A New Structured Domain Adversarial Neural Network for Transfer Fault Diagnosis of Rolling Bearings Under Different Working Conditions

Abstract

This article presents a new deep transfer learning method, named structured domain adversarial neural network (SDANN), for bearing fault diagnosis with the data collected under different working conditions. The key idea of this method is integrating the strong adaptability of domain adversarial neural network (DANN) and structured relatedness information among multiple failure modes to improve the effect of transfer learning. First, for fine-grained alignment between the data collected from different working conditions, a new loss function with a discriminative regularizer is designed for DANN by using maximum correlation entropy constraint. Second, to improve the stability of DANN on an insufficient amount of data, a relatedness matrix is introduced, and a new regularizer with symmetry constraint on this matrix is designed to capture the intrinsic similarity structure among multiple fault types. Finally, a stochastic gradient descent optimization strategy is used to train the network and establish an end-to-end diagnostic model. Comparative experiments are conducted on two widely used bearing data sets. The results show that the proposed method has good diagnosis performance on insufficient monitoring data and outperforms several state-of-the-art transfer diagnosis methods and deep learning-based diagnosis methods in terms of diagnostic accuracy and numerical stability.