A new method of health condition detection for hydraulic pump using enhanced whale optimization-resonance-based sparse signal decomposition and modified hierarchical amplitude-aware permutation entropy

Abstract

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.