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Multiwavelet transform and its applications in mechanical fault diagnosis - A review

机译：多小波变换及其在机械故障诊断中的应用

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摘要

Mechanical fault diagnosis is important to reduce unscheduled machine downtime and avoid catastrophic accidents. It is significant to extract incipient fault and compound fault features as early as possible, which is a complex and challenging task that requests advanced analytical methods with high reliability, high accuracy and high efficiency. Compound fault features are mutually coupled in dynamic signals from the complex system. Weak features of incipient faults are always submersed in background noises. Multiwavelet transform is a remarkable development of wavelet transform, which uses vector scaling functions and wavelet functions. Multiwavelets possess the property of orthogonality, symmetry, compact support and high vanishing moments simultaneously. These advantages promote the development of multiwavelets and their applications in mechanical fault diagnosis in the past decades. This paper attempts to summarize the recent development of multiwavelet transform and its applications in mechanical fault diagnosis. First, the history of wavelets and multiwavelets is introduced. Second, the necessity and the overview of preprocessing methods for multiwavelets are summarized. Third, the advantages of multiwavelets and improvements of different generation multiwavelets are addressed. Fourth, different algorithms of these multiwavelet transforms and their flow charts are presented. Fifth, engineering applications of multiwavelets in mechanical fault diagnosis are investigated. This review also describes a simulation experiment and three application examples which provide a better understanding of different generation multiwavelets for compound fault detection. Finally, existent problems and prospects of further researches are discussed. It is expected that this review will construct an image of the contributions of different generation multiwavelets and link the current frontiers with engineering applications for readers interested in this field.
机译：机械故障诊断对于减少计划外的机器停机时间并避免灾难性事故很重要。尽早提取初期故障和复合故障特征非常重要，这是一项复杂而具有挑战性的任务，需要具有高可靠性，高精度和高效率的先进分析方法。复合故障特征在复杂系统的动态信号中相互耦合。初期故障的弱点总是淹没在背景噪声中。多小波变换是小波变换的显着发展，它使用矢量缩放函数和小波函数。多小波同时具有正交性，对称性，紧凑支撑和高消失矩的特性。这些优点在过去几十年中促进了多小波的发展及其在机械故障诊断中的应用。本文试图总结多小波变换的最新发展及其在机械故障诊断中的应用。首先，介绍小波和多小波的历史。其次，总结了多小波预处理方法的必要性和概况。第三，解决了多小波的优点以及不同世代多小波的改进。第四，介绍了这些多小波变换的不同算法及其流程图。第五，研究了多小波在机械故障诊断中的工程应用。这篇综述还描述了一个仿真实验和三个应用示例，这些示例可以更好地理解用于复合故障检测的不同世代多小波。最后，讨论了存在的问题和进一步研究的前景。希望这篇综述将构建不同时代的多小波贡献的图像，并将当前的前沿与工程应用联系起来，以供对该领域感兴趣的读者使用。

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  • 来源
    《Mechanical systems and signal processing》 |2014年第2期|1-24|共24页
  • 作者

    Hailiang Sun; Zhengjia He; Yanyang Zi; Jing Yuan; Xiaodong Wang; Jinglong Chen; Shuilong He;

  • 作者单位

    State Key Laboratory for Manufacturing and Systems Engineering, School of Mechanical Engineering, Xian Jiaotong University, Xi'an 710049, PR China,Beijing Institute of Astronautical Systems Engineering, Beijing 100076, PR China;

    State Key Laboratory for Manufacturing and Systems Engineering, School of Mechanical Engineering, Xian Jiaotong University, Xi'an 710049, PR China;

    State Key Laboratory for Manufacturing and Systems Engineering, School of Mechanical Engineering, Xian Jiaotong University, Xi'an 710049, PR China;

    State Key Laboratory for Manufacturing and Systems Engineering, School of Mechanical Engineering, Xian Jiaotong University, Xi'an 710049, PR China;

    State Key Laboratory for Manufacturing and Systems Engineering, School of Mechanical Engineering, Xian Jiaotong University, Xi'an 710049, PR China;

    State Key Laboratory for Manufacturing and Systems Engineering, School of Mechanical Engineering, Xian Jiaotong University, Xi'an 710049, PR China;

    State Key Laboratory for Manufacturing and Systems Engineering, School of Mechanical Engineering, Xian Jiaotong University, Xi'an 710049, PR China;

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  • 正文语种 eng
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  • 关键词

    Multiwavelets; Mechanical fault diagnosis; Customized multiwavelets; Multiwavelet denoising;

    机译：多小波;机械故障诊断;定制的多小波;多小波去噪;

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