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Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

机译:基于小波自回归模型和主成分分析法的齿轮多故障诊断虚拟样机及实验研究

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摘要

Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate.is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and experimental studies.
机译:齿轮系统是广泛用于各种工业应用中的基本要素。由于传动机械故障的大约80%是由齿轮故障引起的,因此,早期故障检测和准确故障诊断的效率对于正常的机械运行至关重要。综述文献表明,只有有限的研究考虑了齿轮故障的诊断,尤其是对于单个,耦合的分布式和局部故障。通过虚拟样机仿真分析和实验研究,提出了一种齿轮多故障诊断的新方法。该新方法是基于小波变换(WT)技术,自回归(AR)模型和主成分分析(PCA)的集成而开发的,用于故障检测。在研究中,WT方法被用作处理原始振动信号的降噪技术。与基于时间同步平均值(TSA)的噪声消除方法相比,WT技术可以直接对原始振动信号执行,而无需计算测试齿轮振动信号的任何总体平均值。更重要的是,WT可以在一个操作中处理齿轮对的耦合故障,而TSA必须执行几次才能进行多个故障检测。虚拟样机仿真的分析结果表明,与TSA相比,该方法是一种更省时,更有效的耦合故障检测方法,故障分类率优于基于TSA的方法。在实验测试中,将该方法与马氏距离法进行了比较。然而,后者对于齿轮多故障诊断而言效率低下。它的缺陷检测率低于60%,远低于所提出的方法。此外,虚拟模型仿真和实验研究都证明了AR模型处理局部齿轮故障和分布式齿轮故障的能力。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2011年第7期|p.2589-2607|共19页
  • 作者单位

    Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China,Key Laboratory of Marine Power Engineering and Technology (Ministry of Transportation), Wuhan University of Technology, Wuhan 430063, China;

    Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China,Key Laboratory of Marine Power Engineering and Technology (Ministry of Transportation), Wuhan University of Technology, Wuhan 430063, China;

    Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China,Key Laboratory of Marine Power Engineering and Technology (Ministry of Transportation), Wuhan University of Technology, Wuhan 430063, China;

    School of Engineering & Physical Sciences, James Cook University. Townsville, Qld. 4811, Australia;

    Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang 443002, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    gear fault diagnosis; virtual prototype; wavelet ar model; pca;

    机译:齿轮故障诊断;虚拟原型;小波AR模型聚氯乙烯;

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