The normal operation of the hydraulic pump is the significant premise for the stable and dependable working of hydraulic equipment. Consequently, this research comes up with a health condition detection method of hydraulic pump. First of all, this approach selects resonance-based sparse signal decomposition (RSDD) to adaptively disintegrate vibration signals. The biggest problem of the RSDD algorithm is the requirement to artificially set a large number of key parameters, such as quality factor Q, weight coefficient A, and Lagrange operator u. The improper parameter settings will seriously affect the decomposition performance. To overcome this shortcoming, an enhanced whale optimization algorithm is presented to search the best parameter combination of the RSDD. The algorithm takes the correlation kurtosis as the optimization objective function to adaptively disintegrate the signal into low and high resonance components. Moreover, on the basis of the modified analytic hierarchy process and the amplitude-aware permutation entropy, the modified hierarchical amplitude-aware permutation entropy is raised for measuring the complexity of the measured time series more accurately and comprehensively. After that, a health condition detection method for hydraulic pump based on enhanced whale optimization-resonance-based sparse signal decomposition and modified hierarchical amplitude-aware permutation entropy is raised. Finally, through the usage of the hydraulic pump vibration data, this method is compared with other approaches. According to the experimental results, the raised method can identify the fault type more effectively, which is capable of offering a feasible idea for the health condition detection of hydraulic equipment.

液压泵的正常运行是液压设备稳定可靠工作的重要前提。因此，本研究提出了一种液压泵健康状况检测方法。首先，该方法选择基于共振的稀疏信号分解（RSDD）来自适应地分解振动信号。RSDD算法最大的问题是需要人为设置大量的关键参数，如品质因子Q、权重系数A、拉格朗日算子u. 参数设置不当会严重影响分解性能。为了克服这个缺点，提出了一种增强的鲸鱼优化算法来搜索 RSDD 的最佳参数组合。该算法以相关峰态为优化目标函数，自适应地将信号分解为低、高共振分量。此外，在修正层次分析法和幅度感知排列熵的基础上，提出修正层次幅度感知排列熵，以更准确、更全面地度量被测时间序列的复杂度。之后，提出了一种基于增强鲸鱼优化-基于共振的稀疏信号分解和改进的分层幅度感知排列熵的液压泵健康状况检测方法。最后，通过液压泵振动数据的使用，将该方法与其他方法进行了比较。实验结果表明，提出的方法能够更有效地识别故障类型，为液压设备的健康状况检测提供了一种可行的思路。