关于滚动轴承故障诊断方法的研究 - 图文

关于滚动轴承故障诊断方法的研究

课 程： 学 院： 班 级： 指导教师： 姓 名： 学 号：

完成日期 ： 2015年12月15日

关于滚动轴承故障检测与诊断方法的研究

目录

第一章 研究背景

1进行滚动轴承故障检测与诊断的背景与意义·······················01 1.1滚动轴承故障检测与诊断领域背景···························01 1.2进行滚动轴承故障检测与诊断的意义·························01 2常见的滚动轴承结构···········································01 3常见的滚动轴承故障形式·······································02 4滚动轴承故障监测与诊断的一般步骤·····························03 4.1常见的滚动轴承故障信息获取方法···························04 4.1.1温度监测法···········································04 4.1.2振动监测法···········································04 4.1.3油液监测法···········································04 4.1.4光纤监测法···········································04 4.1.5声发射法·············································05 4.2常见的滚动轴承故障特征提取方法···························05 4.2.1基于传统时域统计参数的特征提取·······················05 4.2.2基于频域和时频分析特征提取···························05 4.2.3基于非线性参数的特征提取·····························05 4.3常见的滚动轴承故障状态模式识别···························06 4.3.1人工神经网络·········································06 4.3.2隐马尔可夫模型·······································07 4.3.3支持向量机···········································07 5常见的用于滚动轴承故障检测与诊断的传感器·····················07 5.1传感器的灵敏度···········································07 5.2滚动轴承故障诊断领域中用到的振动传感器···················08 5.3滚动轴承故障诊断领域中用到的加速度传感器·················08 5.4滚动轴承故障诊断领域中用到的压电式加速度传感器···········08 6常用的滚动轴承故障诊断与检测的分析方法·······················09 6.1基于流行学习法的滚动轴承故障诊断和检测方法···············09 6.2基于无量纲指标与波谱分析的滚动轴承故障诊断方法···········10 6.3基于谱峭度及原子分解的滚动轴承故障诊断方法···············10 6.4基于模型辨识的滚动轴承故障诊断方法·······················10 6.5基于EMD的滚动轴承故障灰色诊断方法·······················11 6.6基于近邻元分析的滚动轴承故障诊断方法·····················11 6.7基于LMD的滚动轴承故障诊断方法···························11 6.8基于BP神经网络的滚动轴承故障诊断方法····················12 6.9基于量子遗传算法和谱峭度法相结合的滚动轴承故障诊断方法···12 6.10基于EMD和相关系数的希尔伯特振动分解滚动轴承检测方法····12 6.11基于奇异谱分析和连续隐马尔可夫模型的故障诊断方法········12 6.12基于改进的固有时间尺度分解和鲁棒回归变量预测模式诊断····13 6.13基于多尺度模糊熵变预测模型的滚动轴承故障诊断方法········13 7本文思路及内容安排···········································13

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关于滚动轴承故障检测与诊断方法的研究

第二章 滚动轴承故障检测与诊断

1系统设计与滚动轴承故障信息获取·······························14 2原始数据零均值化处理·········································14 2.1数据零均值化的意义·······································14 2.2时域中零均值化效果·······································14 2.3频域中零均值化效果·······································15 3滚动轴承故障诊断与检测分析方法·······························16 3.1时域分析法···············································16 3.1.1时域特征值提取·······································18 3.1.2时域特征值归一化处理·································18 3.1.3时域特征比较·········································21 3.2频域分析法···············································21 3.2.1频域特征提取·········································24 3.2.2频域特征值归一化处理·································26 3.2.3频域特征比较·········································28 4滚动轴承故障诊断与检测模式识别·······························29 4.1 BP神经网络··············································30 4.2输入层、输出层和隐层的设计·······························31 4.3 BP神经网络的识别和测试··································31 4.3.1数据预处理···········································31 4.3.2神经网络识别·········································32 4.3.3神经网络测试·········································35 5误差分析与综合评价···········································35 5.1方案设计与误差分析·······································35 5.2综合评价·················································35 6方案优化与能力提升思考·······································36 6.1针对本文方案的优化·······································36 6.1.1故障信息获取手段的优化·······························36 6.1.2故障特征提取手段的优化·······························36 6.1.3故障模式识别手段的优化·······························38 6.2对成分复杂的原始振动信号进行分析·························38 6.3当需要精确判断故障发生的位置时···························38 第三章 结束语 参考文献························································39 附录

