Research of a hybrid training approach for induction motor fault diagnosis
Chen Zai-pingb · Journal of tianjin University of Technology · 2008
Considering the difficulty in fault diagnosis due to the complexity between the fault symptom and fault pattern of induction motor and nonlinearity in actual system,a hybrid training approach(MABPM),combined with magnified network gradient function(MGF) and adaptive learning rate backpropagation with momentum(ABPM),is adopted to construct the fault diagnosis model of induction motor based on neural network.When compared with the algorithms of standard backpropagation with momentum,ABPM,Polak-Ribiere conjunction gradient and RPROP,MABPM has better abilities of stable generalization and global convergence.The average fault diagnosis accuracy is enhanced compared with other algorithms,which shows promising diagnosis effectiveness.