Research of Fault Diagnosis for Motor Based on RBF Neural Network and Wavelet Packet - WANG Fan-zhong; TIAN Mu-qin (Gong-kuang zidonghua, 2011)
In order to solve the problem that it is difficult to extract fault characteristic signals and make an accurate fault prediction exsisted in traditional fault diagnosis for motor, a diagnosis method of motor faults based on RBF neural network and wavelet packet was put forward. The method can extract energy of special frequency bands of vibration signals of typical faults of bearing, rotor and insulation of motor with wavelet package analysis technology, and serve the energy as a group of vector to be input of RBF network for diagnosing motor fault. The experiment and simulation results showed that the method is very effective to diagnose motor, which has positive significance to find early fault and maintain for motor.