机电工程技术2025，Vol.54Issue(5)：103-107,196,6.DOI:10.3969/j.issn.1009-9492.2024.00126
一种基于数据驱动和负时间序列PCA的滚动轴承健康指标构建方法
Constructing Health Indicators of Rolling Bearings Based on Data-driven Method and Negative Time Series of PCA Theory
摘要
Abstract
Uncertainties in the manufacturing and assembly processes of rolling bearings result in inconsistent initial degradation levels of their health indicators and insensitivity to early failures.In response to this issue,a method is proposed to construct health indicators characterizing the degradation performance of rolling bearings based on a data-driven method and negative time series of principal component analysis(PCA)theory.First,16 time-domain features,which represent all fault information at various stages of bearing degradation,are extracted from the original vibration data using a data-driven method.Then,feature data are denoised using a mean normalization method,and dimensions are unified using a standardization method.The final step is to construct the health indicators of the bearings.This step is mainly achieved by using PCA based on negative time series.Through application to experimental data and comparison with root mean square(RMS)indicators,the results show that the negative time series indicators all find the early faults before the RMS indicators,the effectiveness of this method can be verified.关键词
滚动轴承/健康指标/数据驱动/主成分分析(PCA)Key words
rolling bearings/heath index/data-driven/principal component analysis(PCA)分类
机械制造引用本文复制引用
王栩沂,丁泽良,米承继,王思睿..一种基于数据驱动和负时间序列PCA的滚动轴承健康指标构建方法[J].机电工程技术,2025,54(5):103-107,196,6.基金项目
湖南省自然科学基金项目(2023JJ50186) （2023JJ50186）
湖南省自然科学基金项目(2022JJ50056) （2022JJ50056）
