机械制造与自动化2023，Vol.52Issue(6)：226-228,3.DOI:10.19344/j.cnki.issn1671-5276.2023.06.054
基于WPD-tSNE-SVM方法的电站机组主轴故障诊断分析
Fault Diagnosis Analysis of Power Station Spindle Based on WPD-tSNE-SVM Model
摘要
Abstract
In order to improve the fault diagnosis efficiency of power station unit spindle,a WPD-tSNE-SVM combined model was designed,and wavelet packet mixed feature and support vector machine were used to carry out fault diagnosis of power station unit bearing.The results show that the wavelet packet mixed feature extraction method can satisfy the effectiveness of the regular distribution of dimensionality reduction data by using tSNE method.The nonlinear SVM multi-fault classifier is in line with the precise fault analysis of the wavelet packet mixed features,each classifier can effectively classify the wavelet packet mixed feature set,and the radial basis kernel function is applied to set the nonlinear SVM diagnosis method to achieve higher accuracy,thus providing reference value for the subsequent maintenance process,promoting the further improvement of maintenance efficiency,and effectively guaranteeing the stable operation of the main shaft of the power station unit.With the method,the operation fault of spindle bearing is diagnosed,which provides guidance for subsequent maintenance,achieves higher maintenance efficiency,and ensures the operation stability of power station unit spindle.关键词
电站机组/主轴/故障诊断/小波包分解/t分布式随机邻域嵌入/支持向量机Key words
power station unit/spindle/fault diagnosis/wavelet packet decomposition/t-distributed random neighborhood embedding/support vector machine分类
机械制造引用本文复制引用
曹康栖,李灿..基于WPD-tSNE-SVM方法的电站机组主轴故障诊断分析[J].机械制造与自动化,2023,52(6):226-228,3.基金项目
河南省高等学校重点科研项目(21B535003) （21B535003）
