中国电机工程学报2024，Vol.44Issue(22)：8908-8919,中插18,13.DOI:10.13334/j.0258-8013.pcsee.230892
考虑多时间尺度信息的风力发电机滚动轴承故障预测
Fault Prediction of Wind Turbine Rolling Bearing Considering Multi-time Scale Information
摘要
Abstract
The fault of wind turbine rolling bearing will cause long-term downtime.To accurately predict the fault of wind turbine rolling bearing,this paper proposes a fault prediction method of wind turbine rolling bearing considering multi-time scale information.First,successive variational mode decomposition(SVMD)is used to adaptively extract the multi-dimensional characteristics of bearing health data.Next,the decomposed IMFS is input into the Informer model to extract multi-scale time information for training,and the nonstandard Bayesian optimization algorithm based on tree structure Parzen density estimation(TPE)is used to optimize the super parameters of the Informer model.Then,the fault index based on residual is constructed,and the fault early warning threshold is determined by kernel density estimation(KDE).Finally,the running data is input into the trained Informer model for fault prediction.In this paper,the bearing temperature data of a wind farm are selected for fault prediction.The simulation results show that the SVMD-TPE-Informer model considering multi-time scale information has higher prediction accuracy and calculation efficiency.The proposed method can predict faults with a lead time of 15.5 hours and 10 hours respectively in two fault cases,without any false alarm,which verifies the effectiveness and stability of the proposed model.关键词
连续变分模式分解/贝叶斯优化/Informer模型/故障预测Key words
successive variational mode decomposition(SVMD)/Bayesian optimization/informer model/failure prediction分类
信息技术与安全科学引用本文复制引用
赵洪山,林诗雨,孙承妍,杨伟新,张扬帆..考虑多时间尺度信息的风力发电机滚动轴承故障预测[J].中国电机工程学报,2024,44(22):8908-8919,中插18,13.基金项目
国家自然科学基金项目(51277074) （51277074）
国家电网有限公司科技项目(52018K2001P). Project Supported by National Natural Science Foundation of China(51277074) （52018K2001P）
Science and Technology Project of State Grid Corporation of China(52018K2001P). （52018K2001P）
