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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part C. Journal of mechanical engineering science >Fault diagnosis of hydraulic piston pumps based on a two-step EMD method and fuzzy C-means clustering
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Fault diagnosis of hydraulic piston pumps based on a two-step EMD method and fuzzy C-means clustering

机译：基于两步EMD方法和模糊C均值聚类的液压柱塞泵故障诊断

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摘要

Hydraulic piston pumps are commonly used in aircrafts and various other equipment, and efficient fault diagnosis of them is playing an important role in improving the reliability and performance of hydraulic systems. Given that the discharge pressure signal of piston pump is a quasi-periodic signal and contains variety of state information, this article proposes a fault diagnosis method combining a two-step empirical mode decomposition (EMD) method based on waveform matching and extrema mirror extension with fuzzy C-means clustering. Based upon discharge pressure signals of piston pumps, the two-step EMD method which can restrain the end effects of traditional EMD is adopted to decompose the original signal. Characteristic vectors are then constructed by computing the normalized characteristic energy of selected Intrinsic Mode Function (IMF) components on the basis of local Hilbert marginal energy spectrum. Finally, fuzzy C-means clustering algorithm is used to identify the faults of pumps. Experimental results indicate that the proposed method can identify the faults of pumps effectively.
机译：液压活塞泵通常用于飞机和各种其他设备，对其进行有效的故障诊断在提高液压系统的可靠性和性能方面起着重要的作用。鉴于活塞泵的排气压力信号是准周期信号并且包含各种状态信息，本文提出了一种基于波形匹配和极值镜扩展的两步经验模态分解（EMD）方法结合故障诊断方法。模糊C均值聚类。根据活塞泵的排气压力信号，采用可抑制传统EMD端效应的两步EMD方法对原始信号进行分解。然后，通过基于局部希尔伯特边际能谱计算选定的本征函数（IMF）分量的归一化特征能量来构建特征向量。最后，采用模糊C均值聚类算法识别泵的故障。实验结果表明，该方法可以有效地识别泵的故障。

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