基于经验模式分解的单通道机械信号盲分离  

Blind Source Separation of Single-channel Mechanical Signal Based on Empirical Mode Decomposition

作　　者：毋文峰[1] 陈小虎[1] 苏勋家[1] 

机构地区：[1]中国人民解放军第二炮兵工程学院5系,西安710025

出　　处：《机械工程学报》2011年第4期12-16,共5页Journal of Mechanical Engineering

基　　金：总装备部预研重点基金资助项目

摘　　要：盲源分离是机械设备复合故障诊断的一种有效方法,经验模式分解是非平稳信号分析的有力工具,它将非线性、非平稳信号分解成为一系列线性、平稳的本征模函数信号。在机械故障信号盲分离中,单通道机械信号盲分离是一个病态问题。针对单通道机械信号盲分离的困境,综合盲源分离和经验模式分解各自的优点,提出基于经验模式分解的单通道机械信号源数估计和盲源分离方法。针对单通道机械观测信号进行经验模式分解,并将单通道信号和其本征模函数组成多维信号,利用奇异值分解估计机械源数目,根据源信号数目重组多通道机械混合信号,并利用FastICA算法实现机械信号的盲分离。将该方法应用于轴承和齿轮的仿真研究,正确分离出轴承和齿轮源信号,仿真研究表明,它能很好地解决单通道机械信号的源数估计和盲源分离难题。Blind source separation（BSS） is an effective method to diagnose multi-fault of mechanical equipment.Empirical mode decomposition（EMD） is a potent tool to analyze non-stationary signals and it can decompose nonlinear and non-stationary signal into a set of linear and stationary intrinsic mode functions.In blind source separation of mechanical fault signals,single-channel mechanical signal separation is an ill-conditioned problem.Here,by combining the respective advantages of BSS and EMD,a blind source separation method based on empirical mode decomposition is proposed to solve the difficult problem of single-channel mechanical signal separation.This algorithm is composed of three steps.Step 1 is empirical mode decomposition of single-channel mechanical signal and its combining with intrinsic mode functions.Step 2 is to estimate mechanical sources number by singular value decomposition（SVD）.Step3 is to recombine multi-channel mixed mechanical signals according to estimated sources number and separate mechanical sources with FastICA algorithm.This method is applied to the simulation research of bearing and gear in order to correctly separate their source signals.Simulation research indicates that it can well solve the difficult problem of source number estimation and blind source separation of single-channel mechanical signal.

关 键 词：盲源分离 独立成分分析 FASTICA 经验模式分解 奇异值分解 

分 类 号：TH165.3[机械工程—机械制造及自动化]