A novel hybrid distance guided domain adversarial method for cross domain fault diagnosis of gearbox

Abstract

Abstract Distance-based domain adaptation methods have received extensive application in the transfer learning field. Different domain distances have different characteristics due to various data processing principles. Therefore, choosing appropriate domain distance can accomplish transfer tasks more efficiently. Domain adversarial neural networks can extract domain invariant features through game confrontation, but it is not capable of extracting hidden features of gear under speed fluctuations, and only using the adversarial mechanism for domain feature alignment is prone to gradient collapse. To solve the above problems, a novel hybrid distance guided domain adversarial fault diagnosis method of gear is proposed. First, stacked sparse autoencoders is employed in the model to extract the hidden features from the domain data, and the extracted features are input into the corresponding feature classifier and domain discriminator. Then, a mixture of maximum mean discrepancy (MMD) and Wasserstein distance is utilized to reduce the distribution difference. Finally, the domain adversarial mechanism is used to conduct adversarial training for feature alignment. Through two verification experiments of planetary gearboxes, it is verified that the proposed a Wasserstein and MMD distance guided Domain Adversarial model has excellent fault diagnosis performance under gear fluctuating conditions. In addition, the model has higher prediction accuracy and better fault feature extraction ability compared with other methods.