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首页> 外文期刊>Shock and vibration >Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm
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Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm

机译：基于跟踪比率准则的核判别分析的二进制免疫遗传算法在滚动轴承故障诊断中的应用

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摘要

The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems. Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems. This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA) for fault diagnosis of rolling element bearings. The binary immune genetic algorithm (BIGA) is employed to solve the trace ratio problem in TR-KDA. The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA) can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings. Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data. The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings.
机译：滚动轴承是飞机，火车，汽船和机床等许多系统的核心组件，它们的故障会导致性能下降，停机甚至发生灾难性故障。由于操作不当，制造缺陷或缺乏监视和维护，通常发现它是这些系统中最不可靠的组件。因此，滚动轴承的有效而有效的故障诊断对于确保其主机系统的持续安全可靠运行具有重要作用。这项研究提出了一种基于跟踪比率准则的核判别分析（TR-KDA），用于滚动轴承的故障诊断。采用二进制免疫遗传算法（BIGA）解决TR-KDA中的痕量比问题。使用大量模拟获得的数值结果表明，使用BIGA提出的TR-KDA（称为TR-KDA-BIGA）可以有效，高效地对滚动轴承数据的不同类别进行分类，同时还提供了非常直观的实时可视化功能。对于从业人员监视滚动轴承的健康状况很有用。经验比较表明，在对不同类型的滚动轴承数据进行分类时，提出的TR-KDA-BIGA的性能优于现有方法。提出的TR-KDA-BIGA可能是用于滚动轴承故障诊断的有前途的工具。

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  • 来源
    《Shock and vibration》 |2016年第3期|8631639.1-8631639.15|共15页
  • 作者

    Yang Wen-An; Xiao Maohua; Zhou Wei; Guo Yu; Liao Wenhe; Shen Gang;

  • 作者单位

    Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing 210016, Jiangsu, Peoples R China;

    Nanjing Agr Univ, Coll Engn, Nanjing 210031, Jiangsu, Peoples R China;

    Nanjing Surveying & Mapping Instrument Factory, Nanjing 210003, Jiangsu, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing 210016, Jiangsu, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing 210016, Jiangsu, Peoples R China;

    China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, 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
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