Fault diagnosis model for engines based on principal component analysis and integrated neural network

Aimed at the complicated non-linear relationship between the emission parameters of the engine and their faults, a fault diagnosis model for engines based on principal component analysis (PCA) and integrated neural network was developed. First, PCA was used to reduce the dimension of the input numbers. Second, all samples were classified into different groups by the operation condition of engine, and then were trained on the sub-networks. Lastly, the different diagnosis conclusions resulted from different sub-networks were combined and outputted by voting method. Experimental results demonstrate that the model can simplify the training samples and their characteristic parameters, optimize the structure of the network, and improve the diagnosis precision, and study ability is superior to unitary neural network model, which can solve the problems of the former neural network with the shortcomings of too large scale networks, slow training speed, low diagnosis precision, etc..