声发射在机械结构缺陷检测中的应用

发布时间：2018-06-12 07:49

本文选题：声发射 + 疲劳裂纹　；参考：《昆明理工大学》2014年硕士论文

【摘要】：滚动轴承旋转机械中最易损坏的部件之一,其运行状态严重影响整个设备的运行。因此,滚动轴承的状态监测与故障诊断对于工程人员有重要意义。现有检测手段,如振动信号分析、油液分析、温度诊断分析等并不能有效地诊断出关键部位轴承的早期故障。起重机是广泛应用的八大特种设备之一。由于数量巨大,结构复杂,非正常的使用使得安全事故日益频发。起重机的常规无损检测方法既耗时费力,又需要起重机停止作业,最重要的是无法及时发现关键部位的微弱结构缺陷,导致即便起重机检测合格仍然有较大的安全隐患。故亟需解决起重机安全检测的有效性,并对起重机中的结构缺陷源进行粗略定位,再结合其他无损检测手段对可能的缺陷位置进行仔细检测。另外,声发射对于活性缺陷极其敏感,可以对运行中的起重机进行整体性检测。本文以声发射检测技术为手段,对健康滚动轴承和起重机主梁进行了理论和试验研究,着重对滚动轴承和起重机主梁早期疲劳缺陷和声发射检测的有效性进行了详细研究。研究工作主要包括以下四个方面： 1)滚动轴承声发射信号的特征参数及信息熵分析。采用了峭度、有效值、峰值和信息熵等对运行中健康滚动轴承声发射信号的特征进行了分析,并与振动信号进行了对比分析,得出了声发射信号的参数及信息熵趋势能更好地反应当前轴承疲劳磨损状况。 2)通过轴承声发射包络频谱与振动频谱对比分析,证明声发射信号中故障特征频率比振动信号更为突出,也说明声发射信号比振动信号具有更高的信噪比,能更有效地进行状态监测和故障诊断。 3)通过后期对实验采集的起重机主梁声发射数据波形进行仔细分析,结合声发射波形的定义及一些相关的特征参数,从而设定一定的阈值条件,筛选出了可能的裂纹产生及扩展时的声发射特征信号。 4)通过对对这些筛选后的声发射信号进行峭度、有效值、峰值、波峰系数、能量等特征参数分析,结果表明：声发射检测手段可以准确地监测起重机主梁这类大型构件中的疲劳裂纹形成及扩展的整个过程。这里还尝试了缺陷声发射源的定位,但是定位精度不是很高。
[Abstract]:One of the most easily damaged parts in rolling bearing rotating machinery, its running state seriously affects the operation of the whole equipment. Therefore, the condition monitoring and fault diagnosis of rolling bearings is of great significance to engineers. The existing detection methods, such as vibration signal analysis, oil analysis, temperature diagnosis and so on, can not effectively diagnose the early failure of bearings in key parts. Crane is one of the eight widely used special equipment. Due to the large number, complex structure and abnormal use, safety accidents are more and more frequent. The conventional nondestructive testing method of crane is time-consuming and laborious, and it also needs the crane to stop its operation. The most important thing is that the weak structural defects of the key parts can not be found in time, which leads to a great potential safety hazard even if the crane is qualified for inspection. Therefore, it is urgent to solve the effectiveness of crane safety inspection, and to roughly locate the structural defect source in the crane, and then combine other non-destructive testing means to carefully detect the possible defect position. In addition, acoustic emission (AE) is very sensitive to active defects and can be used to detect the whole of crane in operation. In this paper, the healthy rolling bearings and crane main beams are studied theoretically and experimentally by means of acoustic emission detection technology. The effectiveness of early fatigue defects and acoustic emission detection of rolling bearings and crane main beams is studied in detail. The research work mainly includes the following four aspects: 1) characteristic parameters and information entropy analysis of acoustic emission signal of rolling bearing. Using kurtosis, effective value, peak value and information entropy, the characteristics of acoustic emission signals of healthy rolling bearings in operation are analyzed and compared with vibration signals. It is concluded that the parameters and information entropy trend of acoustic emission signals can better reflect the current fatigue wear of bearings. 2) by comparing and analyzing the spectrum of acoustic emission envelope and vibration spectrum of bearings, It is proved that the fault characteristic frequency is more prominent than the vibration signal in the acoustic emission signal, and it also shows that the acoustic emission signal has a higher signal-to-noise ratio than the vibration signal. It can more effectively carry out the condition monitoring and fault diagnosis. 3) through the careful analysis of the acoustic emission data waveform of the crane main beam collected in the later stage, combining the definition of the acoustic emission waveform and some relevant characteristic parameters, By setting certain threshold conditions, the acoustic emission characteristic signals of possible crack generation and propagation are screened. 4) by means of kurtosis, effective value, peak value and peak coefficient of these filtered acoustic emission signals, The analysis of energy and other characteristic parameters shows that the acoustic emission detection method can accurately monitor the whole process of fatigue crack formation and propagation in large components such as crane girder. The localization of defective acoustic emission sources is also tried, but the positioning accuracy is not very high.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TH133.33;TH165.3

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