Aiming at a single fault diagnosis method can′t meet the actual need

a classification model were proposed

which based on rough set-genetic algorithm-neural network algorithm

to come true excavator fault diagnosis classification. Firstly

the attributes were reduced using rough set theory to choose neural network′s input parameters

which reduced the work and calculation time. Then

in order to solve the shortcoming in the back propagation algorithm

such as trapping to the local minimum and slowness in training speed

genetic algorithm was integrated to optimizing the BP network parameters. Finally

the model carried on the training by the reduction results and the optimized BP network parameters. The experimenta1 result shows the effectiveness of the new proposed model.