基于随机森林的离心泵滚动轴承故障诊断

Fault diagnosis of antifriction bearing of centrifugal pump based on random forest

    摘要
  • 摘要:
      目的  离心泵组出现故障后会给所在平台任务完成及战备完好性的提升带来较大影响。为解决离心泵组常见的故障检测与定位问题，提出一种离心泵滚动轴承故障诊断方法。
      方法  首先使用局部特征尺度分解（LCD）对滚动轴承信号进行自适应分解，然后提取分解后各内禀模态分量（ISC）的样本熵作为故障特征，并利用随机森林对离心泵滚动轴承进行故障诊断，最后结合故障诊断试验，基于离心泵组中所注入的轴承故障的监测数据分析验证上述方法的正确性。
      结果  试验结果表明，该方法能有效诊断出离心泵滚动轴承的故障模式。
      结论  对离心泵组相应故障诊断方法的研究可为提高机电设备诊断能力奠定基础，为泵组故障预测与健康管理系统的建立提供技术支持。

     

    Abstract:
      Objectives  The fault of centrifugal pump unit would influence the missions' accomplishment and the improvement of operational readiness. In order to solve the problem of detection and location of common faults of centrifugal pump units, a fault diagnosis method of antifriction bearing of centrifugal pump is presented.
      Methods  the vibration signal of the antifriction bearing was decomposed adaptively with the method of local characteristic-scale decomposition(LCD). The random forest was used for fault diagnose based on the sample entropy generated by the intrinsic scale component(ISC)extracting. Finally, combined with the fault diagnosis test, the correctness of the above method was verified based on the monitoring data of bearing fault injected into the centrifugal pump unit.
      Results  The results show that the diagnose method based on LCD and random forest can effectively diagnose the fault mode of the antifriction bearing of centrifugal pump.
      Conclusions  The study in this paper can lay a foundation for the improvement of equipment fault diagnosis capability, and provide technical support for the establishment of centrifugal pump fault prediction and health management system.

     

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