电力系统保护与控制2025，Vol.53Issue(10)：117-129,13.DOI:10.19783/j.cnki.pspc.240967
基于RLMTS的小样本风力发电机齿轮箱故障检测
Fault detection of small-sample wind turbine gearboxes based on RLMTS
摘要
Abstract
To address issues such as overfitting and poor generalization caused by small-sample data in wind turbine gearbox fault detection,this paper proposes a fault detection model based on the reinforcement learning Mahalanobis-Taguchi system(RLMTS).First,features filtered by orthogonal array and signal-to-noise ratio analysis are used to construct the initial Mahalanobis space.Then,reinforcement learning and predefined rules are used to explore and optimize this space.Finally,an antlion optimizer is employed to improve the threshold determination of the traditional Mahalanobis Taguchi system.Experimental results show that RLMTS is effective for fault detection in different small-sample scenarios.Compared with 17 other methods,RLMTS has better diagnostic performance,greater robustness,and broader applicability,making it particularly suitable for small-sample wind turbine gearbox fault detection.It is conducive to improving the reliability,efficiency and safety of the gearbox operation,while also reducing maintenance cost and ensuring stable and high efficiency wind power generation.关键词
风电机齿轮箱/故障检测/小样本/马田系统/强化学习/蚁狮优化器Key words
wind turbine gearbox/fault detection/small samples/Mahalanobis Taguchi system/reinforcement learning/antlion optimizer引用本文复制引用
茅婷,程龙生,张月义,胡静..基于RLMTS的小样本风力发电机齿轮箱故障检测[J].电力系统保护与控制,2025,53(10):117-129,13.基金项目
This work is supported by the National Social Science Foundation of China(No.23BGL079). 国家社会科学基金项目资助(23BGL079) （No.23BGL079）
国家留学基金管理委员会项目资助(202406840086) （202406840086）
江苏省研究生科研与实践创新计划项目资助(KYCX23_0532) （KYCX23_0532）
