Semantic knowledge transfer framework for wind turbine gearbox compound fault diagnosis via zero-shot learning

Abstract

Compound fault diagnosis of wind turbine gearboxes (WTGs) has received extensive attention. Existing deep learning-based methods typically require sufficient compound fault samples for model training. However, collecting such samples is extremely difficult and often impractical in real-world industrial scenarios. Inspired by zero-shot learning, this paper proposes a semantic knowledge transfer framework to diagnose compound faults using only single-fault data for training. Within this framework, a semantic knowledge library is first constructed to encode human expert intelligence into high-fidelity knowledge vectors, establishing a shared semantic space for all fault classes. To ensure signal representations align with these expert semantics, a time-frequency informative perceptron is introduced to capture comprehensive fault signatures by simultaneously capturing discriminative features from both time and frequency domains. Finally, imbalance-robust knowledge learners are designed to bridge the gap between physical features and knowledge labels while mitigating the inherent class imbalance effects. The proposed framework is validated on a self-built WTG compound fault test platform. Experimental results showcase its exceptional effectiveness and superiority in recognizing unseen compound faults, providing a robust solution for mechanical fault diagnosis under zero-sample scenarios.