基于RBF神经网络的液压位置伺服系统故障诊断

针对液压系统的非线性、时变、流固耦合的特点,提出双级径向基函数(Radial Basis Function,RBF)神经网络模型实现液压伺服系统故障检测与定位.采用第1级RBF网络作为液压伺服系统的故障检测滤波器,通过实际系统与RBF观测器输出的残差实现液压伺服系统故障检测.利用第1级RBF观测器的输出残差和网络结构参数,应用第2级RBF网络实现液压伺服系统典型故障定位.针对K均值聚类算法收敛速度慢的缺点,提出了改进K均值聚类算法和学习速率自适应调整算法,利用网络优化结构参数和学习率,加快神经网络收敛速度,减少运算量.实验结果表明,利用双级RBF神经网络能够有效地检测出液压位置伺服系统的故障,并能实现系统的故障定位.

Fault Diagnosis in a Hydraulic Position Servo System Using RBF Neural Network

Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function(RBF)neural network model is proposed to realize the failure detection and fault localization. The first-stage RBF neural network is adopted as a failure observer to realize the failure de- tection. The trained RBF observer, working concurrently with the actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, rebuilds the system states, and estimates ac- curately the output of the system. By comparing the estimated outputs with the actual measurements, the resid- ual signal is generated and then analyzed to report the occurrence of faults. The second-stage RBF neural net- work can locate the fault occurring through the residual and net parameters of the first-stage RBF observer. Considering the slow convergence speed of the K-means clustering algorithm, an improved K-means clustering algorithm and a self-adaptive adjustment algorithm of learning rate are presented, which obtain the optimum learning rate by adjusting self-adaptive factor to guarantee the stability of the process and to quicken the con- vergence. The experimental results demonstrate that the two-stage RBF neural network model is effective in detecting and localizing the failure of the hydraulic position servo system.