YANG Jian

College of Urban Railway
Transportation,
Shanghai University of
Engineering Science,

Shanghai 201620, China

XU Bing

YANG Huayong

State Key Laboratory of Fluid Power Transmission and Control,

Zhejiang University,

Hangzhou 310027, China

Abstract: The noise identification model of the neural networks is established for the 63SCY14-1B hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks. The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks. Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump.

Key words: Hydraulic axial piston pump  Neural networks  Noise  Identification  MATLAB