Fracture analysis of a connecting rod for oil pumping unit in China western oilfield

Ding, Hongbin; Xie, Junfeng; Bai, Zhongxiang; Li, Y.; Long, Yuan; Qi, Dongtao; Li, H.B.

doi:10.1016/j.engfailanal.2019.07.021

Cited by 18 publications

(6 citation statements)

References 7 publications

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“…As the pumping unit works downhole, different faults are re ected in the corresponding shape of the power diagram, with which the conventional pumping unit fault analysis can be performed. At present, the overall pumping unit failure is categorized into more than 20 types such as gas impact, sand out of oil well, and leakage of swimming vanes [23][24][25] , and different failures correspond to different shapes of schematic diagrams. For example, pumping rod breakage failure will occur due to fatigue breakage, chemical corrosion, and seizure.…”

Section: Failure Type Analysismentioning

confidence: 99%

A Fourier descriptor and PSCS-RBF fusion method for pumping machine fault diagnosis

Raja

Li³

et al. 2023

Preprint

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For the current oilfield pumping machine fault diagnosis, there are time-consuming and inefficient problems, and at the same time high requirements for hardware resources and no universality, this study proposes a pumping machine fault diagnosis method based on improved Fourier descriptor combined with dynamic adaptive cuckoo search (PS cuckoo search, PSCS) to optimize RBF neural network. Firstly, the starting point position of the contour of the power diagram is determined by the minimum inertia axis, and the Fourier transform is performed to achieve the best matching between contours, and the effect of starting point irrelevance. Then feature extraction is performed by combining shape invariant moments as the input layer information of RBF neural network. Then dynamic discovery probability and adaptive step size are introduced to make the cuckoo search easier to retain the better solution, and the step size can be automatically adjusted according to the convergence rate of the objective function to maintain a balanced state of efficiency and accuracy in different search stages. Finally, the RBF neural network is optimized by the improved cuckoo search to obtain the optimal relevant parameters such as the width and weights of RBF, and the PSCS-RBF fault diagnosis model is established. The model is applied to the diagnosis of different fault types of pumping machines and is compared and analyzed with a variety of current mainstream models. The average detection accuracy of the PSCS-RBF fault diagnosis method proposed in the article reaches 96.3%, and the measured results have high accuracy and short time, which verifies the practical value and advantages of the method.

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Section: Failure Type Analysismentioning

confidence: 99%

A Fourier descriptor and PSCS-RBF fusion method for pumping machine fault diagnosis

Raja

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In Eq (31), f denotes the objective function value of the current best nest, f 0 denotes the objective function value corresponding to the position of the merit-selected nest after the next round of update, and s t i is the step size of the search path in the tth generation. Therefore, the optimized DACS algorithm can be obtained:…”

Section: Plos Onementioning

confidence: 99%

“…As the pumping unit works downhole, different faults are reflected in the corresponding shape of the power diagram, with which conventional pumping unit fault analysis can be performed. At present, the overall pumping unit failure is categorized into more than 20 types, such as gas impact, sand out of oil well, and leakage of swimming vanes [30][31][32], and different failures correspond to different shapes of schematic diagrams. In this experiment, 1278 sets of indicator diagram data of 10 common types of pumping units in Shengli Oilfield were selected as experimental samples.…”

Section: Failure Type Analysismentioning

confidence: 99%

Pumping machine fault diagnosis based on fused RDC-RBF

Li,

Raja,

et al. 2023

PLoS ONE

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At present, the fault diagnosis of pumping units in major oil fields in China is time-consuming and inefficient, and there is no universal problem for high requirements of hardware resources. In this study, a fault fusion diagnosis method of pumping unit based on improved Fourier descriptor (IDF) and rapid density clustering RBF (RDC-RBF) neural network is proposed. Firstly, the minimum inertia axis of the center of gravity of the indicator diagram is obtained. The farthest point of the intersection of the inertial axis and the contour is determined as the starting point. Then Fourier transform is performed on the contour boundary of the graph to obtain the feature vector. Then, combining with the idea of fast density clustering algorithm, the number of hidden layer neurons of RBF is determined by finding the point with the highest density and using it as the hidden layer neuron. At the same time, the characteristics of Gaussian function are introduced to ensure the activity of hidden layer neurons. Finally, through dynamic adaptive cuckoo search (DACS), the step size is automatically adjusted according to the convergence speed of the objective function of RBF, and the efficiency and accuracy of RBF in different search stages are balanced. The optimal parameters such as the width and weight of RBF are determined, and the optimal RDC-RBF fault diagnosis model is established. The model is applied to the diagnosis of different fault types of pumping units, and compared with the current mainstream models. The average detection accuracy of the fusion RDC-RBF fault diagnosis method proposed in this paper reaches 96.3%. The measured results have high accuracy and short time. At the same time, this method is currently applied to oil production sites such as Shengli Oilfield in China, which greatly reduces the human resources required for fault diagnosis of pumping units in the past.

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“…In the process of oil production, a sucker rod is subjected to asymmetric cyclic loads, and the breakage of the sucker rod will seriously affect the production of crude oil and increase the cost of workover operations and the cost of crude oil. The failure parts of a sucker rod are mainly concentrated in the external thread and the thread relief groove, so the processing quality of the thread directly affects the life of a sucker rod [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Measurement of External Thread at the End of Sucker Rod Based on Machine Vision

Wang

2022

Sensors

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Aiming at the low efficiency of manual measurement of threads and the lack of practicability in machine vision measurement before, online size measurement of threads at the end of sucker rods based on machine vision was studied. A robotic arm is used to carry an optical device to achieve high-quality image acquisition of threads. Based on the prior knowledge of the thread profile angle, the directional edge detection operator is customized to achieve the accurate detection of the left and right edges of the thread. Noise filtering, sorting, and left and right edge-matching algorithms based on connected domains are developed to eliminate the interference effects of electrostatic dust and oil pollution in online measurement, and the dimension of thread profile angles, pitches, major diameters, and minor diameters can be precisely calculated. The experimental results show that the screw thread parameter measurement time is about 0.13 s; the maximum and minimum average errors of the thread angles are 0.011° and 0.632°, respectively; and the total average deviation is less than 0.08°. For the screw thread pitch, major diameter, minor diameter, and pitch diameter parameter measurement, the deviation of the measurement results between the proposed method and the universal tool microscope (UTM) method is less than 10 μm. It fully proves the effectiveness and accuracy of the method in this paper and, at the same time, shows that the method has good real-time performance and high application significance, which lays a good foundation for the subsequent online thread measurement.

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Fracture analysis of a connecting rod for oil pumping unit in China western oilfield

Cited by 18 publications

References 7 publications

A Fourier descriptor and PSCS-RBF fusion method for pumping machine fault diagnosis

A Fourier descriptor and PSCS-RBF fusion method for pumping machine fault diagnosis

Pumping machine fault diagnosis based on fused RDC-RBF

Automatic Measurement of External Thread at the End of Sucker Rod Based on Machine Vision

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