Failure Diagnosis Method for Electric Submersible Plunger Pump Based on Mind Evolutionary Algorithm and Back Propagation Neural Network

YU Deliang; LI Yanmei; DING Bao; REN Yulong; QI Weigui

doi:10.13976/j.cnki.xk.2017.0698

Volume 46 Issue 6

Dec. 2017

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INFORMATION AND CONTROL > 2017 > 46(6): 698-705. > DOI: 10.13976/j.cnki.xk.2017.0698 CSTR: 32166.14.xk.2017.0698

YU Deliang, LI Yanmei, DING Bao, REN Yulong, QI Weigui. Failure Diagnosis Method for Electric Submersible Plunger Pump Based on Mind Evolutionary Algorithm and Back Propagation Neural Network[J]. INFORMATION AND CONTROL, 2017, 46(6): 698-705. DOI: 10.13976/j.cnki.xk.2017.0698

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Failure Diagnosis Method for Electric Submersible Plunger Pump Based on Mind Evolutionary Algorithm and Back Propagation Neural Network

1.
Institute of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150001, China
2.
Daqing Oilfield Personnel Development Institute, Daqing 163000, China
3.
Harbin Institute of Technology, Harbin 150001, China

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Received Date: December 28, 2016
Revised Date: May 14, 2017
Accepted Date: April 18, 2017
Available Online: December 01, 2022
Published Date: December 19, 2017

Graphical Abstract

Abstract

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.
- electric submersible plunger pump,
- inspection cycle,
- simulation,
- neural network

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