重庆大学学报：自然科学版2012，Vol.35Issue(5)：7-13,7.
免疫遗传优化Elman神经网络的旋转机械故障诊断
Rotating machinery fault diagnosis based on Elman neural network optimized by immune genetic algorithm
摘要
Abstract
As it＇s difficult to get comprehensive fault information with traditional machine model in the interrelated process of fault knowledge in rotating machinery fault diagnosis, an immune genetic algorithm （IGA） is proposed to optimize Elman neural network. Fault vibration signals are decomposed into several stationary intrinsic mode functions （IMF） first, then the instantaneous amplitude energy of the IMF which has the fault characteristics are computed and regarded as the input characteristic vector of the Elman neural network optimized by IGA algorithm for fault classification. EMD decomposition adaptively isolates fault vibration signals from original signals. IGA algorithm has more superior performance on global optimization and convergence speed. So it can improve the fault diagnosis accuracy and the adaptive dynamic memory of the Elman neural network. The result of rolling-bearings fault simulation experiments show that, compared with traditional fault diagnosis model, the proposed method significantly improves the diagnostic accuracy and generalization ability of the typical failure of the rolling-bearings.关键词
遗传算法/Elman神经网络/旋转机械/故障诊断Key words
genetic algorithms/Elman neural network/rotating machinery/fault diagnosis分类
机械制造引用本文复制引用
陈法法,汤宝平,黄庆卿..免疫遗传优化Elman神经网络的旋转机械故障诊断[J].重庆大学学报：自然科学版,2012,35(5):7-13,7.基金项目
重庆市自然科学杰出青年基金计划资助项目 （）
重庆市科技攻关计划资助项目 （）
