Equipment Diagnosis Method Based on Hopfield-BP Neural Networks

BP neural network is easily trapped into the local minimum during the training process, which results that it can´t get the optimal solution, even misjudging in device fault diagnosis. Directing to the above problems, a Hopfield-BP neural network fault diagnosis method was proposed, which combined Hopfield neural network, having the global optimal neural network computing ability, with the BP neural network, charactering the nonlinear classification ability. It avoids the network to be trapped to a local optimum. Implementing the new network into the fault diagnosis of centrifugal fan has proven that fault pattern recognition could be solved well, and the accuracy of fault diagnosis is increased than that with the method of BP neural network.