Imbalanced Sample Fault Diagnosis of Rolling Bearing Using Deep Condition Multidomain Generative Adversarial Network

Abstract

Rolling bearing fault diagnosis plays a crucial role in ensuring the safety and reliability of rotary machine, and some methods that employ deep-learning technology are grounded on the assumption that there are large-scale failure data. In reality, it is difficult to acquire more failure rolling bearing data under the different failure rates. In this article, a novel condition multidomain generative adversarial network (CMDGAN) is introduced for imbalanced sample fault diagnosis. This framework effectively captures the sample distribution information by a fusion of two-domain information when there are limited raw samples. Also, the introduced self-adaptive sample condition is no prior knowledge needed and contributes to the different state of synthetic data. Finally, an improved fault diagnostic model with the self-attention module implements the fusion of local fault feature and global periodic fault feature. Multiple sets of experiments on the Case Western Reserve University (CWRU) and Dalian University of Technology Vibration Lab (DUT-VL) datasets reveal the efficiency of our proposed approach, and the generating samples improve the fault diagnosis performance, which outperforms the contrasting methods.