一种Informer模型的滚动轴承剩余寿命预测方法  

A Method for Predicting Remaining Life of Rolling Bearings Using Informer Model

作　　者：李广福 马萍[1] 张宏立[1] 王聪[1] LI Guangfu;MA Ping;ZHANG Hongli;WANG Cong(School of Electrical Engineering,Xinjiang University,Urumqi 830017,China)

机构地区：[1]新疆大学电气工程学院,乌鲁木齐830017

出　　处：《机械科学与技术》2024年第12期2016-2023,共8页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家自然科学基金项目(52065064,51967019);新疆维吾尔自治区自然科学基金青年项目(2022D01C89);天山青年计划(2020Q066)。

摘　　要：为准确描述滚动轴承性能退化趋势和预测剩余使用寿命,解决轴承剩余使用寿命预测精度低的问题,提出一种基于多尺度退化指标结合Informer模型的滚动轴承剩余使用寿命预测方法。提取滚动轴承振动信号时频域特征,通过皮尔逊相关系数结合核主成分分析法(Kernel principal component analysis,KPCA)得到能更好的表征滚动轴承退化状态的多尺度退化指标;引入Informer模型,将多尺度退化指标作为其输入高效充分地挖掘输入多尺度退化指标与滚动轴承退化趋势的复杂关系,实现滚动轴承剩余使用寿命预测。实验结果表明该方法能有效提升轴承剩余使用寿命预测精度,为滚动轴承健康管理和性能评估提供参考依据和实现途径。In order to accurately describe the performance degradation trend and predict the remaining useful life of rolling bearings,and solve the problem of low prediction accuracy of the remaining useful life of rolling bearings,a new method for predicting the remaining useful life of rolling bearings was proposed based on multi-scale degradation index combined with Informer model.The time-frequency domain characteristics of rolling bearing vibration signals were first extracted,and a multiscale degradation index was obtained by Pearson correlation coefficient combined with kernel Principal Component analysis(KPCA),which could better characterize the degradation state of rolling bearings.Informer model was introduced to efficiently and fully explore the complex relationship between the input multi-scale degradation index and the rolling bearing degradation trend,and realize the prediction of the remaining useful life of rolling bearings.The experimental results show that this method can effectively improve the prediction accuracy of remaining useful life of bearings,and provide a reference and implementation approach for health management and performance evaluation of rolling bearings.

关 键 词：风力机 滚动轴承 剩余使用寿命预测 Informer模型 多尺度退化指标 

分 类 号：TH133.33[机械工程—机械制造及自动化]