【作者】 刘观青；

【导师】 王生昌； 李良敏；

【作者基本信息】 长安大学 ， 载运工具运用工程， 2011， 硕士

【摘要】 滚动轴承是各种机械设备中的重要组成部件,同时也是易损坏的元件之一,据统计,现有机械设备的故障中,有30%是由滚动轴承故障造成的,可见其工作状态直接影响着机械设备的工作性能。由于滚动轴承寿命的随机性较大,并不适合采用传统的定时维修制度。对滚动轴承进行状态监测与诊断,变定时维修于视情维修或预知维修,可以防止机械设备功能性能下降,减小或避免事故发生,具有重要的现实意义。本文研究了滚动轴承的振动机理,给出了故障特征频率和固有频率的计算方法,分析了各时域、频域参数指标；研究了基于小波变换及小波包分解提取故障特征的方法,构建了初始特征集,并针对Mallat算法的频率混叠现象,给出了单子带重构改进算法,通过仿真实验对比分析了两种算法的性能；阐述了主成分分析与RBF网络的基本原理及计算步骤,结合实例分析了基于主成分分析特征提取的必要性和有效性；最后搭建了滚动轴承故障诊断系统的框架,采用VC++与Matlab语言混合编程,开发了滚动轴承故障诊断系统软件,采用滚动轴承故障模拟实验台实测数据对系统进行了功能检测,结果表明该系统对于滚动轴承状态识别率可达到90%以上,较好的完成了滚动轴承故障诊断任务。更多还原

【Abstract】 Rolling bearing is an important part of mechanical and electrical equipments. According to statistics, the existing mechanical equipment failures,30% are caused by the rolling bearing failure, we can see their work directly affects the performance of machinery and equipment. Because the randomness of life of large rolling bearings, the timing is not suitable for traditional maintenance system. Rolling element bearing condition monitoring and diagnosis, changes in maintaining the condition maintenance or predictive maintenance to prevent performance degradation machinery and equipment capabilities, reduce or avoid the accident, has important practical significance.In this paper,we first introduced the vibration mechanism of rolling bearing and the function of characteristic and natural frequency to fault bearing, the indicators of time-domain parameters and the frequency domain parameter index; Study the method of based on wavelet transform and wavelet packet decomposition to extract fault features and constructed the initial feature set,because the mallat algorithm of discrete wavelet transform exist frequency alias, we put forward the improved single sideband reconstruction algorithm, provid the arithmetic software, Compare the performance of two algorithms.Explain the basic principles and calculation steps of principal component analysis and RBF Neural Networks, With examples of PCA-based feature extraction the necessity and effectiveness; Finally we built a framework for rolling bearing fault diagnosis system, using VC++and Matlab mixed programming language,developed rolling bearing fault diagnosis system software, using real data that gathered from Bearing Fault simulation sets the system functional testing, The results show that the system state recognition rate for the rolling bearing 90% or more, the better to complete the rolling bearing fault diagnosis tasks.更多还原