Research on noise reduction of pressure pulsation signal of axial flow pump based on global optimization parrot algorithm improved variational mode decomposition

Aiming at the problem of difficult extraction of fault features caused by noise interference in pressure pulsation signals of axial pumps, this study proposes a hybrid noise reduction model, GPO-VMD, based on the global optimization parrot algorithm (GPO) and variational mode decomposition (VMD). First, based on the traditional parrot optimization algorithm (PO), the population initialization strategy with hybrid chaotic mapping and inverse learning, the dynamic diversity maintenance with the global exploration mechanism and elastic annealing boundary optimization strategy to improve it, which significantly improves the convergence speed and robustness of the algorithm, and test GPO with other eight algorithms in the test function, and the GPO algorithm is ranked the first with an average ranking of 1.57. Second, using GPO to optimize the input parameters of VMD, the mode decomposition of axial pump pressure pulsation signals is performed, and the signal reconstruction is obtained after noise reduction by screening the intrinsic modal function (IMF) according to correlation analysis. The experimental results show that the noise reduction effect of GPO-VMD is significantly better than the other noise reduction methods mentioned above, and the signal-to-noise ratios of GPO-VMD compared with that of PO-VMD in the three stages of cavitation incipient, critical cavitation and severe cavitation are improved by 5.1%, 4.6%, and 4.5%, respectively; and the root mean square errors are reduced by 8.2%, 6.9%, and 7.4%. In addition, the GPO-VMD model is more efficient in handling different cavitation signals due to the real-time update of the GPO global optimal solution.