滚动轴承状态监测选编.docxVIP

西南交通大学 PAGE \* MERGEFORMAT19 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