Single Source Cross-Domain Bearing Fault Diagnosis via Multi-Pseudo 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 Multi-pseudo Domains Augmented Adversarial Domain-invariant learning (MDA-AD) for cross-domain fault diagnosis. First, we design a multi-pseudo domain generator, where inter-domain 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 datasets, affirm the superior diagnostic performance of MDA-AD in a single source domain.