滚动轴承状态监测..docxVIP

轴承状态监测姓 名： 学 号：指导老师： 学 院：机械工程学院班 级： 专 业：测控技术与仪器摘要：轴承是各类机械设备中的常用部件，也是最易损的部件之一。轴承出现故障而得不到及时更换时，将可能影响整个机械装置的工作，所以要对轴承进行状态监测，以便及时发现轴承的故障。本文对轴承状态监测的流程及各流程的实现进行了较为详细的阐述，说明了进行轴承状态监测的意义及其技术的发展过程，之后介绍了状态监测的基本步骤及各个步骤的实现方法，最终完成对轴承状态的正确判断，并对此项技术进行了总结和拓展。关键字：滚动轴承；状态检测；时域分析；频域分析；神经网络目 录第一章 研究背景························································第3页1.1 轴承状态监测的意义和重要性··········································第3页1.2 常见故障形式························································第3页 1.2.1滚动轴承的基本结构1.2.2 滚动轴承的常见故障形式1.3 滚动轴承诊断的发展与现状············································第5页1.3.1 滚动轴承诊断的发展1.3.2 滚动轴承诊断的现状1.4 常见滚动轴承的方法··················································第6页 1.4.1 温度监测 1.4.2 油液监测1.4.1 振动监测1.4.2 声波发射监测1.5 常见的故障监测传感器················································第7页1.6 常见的特征参数提取手段··············································第7页 1.6.1 基于时域和频域特征参数的提取方法 1.6.2 基于AR模型的特征参数提取方法 1.6.3 基于分型理论的特征参数提取方法 1.6.4 基于小波分析的特征参数提取方法1.7 常见的模式识别手段··················································第8页 1.7.1 模糊模式识别法 1.7.2 灰色系统理论法 1.7.3 人工神经网络法1.8 小结································································第9页第二章 滚动轴承故障的检测与诊断·······································第10页2.1 特征提取··························································第10页 2.1.1 零均值化 2.1.2 时域特征值得提取2.2 频域分析法························································第13页 2.1.1信号频域分析及特征提取 2.2.2 频域特征提取2.3 时域与频域信号归一化···············································第15页第三章 模式识别·······················································第16页3.1 神经网络识别······················································第16页3.2 神经网络测试······················································第17页第四章 总结···························································第17页研究背景1.1 轴承状态监测的意义和重要性随着科技的发展，现代工业正逐步向生产设备大型化、复杂化、高速化和自动化方向发展，在提高生产率、降低成本、节约能源、减少废品率、保证产品质量等方面具有很大的优势。?但是，正如文献[1]所述，由于故障所引起的灾难性事故及其所造成的对生命与财产的损失和对环境的破坏等也是很严重的，这就使得人们对诸如航空航天器、核电站、热电厂及其他大型化工设备的可靠性、安全性提出了越来越高的要求。故障诊断理论就是为了满足对系统可靠性和安全性要求的提高，减少并控制灾难性事故的发生而发展起来的。我们可以从查阅文献[2]中得知：滚动轴承与其他机