A Task-Based Few-Shot Learning Framework for Wind Turbine Gearbox Fault Diagnosis via Large Language Models
TL;DR
Inspired by few-shot learning, a support–query task construction strategy is introduced, reformulating classification as conditional task reasoning and a global coverage and local balance task sampling strategy is designed to enhance task diversity and mitigate sample imbalance.
Abstract
Wind turbine gearbox fault diagnosis is essential for ensuring the safe and stable operation of wind energy systems. However, in industrial scenarios, limited fault data and highly complex, non-stationary operating conditions lead to significant distribution shifts, which reduce the generalization ability of traditional methods. To address this issue, we propose a task-oriented fault diagnosis framework based on large language models. Time-domain and frequency-domain features are extracted from vibration signals to construct structured representations. Inspired by few-shot learning, a support–query task construction strategy is introduced, reformulating classification as conditional task reasoning. Low-Rank Adaptation is adopted for parameter-efficient fine-tuning of the pre-trained model. In addition, a global coverage and local balance task sampling strategy is designed to enhance task diversity and mitigate sample imbalance. Experiments on a wind turbine gearbox dataset show consistent performance across multiple backbone models, including LLaMA2-7B, LLaMA3-8B, Qwen-7B, and Baichuan-7B. LLaMA3-8B achieves the best performance, with 98.00% accuracy and an F1-score of 0.9798. These results demonstrate strong robustness and cross-model generalization under few-shot and complex operating conditions.