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Study on Application of WCPSO Optimizing Wavelet Neural Network for Gear Box Fault Diagnosis
LIU Fen;PAN Hong-xia
2011, 31 (5):
146-149.
DOI: 10.3969/j.issn.1006-1355-2011.05.034
Aiming at the problem of complex vibration signal and the difficulty to predict the fault type of gearboxes, this paper proposes a wavelet neural network optimization algorithm (WCPSO) for fault diagnosis of the gearboxes. This algorithm is a particle swarm optimization based on the dynamic acceleration constant coordinating with inertia weight. The diagnosis result of the WCPSO optimizing wavelet neural network is compared with that of the traditional wavelet neural network. It is concluded that this method can obviously improve the accuracy and raise the convergence speed, and has high recognition rate for multi-fault symptoms. Thus, it is an effective method for fault diagnosis.
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