摘要:动车组上的滚动轴承作为动车组走行部的最为关键组成部件,其性能是否优良,直接决定了动车组能否长期的安全稳定地运行，能否为人们提供一个安全的出行环境，因此,建立一套可靠的动车组滚动轴承故障诊断系统非常重要。
本文提出了一种基于FCM算法的动车组滚动轴承的智能诊断方法，可以对动车组滚动轴承故障数据进行信号分析并可以有效地诊断故障数据属于哪类故障。该诊断方法以包络谱分析作为特征提取工具，选取滚动轴承五个特征频率处的谱值为特征向量，针对轴承的正常、内圈故障、外圈故障、滚动体故障的四种状态，利用FCM算法求出聚类中心和隶属矩阵U，对带诊断样本进行特征提取，得到待诊断样本隶属于训练样本的程度，根据隶属度分类，确定故障类型，从而实现动车组滚动轴承FCM智能诊断。
关键词:动车组；智能诊断；信号分析；FCM
Research on FCM intelligent diagnosis of rolling bearing of EMU
Abstract: Rolling bearings on EMUs are the most critical component of EMU's running department. Whether its performance is good or not determines the EMU's long-term safe and stable operation. Can it provide a safe travel environment for people? Therefore, It is very important to establish a reliable rolling stock fault diagnosis system for EMUs.
This paper presents an intelligent diagnosis method for EMU rolling bearing based on FCM algorithm. The system can perform signal analysis on the EMU rolling bearing fault data and can effectively diagnose what kind of fault the fault data belongs to. The diagnosis method uses envelope spectrum analysis as a feature extraction tool. The spectrum at five characteristic frequencies of the rolling bearing is selected as the feature vector, and the FCM is used for the four states of bearing normal, inner ring failure, outer ring failure, and rolling element failure. The algorithm finds the clustering center and the membership matrix U, performs feature extraction on the diagnostic sample, and obtains the extent to which the sample to be diagnosed belongs to the training sample. According to the classification of the degree of membership, the fault type is determined, thereby achieving intelligent diagnosis of the EMU rolling bearing.
Keywords: EMU;Intelligent diagnosis ;Signal analysis ;FCM
目录
1 绪论 1
1.1本课题的背景和研究意义 1
1.1.1课题背景 1
1.1.2研究意义 1
1.2国内外研究概况 1
1.2.1故障诊断技术研究 1
1.2.2滚动轴承的诊断技术研究 3
1.2.3文献综述 4
1.3本文的结构和主要内容 5
2 滚动轴承振动机理及信号分析方法 6
2.1滚动轴承振动特征 6
2.1.1滚动轴承的基本机构 6
2.1.2滚动轴承振动机理 6
2.1.3滚动轴承故障的基本形式 7
2.2滚动轴承的振动信号特征 8
2.3滚动轴承振动的特征频率 8
2.3.1滚动轴承故障振动波形 9
2.3.2滚动轴承的固有振动频率 10
2.3.3滚动轴承的故障特征频率 动车组滚动轴承FCM智能诊断研究:http://www.chuibin.com/zidonghua/lunwen_205340.html