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Construction of customized redundant multiwavelet via increasing multiplicity for fault detection of rotating machinery

机译：通过增加多样性构建定制的冗余多小波，用于旋转机械故障检测

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摘要

Fault detection from the vibration measurement data of rotating machinery is significant for avoiding serious accidents. However, non-stationary vibration signal with a large amount of noise makes this task challenging. Multiwavelet not only owns the advantage on multi-resolution analysis but also can offer multiple wavelet basis functions. So it has the possibility of detecting various fault features preferably. However, the fixed basis functions which are not related to the given signal may lower the accuracy of fault detection. Moreover, another major intrinsic deficiency of multiwavelet lies in its critically sampled filter-bank, which causes shift-variance and is harmful to extract the feature of periodical impulses. To overcome these deficiencies, a new method called customized redundant multiwavelet (CRM) is constructed via increasing multiplicity (1M). 1M is a simple method to design a series of changeable multiwavelet which are available for the subsequent optimization process. By the rule of the envelope spectrum entropy minimum principle, optimal multiwavelet is searched for. Based on the customized multiwavelet filters, the filters of CRM can be calculated by inserting zeros. The proposed method is applied to analyze the simulation, gearbox and rolling element bearing vibration signals. Compared with some other conventional methods, the results demonstrate that the proposed method possesses robust performance in detecting fault features of rotating machinery.
机译：从旋转机械的振动测量数据中进行故障检测对于避免严重事故具有重要意义。然而，具有大量噪声的非平稳振动信号使该任务具有挑战性。多小波不仅具有多分辨率分析的优势，而且可以提供多种小波基函数。因此，有可能优选地检测各种故障特征。但是，与给定信号无关的固定基函数可能会降低故障检测的准确性。此外，多小波的另一个主要内在缺陷在于其严格采样的滤波器组，这会导致频移变化，并且不利于提取周期性脉冲的特征。为了克服这些缺陷，通过增加多重性（1M）构造了一种称为定制冗余多小波（CRM）的新方法。 1M是设计一系列可变多小波的简单方法，可用于后续的优化过程。根据包络谱熵最小原理，寻找最优的多小波。基于定制的多小波滤波器，可以通过插入零来计算CRM的滤波器。该方法被用于分析仿真，变速箱和滚动轴承的振动信号。与其他常规方法相比，结果表明该方法在检测旋转机械故障特征方面具有较强的性能。

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