Intelligent fault diagnosis of cutting part of tunnel boring machine based on PSO-BP composite network
In view of the frequent fault diagnosis of underground tunnel boring machine(TBM)and the long diagnosis period of traditional diagnosis methods and BP neural network,taking EBZ-160TY TBM in Changcun Coal Mine as the back-ground,an intelligent diagnosis model of TBM cutting part based on PSO-BP neural network model is proposed,which can o-vercome the long convergence period of BP neural network model and realize the fast convergence of the model and the ac-curate prediction of fault.By setting PSO-BP neural network model parameters,sample data training,and data testing at the same time,it is determined that the fault prediction rate of the PSO-BP neural network model is 100％,while the prediction accuracy of BP neural network is 80％.Moreover,at the same time,the prediction accuracy of PSO-BP neural network is higher than that of BP neural network.Under the same accuracy,the convergence speed of PSO-BP neural network model is faster.When the accuracy is 1 × 10-5,the PSO-BP neural network model only needs 7 steps,while the average BP neural network needs 198.5 steps.The comprehensive test results show that the PSO-BP neural network model can realize the fas-ter prediction of fault for TBM with higher accuracy,which provides a basis for fault diagnosis of TBM.
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