Partial transfer learning method based on MDWCAN for rolling bearing fault diagnosis under noisy conditions

Abstract

Domain adaptation shows excellent diagnostic performance for transfer bearing fault diagnosis (FD) in noise-free scenarios, where the training and test data label spaces are the same. However, in actual engineering, collecting test data for all health states is difficult and the vibration signal is susceptible to noise interference, which limits its application in real industry. Therefore, how to effectively source domain outlier samples and suppress irrelevant noise poses a challenge to the effectiveness of existing FD methods based on domain adaptation. To address the above issues, a novel transfer learning model named multi-scale denoising weighted conditional adversarial network (MDWCAN) is developed for partial transfer FD problem under noisy conditions. Firstly, a global multimodal denoising module is proposed to form a new feature extractor with multi-scale convolutional modules, which can enhance important features and weaken interference features. Secondly, a new loss function is proposed, in which the weight function is introduced into the conditional adversarial function to suppress the negative influence of source domain outlier samples. Finally, experimental results on two bearing datasets show that MDWCAN outperforms other methods.