Dynamic Balanced Domain-Adversarial Networks for Cross-Domain Fault Diagnosis of Train Bearings

Abstract

Confronting with the problems of inconsistent data distributions, noise interference, and class imbalance in fault diagnosis of train bearings under variable working conditions, this paper proposes a cross-domain fault diagnosis model called dynamic balanced domain-adversarial networks (DBDAN). The major contributions of the proposed DBDAN are that, a novel frequency band attention module (FBAM) with a clear physical interpretation is embedded in the feature extraction of the DBDAN to alleviate the noise interference, and an adaptive class-level weighting strategy is designed for the imbalanced dataset to tackle the class imbalance problem. Specifically, the FBAM highlights the time-frequency contents in the frequency bands containing state information by adaptively learning the time-frequency characteristics of bearing vibration signals. Then, high-level representations are extracted by the following feature extractor, which are used for fault recognition by the label classifier, and alignments of marginal and conditional distributions across domains by the domain discriminator. The adaptive class-level weighting strategy is used to dynamically balance the distribution alignments between the imbalanced classes. The DBDAN model employs Wasserstein distance to measure the distribution discrepancy, and is trained in an adversarial way. The experiment results of cross-domain fault diagnosis of a common rolling bearing and a train axle box bearing under variable working conditions verify the superiority of the proposed method over other domain-adversarial methods in terms of diagnostic accuracy, convergence speed and stability.