基于全息SDP的船舶推进轴系轴承故障诊断研究

Ship propulsion shafting bearing fault diagnosis based on holographic SDP similarity visual recognition

  • 摘要:
      目的  针对船舶推进轴系轴承的故障诊断问题,提出一种基于全息对称点图形(SDP)和相似性识别的可视化诊断方法。
      方法  首先,多方位采集轴承振动信号,全面监测轴承发生故障时的规律性冲击在时域和频域中引起的非平稳性变化特征;然后,基于SDP对称点分布原理,将多个维度信号的时域和频谱融合至同一个二维图形,以放大信号之间的差异性;最后,基于相似性识别方法对轴承进行简易诊断。
      结果  轴承故障实验平台的验证结果表明,该方法可以实现多个信号的有效图形融合,全面展示设备信号的状态特征,从而准确地诊断故障。
      结论  研究成果可为船舶推进轴系轴承的可视化故障简易诊断提供参考。

     

    Abstract:
      Objectives  To address the problem of the fault diagnosis of ship propulsion shafting bearings, this paper proposes a visual diagnosis method based on a holographic symmetrical dot pattern (SDP) and similarity recognition.
      Methods  First, the bearing vibration signals are collected in three directions to comprehensively monitor the non-stationary changes in the time-domain and frequency-domain caused by the regular impact of the bearing faults. Second, based on SDP, multiple one-dimensional time-domain signals and spectrums are merged into a two-dimensional image to amplify the difference between different state signals. Finally, a simple bearing diagnosis is performed based on the similarity recognition method.
      Results  The results of engineering experiments show that this method can achieve the effective graphic fusion of multiple signals, fully display the characteristics of equipment signals and accurately diagnose faults.
      Conclusions  The results of this study can provide valuable references for the visual fault diagnosis of ship propulsion shafting bearings.

     

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