In the process of immune algorithm training

the affinity propagation(AP) clustering and entropy weight method were introduced

the training samples were clustered and weighted

and the weights were introduced into the calculation of the sample selection threshold in the immune algorithm to solve the problem of a fixed selection threshold in the training process

which led to over fitting of the detector in a partial area

and under-fitting of the partial area. Result showed that

when the improved immune algorithm was used for the optimization of typical nonlinear functions

the iterative performance was better than the traditional immune algorithm. In most cases it was better than the particle swarm optimization algorithm and the quantum genetic algorithm

in the case of a certain type of engine fault diagnosis. The improved algorithm had a diagnostic accuracy of 98.06%

which was higher than 92.60% of the traditional immune algorithm.