As the core power source of the hydraulic transmission system, hydraulic pump has been widely used in various industrial machinery and national defense equipment. It is of great significance to explore the monitoring of the health status of the hydraulic pump. It is also necessary to extract the effective components in condition monitoring of the hydraulic pump. In this work, based on fast empirical mode decomposition (FEMD) and relative entropy, a novel method is proposed for extracting the effective components of the signal. The original signal can be automatically separated by FEMD, and the useful components of the signal can be obtained via the measurement of relative entropy. Through the validation of the numerical experiment and measured data, the results indicate that the method presents good ability in the useful component extraction for signals with multi-frequency vibration. It provides an effective solution for the reduction of the interference of useless signals, including the direct current component and noise. The desired useful signals are also accurately reconstructed.

中文翻译：

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基于快速经验模态分解和相对熵的液压泵压力信号有效分量提取

液压泵作为液压传动系统的核心动力，已广泛应用于各种工业机械和国防设备中。探索液压泵的健康状况监测具有重要意义。在液压泵的状态监控中还必须提取有效成分。在这项工作中，基于快速经验模式分解（FEMD）和相对熵，提出了一种提取信号有效成分的新方法。原始信号可以通过FEMD自动分离，并且可以通过测量相对熵来获得信号的有用成分。通过数值实验和实测数据的验证，结果表明，该方法具有较好的提取多频振动信号有用成分的能力。它为减少无用信号（包括直流分量和噪声）的干扰提供了有效的解决方案。期望的有用信号也被准确地重建。