计算机技术与发展2012，Vol.22Issue(4)：29-32,36,5.
基于EMD和改进PSO-Elman神经网络的液压故障诊断
Hydraulic System Fault Diagnosis Based on EMD and Modified PSO-Elman ANN
摘要
Abstract
Measuring the each element parameters of engineering machinery hydraulic system .extract the eigenvector containing fault information , and apply in neural network fault diagnosis. Experience mode decomposition ( EMD) is used to extract fault characteristic vectors in it,combined with the pressure, temperature, flow rate of dominant signal as neural network's inputs. In addition, it improves the Elman neural network learning algorithm by PSO algorithm,it can effectively increase network convergence rate and computing power. The particle swarm is used to optimize Elman neural network weights and the threshold value ,and then applied in the fault diagnosis system by training the network. The simulation results show that this method increases the neural network convergence rate and reduces diagnosis error.关键词
经验模态分解/粒子群算法/Elman神经网络/故障诊断Key words
experience mode decomposition/PSO algorithm/Elman neural network/ fault diagnosis分类
信息技术与安全科学引用本文复制引用
张晓宇,董增寿,宋仁旺..基于EMD和改进PSO-Elman神经网络的液压故障诊断[J].计算机技术与发展,2012,22(4):29-32,36,5.基金项目
国家自然基金项目(41140026) （41140026）
太原市科技局大学生创新创业专项(110148052,110148020) （110148052,110148020）
