浅谈研究故障诊断的几个混沌特征指数  

Study on Several Chaos Characteristic Indices for Fault Diagnosis

作　　者：张妍 李翊宁 张文帅 Zhang Yan;Li Yi-ning;Zhang Wen-shuai(Shaanxi Polytechnic Institute,Xianyang Shannxi 712000)

机构地区：[1]陕西工业职业技术学院,陕西咸阳712000

出　　处：《内燃机与配件》2022年第17期55-57,共3页Internal Combustion Engine & Parts

基　　金：陕西省教育厅自然科学专项(20JK0499)。

摘　　要：振动信号特征的有效提取是准确判断机器故障的关键。机械设备的振动信号有时会呈现出非平稳、非线性的混沌动力学特征。传统的时域、频域或者时频分析法都不能精准提取出故障振动信号中的非线性特征。本文列举了几个研究混沌动力系统的特征指数:Lyapunov指数、关联维数、Kolmogorov熵、广义线性指数、曲率指数,分析了基于混沌动力学理论的几个特征指数的计算原理及其优缺点。研究混沌动力系统的指数对非线性故障信号特征的提取有着重要意义。Effective extraction of vibration signal features is the key to determine machine faults accurately.The vibration signal of mechanical equipment exhibits non-stationary and nonlinear chaotic dynamic characteristics sometimes.Traditional time-domain,frequency-domain or time-frequency analysis methods cannot extract the nonlinear characteristics of fault vibration signals accurately.This paper lists several characteristic indices for studying chaotic dynamical systems:Lyapunov exponent,Correlation dimension,Kolmogorov entropy,Generalized alignment index,Curvature index.Based on the theory of chaotic dynamics,this paper analyzes the calculation principles and their advantages and disadvantages of the above characteristic indices.Research on the indices of chaotic dynamic systems is of great significance to the extraction of nonlinear fault signal features.

关 键 词：故障诊断 混沌动力系统 特征指数 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]