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Optimised Spectral Kurtosis for bearing diagnostics under electromagnetic interference

机译：优化的光谱峰度，用于电磁干扰下的轴承诊断

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摘要

The selection of the optimal demodulation frequency band is a significant step in bearing fault diagnosis because it determines whether the fault information can be extracted from the demodulated signal via envelope analysis. Two well-known methods for selecting the demodulation band are the Fast Kurtogram, based on the kurtosis of the filtered time signal, and the Protrugram, which uses the kurtosis of the envelope (amplitude) spectrum. Although these two methods have been successfully applied in many cases, the authors have observed that they may fail in specific environments, such as in the presence of electromagnetic interference (EMI) or other impulsive masking signals. In this paper, a simple spectral kurtosis-based approach is proposed for selecting the best demodulation band to extract bearing fault-related impulsive content from vibration signals contaminated with strong EMI. The method is applied to vibration signals obtained from a planetary gearbox test rig with planet bearings seeded with inner and outer race faults. Results from the Fast Kurtogram and Protrugram methods are also included for comparison. The proposed approach is found to exhibit superior diagnostic performance in the presence of intense EMI. Another contribution of the paper is to introduce and explain the issue of EMI to the condition monitoring community. The paper outlines the characteristics of EMI arising from widely-used variable frequency drives, and these characteristics are used to simulate an EMI-contaminated vibration signal to further test the performance of the proposed approach. Although EMI has been acknowledged as a serious problem in many industrial cases, there have been very few studies showing its adverse effects on machine diagnostics. It is important for analysts to be able to identify EMI in measured vibration signals, lest it interfere with the analysis undertaken.
机译：最佳解调频带的选择是轴承故障诊断中的重要一步，因为它确定是否可以通过包络分析从解调信号中提取故障信息。选择解调频带的两种众所周知的方法是：基于滤波后的时间信号的峰度的快速Kurtogram和使用包络（振幅）谱的峰度的Protrugram。尽管这两种方法已在许多情况下成功应用，但作者观察到它们可能会在特定环境下失败，例如在存在电磁干扰（EMI）或其他脉冲掩蔽信号的情况下。本文提出了一种基于光谱峰度的简单方法，该方法用于选择最佳解调频带，以从受到强EMI污染的振动信号中提取轴承故障相关的脉冲内容。该方法适用于从行星齿轮箱试验台获得的振动信号，该试验台的行星轴承带有内，外圈故障。快速Kurtogram和Protrugram方法的结果也包括在内以进行比较。发现所提出的方法在存在强烈的EMI的情况下表现出卓越的诊断性能。本文的另一个贡献是向状态监测界介绍并解释了EMI问题。本文概述了由广泛使用的变频驱动器产生的EMI的特性，这些特性用于模拟受EMI污染的振动信号，以进一步测试该方法的性能。尽管在许多工业案例中，电磁干扰已被认为是一个严重的问题，但很少有研究表明其对机器诊断有不利影响。对于分析人员而言，重要的是能够识别出测得的振动信号中的EMI，以免干扰所进行的分析。

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