电子科技大学学报2025，Vol.54Issue(6)：924-934,11.DOI:10.12178/1001-0548.2024200
基于Shapley值的可解释AI在风机齿轮箱健康监测与故障定位中的应用
Application of shapley-value based explainable AI in health monitoring and fault localization for wind turbine gearboxes
摘要
Abstract
In the field of wind power generation,the health status of wind turbine gearboxes directly impacts the power output of wind turbine units.Current gearbox fault diagnosis and localization techniques,which are based on domain knowledge and data-driven approaches,are constrained by the completeness of domain knowledge,insufficient data volume,and lack of algorithm transparency.To address this issue,we propose an explainable AI framework that possesses both learning capabilities and provides interpretable outputs.By incorporating the Shapley value analysis method into unsupervised and supervised learning algorithms,the framework achieves improvements,alleviating the model's excessive dependence on data volume and enhancing the model's interpretability.The effectiveness of the proposed framework was validated through experiments on two typical wind turbine gearbox cases.The results of case 1 indicate that,compared to unsupervised and supervised learning algorithms,the proposed framework significantly improves clustering performance in situations with scarce data labels.The results of case 2 demonstrate that the framework,through model interpretability analysis,achieves the localization of wind turbine gearbox fault causes,providing guiding suggestions for gearbox fault prevention and maintenance.The experimental results showcase the significant effectiveness of the'knowledge+data'integration approach in engineering applications,offering valuable references for the practical implementation of explainable artificial intelligence.关键词
风力发电/齿轮箱故障/可解释AI(XAI)/Shapley值/聚类Key words
wind power generation/gearbox fault/explainable AI(XAI)/Shapley value/clustering分类
信息技术与安全科学引用本文复制引用
陶冠宏,张婉渝,许文波,范振军..基于Shapley值的可解释AI在风机齿轮箱健康监测与故障定位中的应用[J].电子科技大学学报,2025,54(6):924-934,11.基金项目
中电天奥产业发展基金(202201090404) （202201090404）
