Rock Drill Robot's Electro-hydraulic Control System Fault Diagnosis Using PNN Neural Network
Aiming at the problem of low fault diagnosis efficiency of the electro-hydraulic control system of rock drill truck,this paper proposes a fault diagnosis method combining fault tree analysis and probabilistic neural network(PNN).First of all,based on the structure and working principle of the electro-hydraulic control system,a fault tree model is constructed,then the fault tree model is qualitatively analyzed to determine the minimum cut-off set and the typical fault types,and the fault sign matrix is constructed with the key parameters of the selected typical fault types,which is trained and computed by the PNN neural network,to realize the automatic identification of the typi-cal fault states of the system.The experimental results show that the average diagnosis time of this paper's method is 1.2s,and the average diagnosis accuracy rate is 80％,which can quickly and accurately locate the system faults,and can satisfy the engineering practical needs of the fault diagnosis of the electro-hydraulic control system of rock drill cart.
rock drill robotelectro-hydraulic control systemfault treePNN neural network algorithmfault diagnosis
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