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Application of multivariate signal analysis in vibration‐based condition monitoring of wind turbine gearbox
International Transactions on Electrical Energy Systems ( IF 2.3 ) Pub Date : 2020-12-30 , DOI: 10.1002/2050-7038.12762
Hogir J. Rafiq 1 , Ghamgeen I. Rashed 2 , M.B. Shafik 2, 3
Affiliation  

The accuracy of fault diagnosis and condition monitoring of mechanical systems depends on the feature extraction of a non‐stationary vibration signals acquired from multiple accelerometer sensors. Extracting fault features of such complex vibration signals is a challengeable task due to the signals masked by an intensive noise. Recently, the multivariate empirical mode decomposition (MEMD) algorithm has been proposed in order to extend empirical mode decomposition (EMD) for the multi‐channel signal and make it suitable for processing multivariate signals. It is found that, likewise, EMD, MEMD is also essentially acting as a dyadic filter bank for the multivariate input signal on each channel. However, different from EMD, MEMD better aligns the same intrinsic mode functions (IMFs) across the same frequency range from different channels, which plays an important role in real‐world applications. However, MEMD still exhibits the degree of mode mixing problem, which affects the accuracy of extracting fault features. In this article, an improved MEMD, namely NAMEMD, is proposed to extract the most meaningful multivariate IMFs by adding uncorrelated white Gaussian noise in separate channels, under certain conditions, to enhance the decomposed multivariate IMFs by minimizing mode mixing problem. After that, a new method is proposed to select the most effective multivariate IMFs related to faults. To optimize the performance of extracting vibration fault features, a proposed noise‐assisted MEMD algorithm is then combined with a competent non‐linear Teager‐Kaiser energy operator, thereby guarantees a superior fault diagnosis performance. To verify the effectiveness of the proposed method, both a synthetic analytic signal and experimental wind turbine benchmark vibration datasets are utilized and tested. The results demonstrate that the proposed approach is suited for capturing a significant fault features in wind turbine multi‐stage gearboxes, thus providing a viable multivariate signal processing tool for wind turbine gearbox condition monitoring.

中文翻译:

多元信号分析在风机齿轮箱振动状态监测中的应用

机械系统的故障诊断和状态监视的准确性取决于从多个加速度传感器获取的非平稳振动信号的特征提取。由于信号被强烈的噪声掩盖,因此提取此类复杂振动信号的故障特征是一项艰巨的任务。最近,为了扩展多通道信号的经验模态分解(EMD),使其适用于处理多元信号,提出了多元经验模态分解(MEMD)算法。发现,同样,EMD,MEMD基本上还充当每个通道上的多元输入信号的二元滤波器组。但是,与EMD不同,MEMD可以更好地在不同通道的相同频率范围内对齐相同的固有模式函数(IMF),在实际应用中起着重要作用。但是,MEMD仍然存在模式混合问题,这会影响提取故障特征的准确性。在本文中,提出了一种改进的MEMD,即NAMEMD,它通过在特定条件下在单独的通道中添加不相关的高斯白噪声来提取最有意义的多元IMF,从而通过最小化模式混合问题来增强分解后的多元IMF。之后,提出了一种新的方法来选择与故障相关的最有效的多元IMF。为了优化提取振动故障特征的性能,然后将提出的噪声辅助MEMD算法与胜任的非线性Teager-Kaiser能量算子相结合,从而确保了出色的故障诊断性能。为了验证所提出方法的有效性,综合分析信号和风力涡轮机基准振动数据集均得到利用和测试。结果表明,所提出的方法适用于捕获风力涡轮机多级齿轮箱中的重要故障特征,从而为风力涡轮机齿轮箱状态监测提供了可行的多元信号处理工具。
更新日期:2021-02-02
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