基于EMD和改进PSO-Elman神经网络的液压故障诊断

摘要

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.%通过对工程机械液压系统各个元件的参数测量,提取包含故障信息的特征向量,并应用神经网络进行故障诊断.文中将经验模态分解(EMD)应用到故障特征向量提取中,结合压力、温度、流量等显性信号作为神经网络的输入,并对Elman神经网络的学习算法用PSO算法进行了改进,以提高网络的收敛率和计算能力.使用粒子群算法对Elman神经网络的权值和阈值进行优化,经过训练后即可应用到故障诊断系统中.仿真结果表明该方法提高了神经网络的收敛率,减小了诊断误差.