Fault Diagnosis of Sucker Rod Pump Based on Deep-Broad Learning Using Motor Data

Jingliang Wei,Xianwen Gao

doi:10.1109/access.2020.3036078

Jingliang Wei, Xianwen Gao

Open Access

https://doi.org/10.1109/access.2020.3036078

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Abstract

Conventional fault diagnosis methods of sucker rod pump (SRP) mainly focus the operating status of oil well by identifying the dynamometer cards (DCs), which are limited by the sensor maintenance and calibration, battery replacement and safety hazards for staff. Motor power, as the most basic parameter providing the energy source for the oil well, is directly related to the real-time operation state of oil well. Therefore, a novel deep and broad learning system (DBLS) based on motor power data for fault diagnosis of sucker rod pump is proposed in this paper. Considering the key parameters such as mechanical wear and balance weight, the motor power data are labeled by the DCs with typical working conditions. Furthermore, CNN-based feature extractor is designed to make up for the lack of expert experience in motor power, which is obtained by merging the output of the CNNs with the manual features extracted based on mechanical analysis. And then the broad learning system is employed as the classifier to solve the problem of real-time update of system structure. Finally, a dataset containing six different working states collected from the oilfield by a self-developed device is employed to verify the proposed method experimentally and compared with other methods.

Highlights

S UCKER rod pump(SRP) is the major artificial lift device employed by substantially more than 80 percent oil wells worldwide
Conventional fault diagnosis methods are based on dynamometer card (DC) data measured by load sensor installed on the horse-head
PROPOSED METHOD The overview of the proposed fault diagnosis system is illustrated in Fig.4, which consists of task-specific feature extractor based on convolutional neural network (CNN) and fault diagnosis model based on broad learning system (BLS)

Summary

Introduction

S UCKER rod pump(SRP) is the major artificial lift device employed by substantially more than 80 percent oil wells worldwide. During the oil extraction processing, many fault states such as mechanical structure damage and unstable reservoir supply may occur because of the downhole portion of the equipment often operates in poor conditions thousands of meters underground. These fault states will seriously affect the production efficiency of the SRPs and even cause production safety risks when they are not diagnosed in time. Conventional fault diagnosis methods are based on dynamometer card (DC) data measured by load sensor installed on the horse-head. Zheng et al [11] explored the characteristic parameters of the typical fault DCs, and employed the hidden Markov model for sucker rod pump system diagnosis. The existing longterm fixed dynamometer card test method has several defects

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