提高大型复杂机电系统故障诊断质量的几种新方法  

SOME MEASURES TO IMPROVE THE QUALITY OF FAULT DIAGNOSIS FOR LARGE-SCALE COMPLEX ELECTROMECHANICAL SYSTEMS

作　　者：史铁林[1] 陈勇辉[1] 李巍华[1] 熊良才[1] 廖广兰[1] 

机构地区：[1]华中科技大学机械科学与工程学院,武汉430074

出　　处：《机械工程学报》2003年第9期1-10,共10页Journal of Mechanical Engineering

基　　金：国家重大基础研究项目基金(G1998020320);湖北省自然科学基金(2000j125)

摘　　要：分析了大型复杂机电系统故障的可诊断性问题,探讨了影响故障可诊断性的主要因素及其评价标准,研究了HHT(Hilbert/Huang transform)时频分析方法在提高大型复杂机电系统诊断信息质量中的应用。为了进一步提高诊断系统对未知故障的诊断质量,分析讨论了自组织特征映射、生成拓扑映射,以及曲元分析等无监督机器学习算法在大型复杂机电系统故障诊断中的应用。针对故障信号的非线性特征以及多类复杂故障的线性不可分问题,结合机器学习领域的最新研究成果,探讨了基于核的机械故障特征提取方法与基于核的故障模式分类方法,并对采用核方法分析设备运行状态的趋势变化作了初步探讨。The diagnosability problems of the large-scale complex electromechanical systems are studied, and the main influencing factors and evaluation criteria of the fault diagnosability are proposed, then the applications of Hilbert/Huang time-frequency analysis to fault diagnosis are investigated in order to improve the quality of fault information. Subsequently, to correctly recognize some unknown faults, some unsupervis-ing learning algorithms such as self-organizing feature maps, generative topographic mapping and curvilinear component analysis are used to classify different working modes of the large-scale complex electromechanical systems. Finally, based on the latest research finds in machine learning, to extract nonlinear features from fault signals and to deal with the problems of non-separated mechanical faults, some kernel-based methods are presented, and an exploratory study for machine running trend analysis using kernel methods has been executed.

关 键 词：机电系统 状态监测 故障诊断 可诊断性 无监督学习 核方法 

分 类 号：TH17[机械工程—机械制造及自动化] TP271.4[自动化与计算机技术—检测技术与自动化装置]