A Bearing Fault Diagnosis Method Using Fusion of Lie Group Classifier and GA

摘要：

　　A novel bearing fault diagnosis method based on Lie group was proposed,and genetic algorithm(GA) was introduced to optimize feature amount.This method was applied to inner ring fault,outer ring fault and rolling element fault of rolling bearing.Firstly,the rolling bearing vibration signal was decomposed as intrinsic model functions (IMF) by using the empirical mode decomposition (EMD) method.The energy of every IMF and the singular value of the IMF matrix were chosen as features.The Shannon and Renyi entropy of the energy and singular value distribution were also extracted.Secondly genetic algorithm was used to reduce feature redundancy,with lowest classifier error rate and least feature amount as finess function.At last,a comparison was made between this method and least square support vector machine(LSSVM).The results showed that Lie group clkassifier was more sensitivce to feature.This method could use less feature amount to diagnose fault.

关键词： Lie group Lie group classifier genetic algorithm(GA) fault diagnosis

作者: Zhao Si-yuan Wang Tao Ge Xin Liu Yun

作者单位: Wuhan Mechanical Technology College,Wuhan,Hubei Province,China

会议类型: 国际会议

会议名称: 2013 International Conference on Advanced Engineering Materials and Architecture Sciences(2013先进工程材料与建筑科学国际会议)(ICAEMAS 2013)

会议地点: 西安

会议语种:英文

页码: 223-226

在线出版日期: 2013-07-27（万方平台首次上网日期，不代表论文的发表时间）