Applications of Fractional Lower Order Time-frequency Representation to Machine Bearing Fault Diagnosis

作     者：Junbo Long Haibin Wang Peng Li Hongshe Fan 

作者机构：Department of Electrical and EngineeringJiujiang UniversityJiujiang 332005China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2017年第4卷第4期

页      面：734-750页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 08[工学] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61261046,61362038) the Natural Science Foundation of Jiangxi Province(20142BAB207006,20151BAB207013) the Science and Technology Project of Provincial Education Department of Jiangxi Province(GJJ14738,GJJ14739) the Research Foundation of Health Department of Jiangxi Province(20175561) the Science and Technology Project of Jiujiang University(2016KJ001,2016KJ002) 

主　　题：adaptive function Alpha stable distribution auto-regressive(AR) model non-stationary signal parameter estimation time frequency representation 

摘      要：The machinery fault signal is a typical non-Gaussian and non-stationary process. The fault signal can be described by SaS distribution model because of the presence of impulses.Time-frequency distribution is a useful tool to extract helpful information of the machinery fault signal. Various fractional lower order(FLO) time-frequency distribution methods have been proposed based on fractional lower order statistics, which include fractional lower order short time Fourier transform(FLO-STFT), fractional lower order Wigner-Ville distributions(FLO-WVDs), fractional lower order Cohen class time-frequency distributions(FLO-CDs), fractional lower order adaptive kernel time-frequency distributions(FLO-AKDs) and adaptive fractional lower order time-frequency auto-regressive moving average(FLO-TFARMA) model time-frequency representation method.The methods and the exiting methods based on second order statistics in SaS distribution environments are compared, simulation results show that the new methods have better performances than the existing methods. The advantages and disadvantages of the improved time-frequency methods have been summarized.Last, the new methods are applied to analyze the outer race fault signals, the results illustrate their good performances.