Study on Health Status Classification of Variable Gear Pump Based on GLCT and CPA-SVM

doi:10.3901/JME.2023.14.310

Abstract

Abstract: Aiming at the variable speed condition of gear pump, a gear pump health state classification and recognition method based on general linear chirplet transform(GLCT) and carnivorous plant algorithm(CPA) optimized support vector machines(SVM) is proposed. Firstly, four groups of shaft bushing with different wear amount are selected, and vibration signals of gear pump under different states are collected by the modified test bed. Then, the time-frequency analysis method of GLCT is introduced to eliminate the influence of speed change. The instantaneous rotation frequency is extracted, and the angle domain resampling is carried out. The peak index, pulse index, kurtosis index in Angle domain are extracted, and the root mean square value of order spectrum and the amplitude of order domain are taken as the characteristic parameters. Finally, CPA is introduced to optimize the c and g two parameters of SVM to classify and identify the health status of gear pump. In order to further verify the validity of the algorithm, it is compared with SVM and ELM. The results show that the average accuracy of the classification method proposed can reach more than 93.75%, which can effectively improve the accuracy of classification and recognition.

Key words: gear pump, variable speed, health status assessment, general linear chirplet transform, support vector machines

CLC Number:

TG156

GUO Rui, ZHANG Yinhao, NIU Wenwen, LUO Xiongshuai, CAI Wei, WANG Jianwei, WANG Yuefeng, ZHAO Jingyi. Study on Health Status Classification of Variable Gear Pump Based on GLCT and CPA-SVM[J]. Journal of Mechanical Engineering, 2023, 59(14): 310-319.

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