现代制造工程Issue(6)：154-161,94,9.DOI:10.16731/j.cnki.1671-3133.2024.06.020
基于自适应LPP特征降维和改进VPMCD的滚动轴承故障诊断
Rolling bearing fault diagnosis method based on adaptive LPP and improved VPMCD
摘要
Abstract
A fault diagnosis method based on adaptive Locality Preserving Projection(LPP)feature dimensionality reduction and improved Variable Predictive Model based Class Discriminate(VPMCD)was proposed to solve the issues of high fault feature di-mensionality and high time consumption caused by pattern recognition in mechanical system condition monitoring and fault diag-nosis.Firstly,time-frequency domain features,energy features,and complexity features were extracted from rolling bearing vibration signals to form a high-dimensional fault feature dataset;secondly,the high-dimensional fault feature set was downgraded by using the adaptive LPP method to obtain the low-dimensional sensitive fault features;lastly,the low-dimensional features were classified and recognized using the improved VPMCD method,and then the type of faults was judged.The analysis of rolling bear-ing fault diagnosis experiments showed that the adaptive LPP method overcomes the defect of manual pardameter selection in the LPP method,and has less computational time on the basis of obtaining low dimensional sensitive fault features.Compared with methods such as PCA,LTSA,LLTSA,Isomap,LLE,etc,it had obvious advantages;the improved VPMCD method can overcome the contingency and one-sided nature of the artificial selection model,and achieve 99.4％diagnostic accuracy in the identification of 10 fault states of rolling bearings.Compared with the optimization parameter support vector machine method,it reduced the identification time and improved the efficiency of fault diagnosis,which has certain advantages.关键词
滚动轴承/故障诊断/特征降维/模式识别/局部保持投影/多变量预测模型Key words
rolling bearing/fault diagnosis/feature dimension reduction/pattern recognition/Locality Preserving Projection(LPP)/Variable Predictive Model based Class Discriminate(VPMCD)分类
机械工程引用本文复制引用
王斐,许波..基于自适应LPP特征降维和改进VPMCD的滚动轴承故障诊断[J].现代制造工程,2024,(6):154-161,94,9.基金项目
河北省自然科学基金项目(E2016506003) （E2016506003）
