辽宁工程技术大学学报（自然科学版）2026，Vol.45Issue(3)：339-348,10.DOI:10.11956/j.issn.1008-0562.20250567
基于深度强化学习变分模态分解的风机叶片故障诊断方法
Blade fault diagnosis of wind turbines based on deep reinforcement learning adaptive variational mode decomposition
摘要
Abstract
To address the problem that wind turbine blades are prone to defects such as cracks and wear under complex operation conditions,and to overcome the limitations of traditional vibration signal analysis methods,this paper proposes a fault diagnosis method for wind turbine blades based on adaptive variational mode decomposition optimized by deep reinforcement learning.In this method,deep reinforcement learning is introduced to construct an adaptive optimization framework for variational mode decomposition parameters.The dynamic optimization of the mode number and penalty factor is realized through a discrete-continuous hybrid action space and a dual-objective reward function constrained by the average envelope entropy and the variance of envelope entropy.On this basis,sensitive modes are screened according to envelope entropy,the time-domain,frequency-domain and entropy features are fused,and the blade fault classification is completed using a support vector machine.The research results show that the proposed method can reduce the average envelope entropy of variational mode decomposition to 2.21,control its variance within the range of 0.21-0.24,and achieve a recognition accuracy of blade faults as high as 96.4％.The research conclusions provide a reference for improving the accuracy and reliability of fault diagnosis for wind turbine blades.关键词
风机叶片/故障诊断/深度强化学习/变分模态分解/近端策略优化算法/包络熵Key words
wind turbine blades/fault diagnosis/deep reinforcement learning/variational mode decomposition/proximal policy optimization algorithm/envelope entropy分类
能源科技引用本文复制引用
王洪江,张楠,任娜,张天,刘金圣,刘振宇..基于深度强化学习变分模态分解的风机叶片故障诊断方法[J].辽宁工程技术大学学报（自然科学版）,2026,45(3):339-348,10.基金项目
辽宁省科技厅重点研发项目(2024JH2/102500074) （2024JH2/102500074）