MATLAB程序代码··············································41

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关于滚动轴承故障检测与诊断方法的研究

第一章 研究背景

一、进行滚动轴承故障检测与诊断的背景与意义 1.1滚动轴承故障检测与诊断领域背景

通过查阅文献[1]相关案例,可以很容易地得到一种结论：随着工业的发展进步，旋转机械日益向集成化、大型化、高速化和智能化的方向发展。不仅设备内部各部分密切相联，不同的设备之间也存在着紧密联系，多个设备在运行中构成一个完整复杂的系统。设备的某一部位一旦发生故障，将可能产生一系列连锁反应，导致整个系统故障，影响正常的生产和产品质量，造成巨大的经济损失和严重的人员伤亡。近几十年来，某些高技术、大型化的设备，因零部件故障而引发的灾难性的事故时有发生。 机械故障诊断就是为解决上述问题与防止事故发生而得以产生和发展的一门交叉学科。通过监控机械设备的运行状态，对存在的异常或故障作出诊断，提供解决的方案，以指导机械设备维修与维护。

在文献[2]当中，笔者任帅提到，据统计，状态监测与故障诊断可使设备的维修费用减少 25%到 50%，因故障停机的时间减少 75%，经济效益显著。工业发达国家的经验认为，90%的设备需要进行预知性维修，仅有 10%的设备需要进行定期维修，这样可以有效提高设备的利用率，降低维修成本。

谈到旋转机械，就不得不提滚动轴承。它是各种旋转机械中最常见最易损坏的零部件之一，起传递运动和承受轴向与径向载荷的作用。滚动轴承通过内部元件之间的滚动接触来支撑转子，具有易启动、摩擦小、润滑简单和更换方便的优点，广泛用于精密仪器、航空航天、汽车、机床、机器人等领域。根据结构和所能承受载荷类型的不同，滚动轴承可以分为滚珠轴承和滚柱轴承这两类。文献[2]还指出，随着科技的发展，对轴承可靠性与技术性能的要求越来越高。目前，一些高速轴承的 DN 值可达(轴承内径(mm)×转速(r/min))可达 3～4×10^6mm·r/min，因滚动体的疲劳剥落、保持架打滑等引起的滚动轴承故障时有发生，轻则导致系统精度降低，振动加大，重则导致抱轴和断轴，造成严重事故。据有关资料统计，旋转机械的故障中振动故障占 70%，而 30%的振动故障是由滚动轴承故障引起的。因此，滚动轴承的状态监测与故障诊断理论和应用的研究，一直是旋转机械故障诊断领域的一个重点。滚动轴承在高速、高温条件下工作，并且需要承受轴向和径向载荷，使信号的采集受到很多噪声因素的干扰，造成有效信号的淹没，该现象在故障早期尤为明显。所以通过信号处理方法，分离出滚动轴承的早期故障信号以实现滚动轴承故障的诊断，是当前亟待解决而又未完全解决的问题。

1.2进行滚动轴承故障检测与诊断的意义

总之，滚动轴承是旋转机械中的常用零件，它的机械状态直接影响到机械系统的运行安全，因此，滚动轴承的早期故障诊断具有重要的工程意义。然而滚动轴承的早期故障通常十分微弱，易被噪声信号淹没，由于该难度的存在，一直以来，对滚动轴承故障的诊断的研究就一直没有停止过。

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