太原理工大学学报2017，Vol.48Issue(6)：959-962,968,5.DOI:10.16355/j.cnki.issn1007-9432tyut.2017.06.014
基于小波包变换和极限学习机的滚动轴承故障诊断
Multifault Dignosis for Rolling Bearings Based on Wavelet Packet Transform and Extreme Learning Machine
摘要
Abstract
In this paper,a new intelligent fault diagnosis scheme and classification based on wavelet packet transform (WPT)and extreme learning machine (ELM)was proposed.The ener-gy of each band was calculated from decomposed original vibration signals as the feature vector in-put to classifiers.A novel classifier,ELM,was introduced in this study to diagnose the fault on rolling bearings.Different kinds of motor bearing vibration signals were analyzed.The results show that the bearing's normal state,single fault state and multifault state can be effectively clas-sified.关键词
轴承/故障诊断/小波包变换/极限学习机Key words
rolling bearings/fault diagnosis/wavelet packet transform/extreme learning ma-chine分类
机械制造引用本文复制引用
李瑞莲,兰媛,熊晓燕..基于小波包变换和极限学习机的滚动轴承故障诊断[J].太原理工大学学报,2017,48(6):959-962,968,5.基金项目
国家自然科学基金资助项目(61371062) （61371062）
山西省自然科学基金资助项目(2014081030) （2014081030）
