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An Enhanced Factor Analysis of Performance Degradation Assessment on Slurry Pump Impellers

机译:泥浆泵叶轮性能下降评估的增强因素分析

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摘要

Slurry pumps, such as oil sand pumps, are widely used in industry to convert electrical energy to slurry potential and kinetic energy. Because of adverse working conditions, slurry pump impellers are prone to suffer wear, which may result in slurry pump breakdowns. To prevent any unexpected breakdowns, slurry pump impeller performance degradation assessment should be immediately conducted to monitor the current health condition and to ensure the safety and reliability of slurry pumps. In this paper, to provide an alternative to the impeller health indicator, an enhanced factor analysis based impeller indicator (EFABII) is proposed. Firstly, a low-pass filter is employed to improve the signal to noise ratios of slurry pump vibration signals. Secondly, redundant statistical features are extracted from the filtered vibration signals. To reduce the redundancy of the statistic features, the enhanced factor analysis is performed to generate new statistical features. Moreover, the statistic features can be automatically grouped and developed a new indicator called EFABII. Data collected from industrial oil sand pumps are used to validate the effectiveness of the proposed method. The results show that the proposed method is able to track the current health condition of slurry pump impellers.
机译:泥浆泵,例如油砂泵,在工业上被广泛使用,以将电能转换为泥浆势能和动能。由于不利的工作条件,渣浆泵叶轮容易磨损,可能导致渣浆泵故障。为防止任何意外故障,应立即进行渣浆泵叶轮性能下降评估,以监控当前的健康状况并确保渣浆泵的安全性和可靠性。在本文中,为了提供一种替代叶轮健康指标的方法,提出了一种基于增强因子分析的叶轮指标(EFABII)。首先,采用低通滤波器来提高渣浆泵振动信号的信噪比。其次,从滤波后的振动信号中提取冗余统计特征。为了减少统计特征的冗余,执行增强因子分析以生成新的统计特征。此外,统计功能可以自动分组并开发一个称为EFABII的新指标。从工业油砂泵收集的数据用于验证所提出方法的有效性。结果表明,该方法能够跟踪泥浆泵叶轮的当前健康状况。

著录项

  • 来源
    《Shock and vibration》 |2017年第1期|1524840.1-1524840.13|共13页
  • 作者单位

    City Univ Hong Kong, Smart Engn Asset Management Lab SEAM, Dept Syst Engn & Engn Management, Kowloon Tong, Hong Kong, Peoples R China;

    City Univ Hong Kong, Smart Engn Asset Management Lab SEAM, Dept Syst Engn & Engn Management, Kowloon Tong, Hong Kong, Peoples R China;

    City Univ Hong Kong, Smart Engn Asset Management Lab SEAM, Dept Syst Engn & Engn Management, Kowloon Tong, Hong Kong, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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