【作者】 潘宏侠； 黄晋英； 毛鸿伟； 刘振旺；

【Author】 PAN Hong-xia HUANG Jin-ying MAO Hong-wei LIU Zhen-wang (School of Mechanical Engineering & Automatization,North University of China,Taiyuan 030051,China)

【机构】 中北大学机械工程与自动化学院；

【摘要】 齿轮传动箱的故障征兆,可以通过不同的特征参量表现出来。传动箱工作过程中,由于响应信号成分复杂,提取其敏感的故障特征信息非常困难。故障程度、部位和类型等对特征参量的敏感程度差别很大,通过传统的特征提取和分析方法可以建立庞大的特征参量集。如何从众多的特征参量中确定可靠有效的故障特征参量,如何根据故障对特征参量的敏感程度优化筛选特征参量集,是实现实时在线故障诊断亟待解决的一个问题。本文针对传动箱故障特征选择问题,提出了基于粒子群优化(PSO)算法的特征选择方法。将粒子群优化技术用于研究传动箱振动响应信号的分析与处理,用于故障诊断特征参量集的提取与优化,形成了适合该齿轮传动箱的有效故障特征参量,从而建立了与齿轮传动箱故障现象密切相关的特征参量集。把此算法应用到齿轮传动箱故障诊断中,结果证明,该算法有很好的效果,提高了诊断精度,比常用的梯度下降算法具有更快的优化速度。更多还原

【Abstract】 The failure sympton of the gearbox case can be indicated by different characteristic parameters.In the work process of gearbox,because the responding signal is very complex,it is difficult to extract its sensitive fault attributive information.The sensitive degree of the fault degree,fault position and fault type is very different,so the characteristic parameter set constructed by the traditional characteristic extraction and analysis method is voluminous.Therefore,how to define the reliable and effective fault characteristic parameter set and how to optimize the parameter set by the sensitive degree are the await solved problems to realize real time and online fault diagnosis.In this paper,the characteristic extractive method base on PSC) is presented for the problem of gear case failure characteristic selection.Then the technology is applied to analyse and process the vibration responding signal of gear case,extract and optimize the fault characteristic parameter set.Finally the parameter set osculationally related to the gear case’s fault is constructed and it is used to the fault diagnosis.It proves of the diagnosis result that PSO algorithm has good effectiveness, higher diagnosis precision and fast optimal speed than the traditional genetic algorithm.更多还原