Transfer Learning Method Based on Adversarial Domain Adaption for Bearing Fault Diagnosis

Abstract

At present, most of the intelligent fault diagnosis methods of rolling element bearings require sufficient labeled data for training. However, collecting labeled data is usually expensive and time-consuming, and when the distribution of the test data is different from the distribution of the training data, the diagnostic performance will decrease. In order to solve the problem of unlabeled cross-domain diagnosis of bearings, this paper proposes an adversarial domain adaption method based on deep transfer learning. The short-time Fourier transform is used to transform the original data into a time-frequency image. The feature extractor is used to extract its deep features. The maximum mean discrepancy and domain confusion function are used for domain adaptation to extract domain-invariant features between two domains for cross-domain fault diagnosis. Experiments on two bearing datasets are carried out for validations. The results prove that the method in this paper is superior to other deep transfer learning methods. It shows the advantages of the improved method and can be used as an effective tool for cross-domain fault diagnosis.