Failure detection of aeroengine based on process neural network with double hidden-layers

Process neural network (PNN) with double hidden-layers model was proposed to detect aeroengine failure. The network can deal with the time-varied signals. The hidden layer of process neuron executes time aggregation operation while the hidden layer of generic neuron raises the mapping capability of the network to complex relation between the system input and output. The network was compared with recurrent neural network (RNN) by predicting exhaust gas temperature (EGT). The results exhibit good convergence and high accuracy of the network and the predictive capability is superior to RNN. This provides an effective way for aeroengine failure detection.