Partial domain adaptation fault diagnosis method based on deep residual shrinkage network

Abstract

Transfer learning, leveraging the gaps through learned knowledge from source domain to target domain recognition, has achieved impressive performance in bearing fault diagnosis with two domains which have only weak distribution discrepancy. From different devices, collected vibration signal always suffered from big distribution discrepancy, which has limited the generalization of these existing transfer learning-based methods significantly. To overcome this challenge, the partial domain transfer learning methods are studied latest. However, most of these available techniques only focus on reducing the discrepancy of two domains using statistical distance metrics, which cannot consider the Riemannian manifold hidden in distribution space that impacted the accuracy of discrepancy measurement during testing. This paper proposes a Log-CORAL–based deep residual shrinkage network for partial domain adaptation bearing fault diagnosis scenario, where the domains data from different machinery are selected. Specifically, the log-correlation alignment (Log-CORAL), as a domain discrepancy metric, is explored to weaken the influence by Riemannian manifold, which disturbed the discrepancy measurement dependability. In addition, adversarial domain discriminator is embedded into deep residual shrinkage network to reduce the discrepancy between the two domains by maximizing the loss of domain discriminator. Comparison experiments with the SOTA methods on three well-known bearing datasets are conducted to verify effectiveness of the proposed method.