A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing.

ABSTRACT: Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-S

SUBMITTER: Wu C 

PROVIDER: S-EPMC7924884 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature