Single-Source Cross-Domain Bearing Fault Diagnosis via Multipseudo-Domain-Augmented Adversarial Domain-Invariant Learning

Empowered by the large amounts of sensor data in the Industrial Internet of Things, data-driven fault diagnosis has a pivotal role in improving equipment reliability in harsh industrial environments. To enhance diagnostic performance under unknown operating conditions, transfer learning-based cross-domain fault diagnosis has been emerging. However, diagnostic models are prone to overfit to the source domain due to the lack of sample diversity when only a single-source domain is available. Moreover, significant domain shifts between the single-source domain and multiple unknown target domains may degrade the generalization performance on the unknown domains. To address these challenges, we propose a multipseudo domains augmented adversarial domain-invariant learning (MDA-AD) for cross-domain fault diagnosis. First, we design a multipseudo domain generator, where interdomain diversity constraints and manifold-semantic consistency constraints are implemented to avoid overfitting on the source domain by generating diverse and representative pseudo samples. Subsequently, to alleviate the domain shift, we design an adversarial domain-aware classifier that extracts domain-invariant features by introducing an adversarial paradigm between a feature extractor and a domain discriminator. Finally, to further enhance the diversity of the pseudo domains, we implement a diversity-consistency constrained domain-invariant training strategy. The experimental results, obtained through comparative studies, hyperparameter influence analysis, and visualization on two bearing data sets, affirm the superior diagnostic performance of MDA-AD in a single-source domain.