机电工程技术2025，Vol.54Issue(5)：134-138,147,6.DOI:10.3969/j.issn.1009-9492.2024.00105
基于小波分析与迁移学习的风电机组关键部件故障诊断系统研究
Research on Fault Diagnosis System of Wind Turbine Key Components Based on Wavelet Analysis and Transfer Learning
摘要
Abstract
In order to further improve the fault diagnosis accuracy of the key components of wind turbine in the variable working condition scenario,and to meet the practical application requirements of industrial field diagnosis.A signal-image conversion method is firstly designed to analyze and process the vibration signals generated by rotating parts in time-frequency analysis using continuous wavelet transform.The low-frequency fault frequency and high-frequency impulse frequency distribution characteristics of the signal are studied,and the image fusion method is used to construct the CWT time-frequency feature map,which fully expresses the key fault feature information contained in the vibration signal.Secondly,in view of the problem of decreasing fault diagnostic accuracy in the scenario of variable operating conditions,the migratory fault diagnostic model based on the ConvNeXt and DJP-MMD is designed to realize the adaptive extraction of invariant features of the depth domain of the rotating component's operating state,which is the best solution to the problem.The experimental results show that the designed fault transfer diagnosis method has good diagnostic effect.The average accuracy reaches 90.7%.Finally,a set of wind turbine key component diagnosis application system based on the Internet of Things architecture is designed to realize the functions of wind turbine multi-source sensing information collection,remote diagnosis and operation and maintenance management,which can satisfy the needs of wind turbine condition online monitoring and predictive maintenance in the actual working scenarios.关键词
风电机组/小波分析/深度学习/故障诊断/物联网Key words
wind turbine/wavelet analysis/deep learning/fault diagnosis/Internet of Things分类
能源与动力引用本文复制引用
秦晓梅,王有杰,俞啸..基于小波分析与迁移学习的风电机组关键部件故障诊断系统研究[J].机电工程技术,2025,54(5):134-138,147,6.基金项目
国家重点研发计划项目(2017YFC0804400,2017YFC0804401) （2017YFC0804400,2017YFC0804401）
