Abstract
In the conventional sucker rod pumping system, the pumping unit often be produced many types of faults that due to the influence of sucker rod, pump, and other accessories, as well as oil well paraffinication, gas interference, sand production and other environmental impacts. Using indicator diagram to analyze the fault diagnosis of pumping units is a common method. In this paper, a lightweight model was designed based on the classical convolutional neural network, and a comparative experiment was used to optimize the model from four perspectives: learning rate, convolution kernel size, batch size, and optimization algorithm. Finally, the average accuracy achieved 95.5%.
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Acknowledgments
This research work was supported by National Oil and Gas Drilling Equipment Engineering Technology Research Center Open Fund 'Emergency rescue wellhead demolition and reconstruction equipment hydraulic system fire prevention' (BOMCO-J118-JKY010-2022).
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CH contributed to methodology, investigation, writing—original draft. XZ contributed to investigation, simulation. CF and JZ contributed to investigation, writing—review, editing.
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Han, C., Zhou, X., Fan, C. et al. Research on Fault Analysis Model of Lightweight Pumping Unit Based on Classical Convolutional Neural Network. J Fail. Anal. and Preven. 23, 2402–2415 (2023). https://doi.org/10.1007/s11668-023-01776-8
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DOI: https://doi.org/10.1007/s11668-023-01776-8