滚动轴承故障诊断的多参数融合特征提取方法研究

发布时间：2018-03-23 00:01

本文选题：故障诊断　切入点：特征指标　出处：《北京交通大学》2011年硕士论文　论文类型：学位论文

【摘要】：摘要：设备状态监测与故障诊断技术是一种了解和掌握设备在使用过程中的状态,对设备运行的状态异常进行监测并对故障类型进行判断的技术。作者所在团队在服务于神华集团准格尔露天煤矿矿铲设备的故障诊断项目时发现：作为矿用设备代表的矿用电铲,其工作条件有环境恶劣、工况复杂变化,工作模式多样等特点。其中滚动轴承承受的压力最大,长期工作于变转速的状态,最容易发生损坏。因此,研究能够适应变转速条件的轴承故障诊断技术非常必要的。本文的主要研究内容与工作如下：首先,对滚动轴承的失效形式及其故障诊断方法进行了分析,通过分析时域特征参数对于故障信息的敏感程度,提取了峭度指标等作为本文故障诊断的有效特征参数。其次,对轴承转速改变的情况下的振动信号进行分析。因为峭度指标等无法完成振动波形复杂度表征,所以加入了近似熵和功率谱熵作为补充,进而提出了融合多参数的滚动轴承故障特征提取方法。通过求待检测波形相对于特征参数指标的距,确定故障类别和故障程度。此方法不仅能较准确的分辨出故障类型,而且能够避免转速对故障诊断结果的影响,经试验证实是切实可行有效的。再次,分别对三维特征指标距和六维特征指标距的诊断效果做了对比分析,从理论上分析了维数的增加对故障诊断效果的影响并利用试验加以验证。提出了加权优化的办法来提高诊断的可靠性。最后,在实验室中模拟变转速情况下的振动波形,利用本文提出的特征参数指标距的方法进行诊断分析。得到了较好的结果,证明该方法在变转速下对故障诊断的有效性。
[Abstract]:Absrtact: the technology of equipment condition monitoring and fault diagnosis is a kind of understanding and mastering the state of equipment in the process of using. The technique of monitoring the abnormal state of the equipment operation and judging the type of fault. The author found that the equipment was used as a mine equipment while serving the fault diagnosis project of Zhungel opencast mine shovel equipment of Shenhua Group. On behalf of the mine shovel, Its working conditions are abominable, the working conditions are complex and varied, and so on. The rolling bearing bears the greatest pressure, and works in the state of variable speed for a long time, which is the most vulnerable to damage. It is necessary to study the bearing fault diagnosis technology which can adapt to variable speed condition. The main contents and work of this paper are as follows:. Firstly, the failure form of rolling bearing and its fault diagnosis method are analyzed. By analyzing the sensitivity of time domain characteristic parameters to fault information, the kurtosis index is extracted as the effective characteristic parameters of fault diagnosis in this paper. Secondly, the vibration signal under the change of bearing speed is analyzed. Because the kurtosis index can not be used to characterize the complexity of vibration waveform, the approximate entropy and power spectrum entropy are added as a supplement. Furthermore, a multi-parameter fault feature extraction method for rolling bearing is proposed. By calculating the distance between the waveform to be detected and the characteristic parameter index, the fault type and the fault degree can be determined. This method can not only accurately distinguish the fault type, but also the fault type. Moreover, it can avoid the influence of rotating speed on fault diagnosis result, and it is proved to be feasible and effective by experiment. Thirdly, the diagnostic effects of 3D and 6-D feature distance are compared and analyzed respectively. The influence of increasing dimension on fault diagnosis effect is analyzed theoretically and verified by experiments. A weighted optimization method is proposed to improve the reliability of fault diagnosis. Finally, the vibration waveform under the condition of variable rotational speed is simulated in the laboratory, and the method proposed in this paper is used for diagnosis and analysis. Good results are obtained, and the effectiveness of this method for fault diagnosis under variable rotational speed is proved.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH133.33;TH165.3

【引证文献】