基于GM-ELM的有杆泵抽油井故障诊断

为了解决有杆泵抽油井故障诊断问题，提出了基于灰度矩阵极限学习机(gray matrix-extreme learning machine，GM-ELM)故障诊断方法.首先用灰度矩阵对有杆泵抽油井进行故障特征提取；然后用数理统计的方法建立灰度矩阵的特征向量，将故障特征向量作为故障诊断模型的输入值；最后建立GM-ELM模型对有杆泵抽油井故障进行诊断.仿真结果表明该方法与GRNN(general regression neural network)方法、LS-SVM(least squares support vector machine)方法、BPNN(back propagation neural network)方法相比具有更高的故障诊断准确率.

Fault Diagnosis of Sucker Rod Pumping Wells Based on GM-ELM

Gray matrix-extreme learning machine (GM-ELM) was proposed to solve the fault diagnosis of sucker rod pumping wells. Firstly， the gray matrix method was applied to extract the fault features of sucker rod pumping wells. Secondly， the mathematical method was applied to establish eigenvectors of gray matrix， and the eigenvectors were used as the input value of the fault diagnosis model. Finally， the GM-ELM model was established to diagnose the fault of sucker rod pumping wells. The simulation results indicate that GM-ELM method has higher accuracy of fault diagnosis than GRNN(general regression neural network)， LS-SVM(least squares support vector machine)， BPNN(back propagation neural network).