Fault diagnosis of aero-engine based on self-adaptive neural network

Abstract

As the gas path components’ performance cannot avoid degeneration while the aero-engine is working, the related fault diagnosis technology is of great meaning to the aero-engine’s health management system. Aiming at the aero-engine’s nonlinear model at the design point, this paper optimizes the selection rules of the neural network’s input parameters and output parameters through relative analysis of PCC (Pearson correlation coefficient). For the regular BP (back propagation) neural network’s weakness of an unstable convergence speed and easy to fall into minimum value, this paper designs a new method via self-adaptive neural network that can accurately estimate the degeneration level of the aero-engine. This method combines scale factor and momentum factor, improves the learning speed of network and enhances the confidence level of neural network as well as the generalization ability of engine’s model parameters. The results prove that the self-adaptive neural network method proposed in this paper has better accuracy than the regular BP neural network, and when the training samples are not too many, the training can also produce an ideal network, which guarantees a good accuracy for the fault diagnosis of the aero-engine’s health parameters.