Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection
Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection
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DOI：
10.1016/j.ymssp.2016.05.036
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发表时间：
2017-01-01
影响因子：
8.4
通讯作者：
Zhao, Qing
中科院分区：
文献类型：
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作者：
McDonald, Geoff L.;Zhao, Qing
最小熵反卷积（MED）方法已成功地应用于旋转机械振动故障检测，但该方法存在局限性。本文提出了MED定义和解决方案的卷积调整，以解决信号开始时的不连续性-在某些情况下会导致虚假脉冲被错误地去卷积。MED解决方案的一个问题是它是一个迭代选择过程，并且不一定会为所提出的问题设计最佳滤波器。此外，MED中的问题目标更倾向于对单个脉冲进行去卷积，而在旋转机器故障中，我们期望故障元件的每个旋转周期有一个类似脉冲的振动源。最大相关峰度反卷积被提出来解决其中的一些问题，尽管它解决了多个周期性脉冲的目标，但它仍然是所提出问题的迭代非最优解决方案，并且仅解决了连续的有限脉冲集。理想情况下，问题目标应该将脉冲序列作为输出目标，并且应该以非迭代方式直接求解最优滤波器。为了满足这些目标，我们提出了一种非迭代反卷积方法称为多点最优最小熵反卷积调整（MOMEDA）。MOMEDA提出了一种以无限脉冲序列为目标的反卷积问题，可以直接求解最优滤波器解。从齿轮箱上的实验数据与齿轮齿的芯片，我们表明，MOMEDA和它的反卷积频谱之间的脉冲周期可以用来检测故障和研究的健康旋转机械元件有效。(C)2016爱思唯尔有限公司版权所有
Minimum Entropy Deconvolution (MED) has been applied successfully to rotating machine fault detection from vibration data, however this method has limitations. A convolution adjustment to the MED definition and solution is proposed in this paper to address the discontinuity at the start of the signal - in some cases causing spurious impulses to be erroneously deconvolved. A problem with the MED solution is that it is an iterative selection process, and will not necessarily design an optimal filter for the posed problem. Additionally, the problem goal in MED prefers to deconvolve a single-impulse, while in rotating machine faults we expect one impulse-like vibration source per rotational period of the faulty element. Maximum Correlated Kurtosis Deconvolution was proposed to address some of these problems, and although it solves the target goal of multiple periodic impulses, it is still an iterative non-optimal solution to the posed problem and only solves for a limited set of impulses in a row. Ideally, the problem goal should target an impulse train as the output goal, and should directly solve for the optimal filter in a non-iterative manner. To meet these goals, we propose a non-iterative deconvolution approach called Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). MOMEDA proposes a deconvolution problem with an infinite impulse train as the goal and the optimal filter solution can be solved for directly. From experimental data on a gearbox with and without a gear tooth chip, we show that MOMEDA and its deconvolution spectrums according to the period between the impulses can be used to detect faults and study the health of rotating machine elements effectively. (C) 2016 Elsevier Ltd. All rights reserved.