信息与控制2017，Vol.46Issue(6)：698-705,8.DOI:10.13976/j.cnki.xk.2017.0698
基于思维进化算法和BP神经网络的电动潜油柱塞泵故障诊断方法
Failure Diagnosis Method for Electric Submersible Plunger Pump Based on Mind Evolutionary Algorithm and Back Propagation Neural Network
摘要
Abstract
We propose a failure diagnosis method for an electric submersible plunger pump on the basis of the mind evolutionary algorithm (MEA) and the back propagation (BP) neural network to solve the problem of high failure rate and short pump inspection period of the electric submersible plunger pump.This method can effectively diagnose failure accidents to prolong the pump inspection cycle.To solve the problem of minimal historical fault data and fault data that do not consider the electric submersible plunger pump in the actual production process,an experimental platform that can simulate the working condition of the electric submersible plunger pump is established.First,we simulate different failure states of the electric submersible plunger pump on the experimental platform,and the operating parameters are measured by using a multi-parameter acquisition module fixed at the bottom of the pump and wellhead instruments.Then,the most representative parameters are extracted from those relative operating parameters to structure the failure feature vectors and sample set.We use the sample set to train and validate the failure diagnosis model.Finally,the effectiveness of the fault diagnosis method is verified by the fault data set of the electric submersible plunger pump obtained from the actual production process.Experimental results show that this failure diagnosis method can diagnose the failure states of electric submersible plunger pump accurately and avoid failure accidents.Thus,this method can prolong the pump inspection cycle of the electric submersible plunger pump effectively.关键词
电动潜油柱塞泵/检泵周期/模拟/神经网络Key words
electric submersible plunger pump/inspection cycle/simulation/neural network分类
信息技术与安全科学引用本文复制引用
于德亮,李妍美,丁宝,任玉龙,齐维贵..基于思维进化算法和BP神经网络的电动潜油柱塞泵故障诊断方法[J].信息与控制,2017,46(6):698-705,8.基金项目
黑龙江省自然科学基金资助项目(E201305) （E201305）
