IJCAS

In order to solve the problem of neural network algorithm for aero-engine’s gas path performance evaluation under high-dimensional evaluation index with non-equal weights, the trend analysis method and fault fingerprints are used to mine engine’s gas path performance characteristic parameters. A comprehensive weighting method based on game theory is proposed to optimize the weight value of each gas path performance characteristic parameter. A discrete feedback neural network with single-layer and binary output is established. The original gas path performance evaluation index is equivalently expanded according to the weight ratio, and the gas path state evaluation indexes with different weights are mapped into higher-dimensional equivalent evaluation indexes with equal weights. The network attractor is designed according to the engine gas path performance evaluation levels, and the design of discrete feedback neural network weights is transformed into multi-objective programming problem, and a particle swarm optimization algorithm with adaptive inertia weight is used to improve the efficiency and global search ability of particle swarm optimization. The experimental results shows that the proposed model and algorithm can provide a scientific and reasonable machine learning method for the evaluation of high-dimensional evaluation index with non-equal weights.

Keywords: Discrete feedback network, gas path performance assessment, inertia weight, multi-source state parameters, particle swarm optimization.