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首页> 外文期刊>Mechanical systems and signal processing >Informative frequency band selection in the presence of non- Gaussian noise - a novel approach based on the conditional variance statistic with application to bearing fault diagnosis
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Informative frequency band selection in the presence of non- Gaussian noise - a novel approach based on the conditional variance statistic with application to bearing fault diagnosis

机译：非高斯噪声存在的信息频段选择 - 一种基于条件方差统计的新方法，其应用于承重故障诊断

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摘要

In this paper a novel approach to local damage detection in the presence of non-Gaussian impulsive noise is introduced. The proposed approach is applied to bearing damage detection in technological processes of ore defragmentation. From the signal processing perspective, this corresponds to the identification of cyclic and non-cyclic impulses in the vibrations. A new informative frequency band selector based on the conditional variance statistic is proposed and studied in details. In particular, it is shown that the proposed method is superior to many common alternatives based e.g. on Kurtosis or Alpha selectors, especially when non-cyclic impulses dominate over the cyclic ones. Moreover, it is shown that the approach based on conditional variance is simple to implement and much more robust concerning various problem specifications like cyclic to non-cyclic impulse number ratio or amplitude ratio. The Monte Carlo method is used to validate the robustness of the method in the statistical sense.
机译：本文介绍了在存在非高斯冲动噪声存在下局部损伤检测的新方法。所提出的方法适用于矿石碎片整理技术过程中的轴承损伤检测。从信号处理透视中，这对应于振动中的循环和非循环脉冲的识别。提出了一种基于条件方差统计的新型信息频带选择器，并详细研究。特别地，示出了所提出的方法优于许多基于许多常见的替代方案。在kurtosis或α选择器上，特别是当非循环脉冲占循环脉冲时。此外，示出了基于条件方差的方法是简单的，并且关于循环与非循环脉冲数比或幅度比的各种问题规范更具稳健。 Monte Carlo方法用于验证统计学中方法的稳健性。

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