such as amplitude fluctuations, unequal impact intervals, and significant differences in data distribution, and so forth. However, the most intelligent fault diagnosis focus on deep learning or transfer learning, which cannot complement knowledge transfer and generalized diagnosis with the structural neighbor relationship under unknown conditions or cross-machine samples. By utilizing Domain Generalized Graph Convolution Autoencoder (DGGCAE), a novel intelligent multicross domain fault diagnosis method for servo-motor bearing systems can be developed. Specifically, the Dirichlet Mixup and Distilled augmentations are first employed to augment the domain data of the feature and label layer for model training. Accordingly, graph representation learning on multisource domain data is mainly performed for the developed algorithm. Afterward, the graph convolutional autoencoder is employed to extract enough generalized high-dimensional features. Furthermore, DGGCAE’s classification loss and domain discrimination loss can be calculated to narrow the distribution gap among multisource domains. Finally, the fault simulation test bench (called servo motor-Cylindrical roller bearing system from Nanjing University of Science and Technology) has validated the development of the diagnostic method.