Xue-Yi Li

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.

Industrial Internet-Oriented Unsupervised Hydro-Turbine Bearing Fault Diagnosis via Prototype-Disentangled Conditional Wasserstein Domain Adaptation

With the rapid development of Industrial Internet-oriented smart energy systems, hydro-turbine generator units are increasingly monitored through networked sensors, industrial communication infrastructures, and edge/cloud-based condition-monitoring platforms. These Internet-connected monitoring environments provide abu...

Fault Diagnosis of Gearbox Bearings Under Extreme Class Imbalance Based on Multi-Resolution Windows and Density-Aware Cross-Modal Fusion

A dual-branch architecture with spatial density-weighted kernels is proposed to decouple high-frequency transients from low-frequency periodic trends and an imbalance-aware strategy integrating Focal Loss and composite augmentation is developed to mitigate model bias.

MAML-S3M: Selective state space meta-learning for cross-condition few-shot bearing fault diagnosis

A novel model-agnostic meta-learning framework based on a selective state space model (MAML-S3M) to address the challenge of cross-condition few-shot bearing fault diagnosis and achieves superior diagnostic accuracy, outperforming state-of-the-art methods by at least 1.1%.

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