Electrical Submersible Pumps Condition Monitoring Using Motor Current Signature Analysis

Popaleny, Peter; Duyar, Ahmet; Ozel, Cumali; Erdogan, Y.

doi:10.2118/193081-ms

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…In addition, in the literature, [9][10][11][12][13] have presented different approaches for condition monitoring in ESP systems. In [9], to investigate the sand wear in ESPs, a sand wear test flow-loop is designed, in which performance degradation, abrasion rate, erosion pattern, and stage vibration of the ESP are recorded.…”

Section: Introductionmentioning

confidence: 99%

“…In [11], principal component analysis (PCA) is utilized for the detection of developing ESP faults and prediction of remaining operating time before failure. Motor current signature analysis is used for the condition monitoring of ESP systems in [12]. There are also patents in the field of condition monitoring of ESP systems based on vibration measurements.…”

Section: Introductionmentioning

confidence: 99%

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Induction Motor Fault Detection in ESP Systems Based on Vibration Measurements

Mohammad‐Alikhani

Vahedi

Khlyupin

2021

Electromechanics and Robotics

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Condition monitoring of electrical submersible pumps (ESPs) is vital since they work in a harsh and confined environment. One of the most important parts of ESP systems is the electric motor which is conventionally the induction motor. Therefore, fast and accurate diagnosis of the motor in the ESP system can cut overhaul expenses and prevent further damages and costs. In ESPs, the vibration of the system is usually measured using the downhole sensors. Accordingly, this paper aims to propose a method for bearing fault detection in the induction motor based on vibration measurements. In this method, first, the best features of the vibration signals are selected using a wrapper approach based on the genetic algorithm. Then, the model is trained using the selected features based on k nearest neighbor classifier. The proposed method is evaluated using data of an experimental set from Case Western Reserve University (CWRU) Bearing Data Center.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Induction Motor Fault Detection in ESP Systems Based on Vibration Measurements

Mohammad‐Alikhani

Vahedi

Khlyupin

2021

Electromechanics and Robotics

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“…Various methods have been developed over the years aiming for the detection of different faults in induction motors. The favourite seems to be the motor current signature analysis (MCSA) [8–11]. The application of this method depends on the monitoring of the motor's current during operation and the analysis of its frequency spectrum via the fast Fourier transform (FFT).…”

Section: Introductionmentioning

confidence: 99%

Detection of simultaneous mechanical faults in 6‐kV pumping induction motors using combined MCSA and stray flux methods

Gyftakis

Panagiotou

Spyrakis

2021

IET Electric Power Appl

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The popularity of stray and air-gap flux monitoring methods is increasing. This trend is justified by several advantages of such methods over the stator current monitoring that has been demonstrated for electrical fault detection in the induction and synchronous machines. However, the use of the magnetic flux for mechanical faults detections has not drawn this much attention, while in industry, the vibration analysis continues to be popular. This study comes to bridge this gap via the detection of mechanical faults of 6-kV induction motors in a pumping station. The diagnostic procedure mainly involves the stator current and stray flux monitoring, and harmonic index analysis. The localisation of the fault has been made possible via oscillometer readings. It will be shown that mechanical faults have a very different impact on the stator current and the flux signals, while the flux is not sensitive to the bearing fault mechanisms. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The use of PCA was also mentioned in the work of Abdelaziz, et al (2017) to predict failure of ESP [9]. The input parameters were also discussed in the research of Popaleny, et al (2018) where the authors presented how ESP mechanical and electrical malfunctions were reflected in the dynamic current spectrum using Motor Current Signature Analysis [10]. In brief, machine learning has been widely used in petroleum industry to predict ESP lifespan in recent years [11].…”

Section: Introductionmentioning

confidence: 99%

Effective Electrical Submersible Pump Management Using Machine Learning

Phạm¹,

Vo²,

Nguyen³

2021

OJCE

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Artificial lift plays an important role in petroleum industry to sustain production flowrate and to extend the lifespan of oil wells. One of the most popular artificial lift methods is Electric Submersible Pumps (ESP) because it can produce high flowrate even for wells with great depth. Although ESPs are designed to work under extreme conditions such as corrosion, high temperatures and high pressure, their lifespan is much shorter than expected. ESP failures lead to production loss and increase the cost of replacement, because the cost of intervention work for ESP is much higher than for other artificial lift methods, especially for offshore wells. Therefore, the prediction of ESP failures is highly valuable in oil production and contributes a lot to the design, construction and operation of oil wells. The contribution of this study is to use 3 machine learning algorithms, which are Decision Tree, Random Forest and Gradient Boosting Machine, to build predictive models for ESP lifespan while using both dynamic and static ESP parameters. The results of these models were compared to find out the most suitable model for the prediction of ESP life cycle. In addition, this study also evaluated the influence factor of various operating parameters to forecast the most impact parameters on the duration of ESP. The results of this study can provide a better understanding of ESP behavior so that early actions can be realized to prevent potential ESP failures.

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Electrical Submersible Pumps Condition Monitoring Using Motor Current Signature Analysis

Cited by 12 publications

References 6 publications

Induction Motor Fault Detection in ESP Systems Based on Vibration Measurements

Induction Motor Fault Detection in ESP Systems Based on Vibration Measurements

Detection of simultaneous mechanical faults in 6‐kV pumping induction motors using combined MCSA and stray flux methods

Effective Electrical Submersible Pump Management Using Machine Learning

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