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Adaptive multiwavelets via two-scale similarity transforms for rotating machinery fault diagnosis

机译：通过两尺度相似变换的自适应多小波用于旋转机械故障诊断

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摘要

Fault diagnosis of rotating machinery is very important and critical to avoid serious accidents. However, the complex and non-stationary vibration signals with a large amount of noise make the fault detection to be challenging, especially at the early stage. Based on the inner product principle, fault detection using wavelet transforms is to match fault features most correlative to basis functions, and its effectiveness is determined by the construction and choice of wavelet basis function. In this paper, a new method based on adaptive multiwavelets via two-scale similarity transforms (TSTs) is proposed. Multiwavelets can offer multiple wavelet basis functions and so have the possibility of matching various fault features preferably. TSTs are simple and straightforward methods to design a series of new biorthogonal multiwavelets with some desirable properties. Using TSTs, a changeable and adaptive multiwavelet: library is established so as to provide various ascendant multiple basis functions for inner product operation. By the rule of kurtosis maximization principle, optimal multiwavelets most similar to the fault features of a given signal are searched for. The applications to a rolling bearing of outer-race fault and a flue gas turbine unit of rub-impact fault show that the proposed method is an effective approach to detecting the impulse feature components hidden in vibration signals and performs well for rotating machinery fault diagnosis.
机译：旋转机械的故障诊断对于避免严重事故非常重要且至关重要。然而，具有大量噪声的复杂且不稳定的振动信号使得故障检测具有挑战性，尤其是在早期阶段。基于内积原理，使用小波变换的故障检测是为了匹配与基函数最相关的故障特征，其有效性取决于小波基函数的构造和选择。提出了一种基于自适应多小波的两尺度相似变换的新方法。多小波可以提供多个小波基函数，因此最好具有匹配各种故障特征的可能性。 TST是设计一系列具有某些所需特性的新型双正交多子波的简单明了的方法。使用TST，建立了一个可变的自适应多小波库：以便为内部产品操作提供各种上升的多基函数。根据峰度最大化原理的规则，搜索与给定信号的故障特征最相似的最优多小波。在外部故障滚动轴承和烟气涡轮机组碰摩故障中的应用表明，该方法是检测振动信号中隐含的冲动特征分量的有效方法，对于旋转机械故障诊断具有良好的效果。

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  • 来源
    《Mechanical systems and signal processing》 |2009年第5期|1490-1508|共19页
  • 作者

    Jing Yuan; Zhengjia He; Yanyang Zi; Yaguo Lei; Zhen Li;

  • 作者单位

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

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

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

    Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada T6G2G8;

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

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

    adaptive multiwavelets; two-scale similarity transforms; fault diagnosis;

    机译：自适应多小波两尺度相似度转换;故障诊断;

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