A novel blind deconvolution method and its application to fault identification

作     者：Cheng, Yao Chen, Bingyan Mei, Guiming Wang, Zhiwei Zhang, Weihua 

作者机构：Southwest Jiaotong Univ State Key Lab Tract Power 111First SectNorth Second Ring Rd Chengdu 610031 Sichuan Peoples R China 

出 版 物：《JOURNAL OF SOUND AND VIBRATION》 (声音与振动杂志)

年 卷 期：2019年第460卷

页      面：114900-000页

核心收录：

学科分类：07[理学] 082403[工学-水声工程] 08[工学] 070206[理学-声学] 0802[工学-机械工程] 0824[工学-船舶与海洋工程] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：Autonomous Research Topics of State key laboratory of Traction power, Southwest Jiaotong University [2018TPL_T01] Development Program of China [2016YFB1200401-102A] National Natural Science Foundation of China 

主　　题：Blind deconvolution Particle swarm optimization algorithm Fault identification Railway Rolling element bearing 

摘      要：Blind deconvolution is a method for enhancing the fault feature of rolling element bearings. Based on different maximization criteria, including kurtosis, correlated kurtosis, D-norm, multi-D-norm, and cyclostationarity indicator, different blind deconvolution algorithms have been proposed as powerful tools for fault feature extraction. However, kurtosis and D-norm are susceptible to extreme values, while the other three criteria strongly rely on prior knowledge of the fault period. To overcome the shortcomings of the existing criteria, this study proposes a new criterion called impulse-norm. It is a time-domain parameter defined as the ratio of the average amplitude of the first several maximum energy points to the energy of the entire signal. As opposed to kurtosis and D-norm, the impulse-norm is not affected by strong random impulses. Unlike correlation kurtosis, multi-D-norm and cyclostationarity indicator, it is also independent from the fault period. Based on impulse-norm, we also propose a new deconvolution algorithm called particle swarm optimization-based maximum impulse-norm deconvolution. This blind deconvolution algorithm employs generalized sphere coordinate transformation and adopts the PSO algorithm to optimally solve the filter coefficients by maximizing the impulse-norm of the signal being filtered. The proposed method was validated using simulated signals and high-speed train axle-box bearing experimental signals. The simulation and experimental results indicated that the proposed PSO-MIND method can effectively identify the weak impulse fault feature of rolling element bearings. (C) 2019 Elsevier Ltd. All rights reserved.