Title :
Fault diagnosis for locomotive bearings based on IPSO-BP neural network
Author :
Bin Lei ; Hailong Tao ; Lijuan Xing
Author_Institution :
Dept. of Mechatronieal, Lanzhou Jiaotong Univ., Lanzhou, China
Abstract :
This paper presents a BP network model based on improved PSO for bearing fault diagnosis. Combining PSO algorithm for global optimization ability with BP neural network advantages of local search, the model effectively prevents the network from a local minimum, and at the same time guarantees the accuracy of diagnosis. Simulation results show that the locomotive bearings have been effectively diagnosed. Compared with the conventional BP neural network model, this method not only improves the convergence speed, but also improves the fault diagnosis accuracy.
Keywords :
backpropagation; condition monitoring; fault diagnosis; locomotives; machine bearings; mechanical engineering computing; neural nets; particle swarm optimisation; IPSO-BP neural network; fault diagnosis; global optimization; improved particle swarm optimization; local search; locomotive bearings; Biological neural networks; Fault diagnosis; Optimization; Particle swarm optimization; Standards; Training;
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-1743-6
DOI :
10.1109/ICACI.2012.6463279