Abstract
At present, although most fault diagnosis methods of rotating machinery is qualitatively used, it is gravely lacking in quantitative accuracy. So a novel algorithm GA-HPSO combining with the advantages of genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO) was provided to train neural network (NN). The proportion and blend methods were applied to the novel algorithm. Information entropy was used to take fault signals. Four kinds of spectral entropies and six kinds of typical rotor faults were used as input and output data. NN classifier based on GA-HPSO was set up. The simulation results indicate that GA-HPSO has a better ability to escape from a local minimum and is more effective than the conventional single algorithm. It can rapidly and accurately realize fault data classification. It provides a new method for fault diagnosis.
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