Application of particle swarm optimization blind source separation technology in fault diagnosis of gearbox  

作　　者：黄晋英 潘宏侠 毕世华 杨喜旺 

机构地区：[1]School of Mechanical Engineering and Automatization, North University of China [2]School of Aerospace Science and Technology, Beijing Institute of Technology [3]School of Electronic and Computer Science Technology, North University of China

出　　处：《Journal of Central South University》2008年第S2期409-415,共7页中南大学学报（英文版）

基　　金：Project(50875247) supported by the National Natural Science Foundation of China;Project(2007011070) supported by the Natural Science Foundation of Shanxi Province, China

摘　　要：Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.

关 键 词：PSO BLIND source SEPARATION FAULT diagnosis FAULT information enhancement GEARBOX 

分 类 号：TH132.41[机械工程—机械制造及自动化]