基于GoogLeNet的多级液压缸故障诊断方法OA北大核心CSTPCD

Aiming at the problem that the fault modes of hydraulic telescopic cylinder are complicated and it is difficult to realize accurate diagnosis,a fault diagnosis method is proposed based on GoogLeNet neural network for hydraulic telescopic cylinder.The working principle of hydraulic telescopic cylinder during the stretching process is taken as starting point to establish its dynamic model.Then,a simulation model containing multiple fault modes is constructed by which fault signals are obtained in different states.On this basis,some key fault features of hydraulic telescopic cylinder are extracted,and the GoogLeNet neural network is applied to establish a fault diagnosis model to realize fault diagnosis and fault localization.The simulation and experimental results show that the simulation model of hydraulic telescopic cylinder is compatible with the actual situation.In addition,the proposed fault diagnosis method is effective in accurately identifying different fault modes of hydraulic telescopic cylinder,thus providing an important guidance for the maintenance and repair.