Identification method of indicator diagram of shock absorber based on BP neural network

DONG Zhi-cheng1， REN Qiang2
（1. Zhejiang Menre Shock Absorber Co.， Ltd.， Deqing 313219， China；
2. Institute of Vehicular Engineering， Zhejiang University of Technology， Hangzhou 310032， China）

Abstract： In order to solve the problems of the artificial judgment in the on-line detection of product performance in the vehicle shock absorber industry，the theory of BP neural network was applied to identification of the indicator diagrams of shock absorber. A three-layer BP neural network used to identify the indicator diagrams was established by means of Matlab. Based on discretizing the tensile and compression parts in the indicator diagram curve，the two eigenvectors of the central distance of tensile and compression，including the two eigenvalues of the satiation degree of tensile and compression，were given. Through training and analysis，the Scaled algorithm of conjugate grads was chosen as the training one for BP neural network. The experimental results indicate that the recognition effect based on the trained BP neural network generally coincides with the judgment of technicians.
Key words： shock absorber；indicator diagram；BP neural network；identification method