Tianyang Wang

Wind turbine drivetrain fault diagnosis and intelligent O&M: a review

As wind turbines evolve toward larger capacities, fleet-level clustering, and operation under complex conditions, fault mechanisms in key drivetrain components show multi-physics coupling and complex evolution, creating a major bottleneck in condition monitoring: models are often constructible but hard to generalize. A...

Semantics-guided contrastive pre-training with cluster-aware decision making for zero-shot bearing fault diagnosis

Fault diagnosis contrastive language-signal pre-training (FD-CLSP) is proposed, a zero-shot cross-domain framework for bearing fault diagnosis under unseen operating conditions that leverages the noise-robustness and domain-invariance of high-level semantics to guide the extraction of elusive physical features.

A federated class-incremental learning framework with dynamic client participation and evolution for machine fault diagnosis