• 模式识别与图像处理 •

基于有理双树复小波和SVM的滚动轴承故诊断方法

1. 重庆大学数学与统计学院 重庆401331,重庆大学数学与统计学院 重庆401331,重庆大学数学与统计学院 重庆401331
• 出版日期:2018-11-14 发布日期:2018-11-14
• 基金资助:
本文受国家自然科学基金项目(61173030)资助

Fault Diagnosis Method of Rolling Bearing Based on Dual-tree Rational-dilation Complex Wavelet Packet Transform and SVM

SUN Shan-shan, HE Guang-hui and CUI Jian

• Online:2018-11-14 Published:2018-11-14

Abstract: In order to improve the recognition accuracy of SVM classification,a fault diagnosis method was proposed based on dual-tree rational-dilation complex wavelet transform and support vector machine(SVM),according to the characteristics of rolling bearing fault vibration signal.Firstly,the fault signal is decomposed into several different frequency band components through dual-tree rational-dilation complex wavelet transform.Secondly,normalization processing is made from the energy of each component.Finally,the energy characteristics parameters of each frequency band component are taken as input of the SVM to identify the fault type of rolling bearing.The experimental results prove that the proposed method can identify the fault type accurately and effectively.

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