Side cloud collaborative diagnosis and early warning system for tower type pumping unit cloud platform

By using an edge-cloud collaborative diagnostic and early warning system, real-time data collection and analysis of tower pumping units' operating data are conducted to establish a fault diagnosis model. This solves the problem of low accuracy in fault diagnosis for tower pumping units, enabling efficient fault identification and early warning, and improving production efficiency and economic benefits.

CN121234071AActive Publication Date: 2025-12-30DAQING PETROLEUM ADMINISTRATION +1
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Patent Information

Application Number
CN202511226451.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-29
Publication Date
2025-12-30
Estimated Expiration
2045-08-29

AI Technical Summary

Technical Problem

In the existing technology, the fault diagnosis method for tower-type pumping units is not effective. The accuracy of fault diagnosis and early warning for tower-type pumping units is low, resulting in decreased production efficiency and increased maintenance costs.

Method used

An edge-cloud collaborative diagnostic and early warning system is adopted. The operating data of the tower pumping unit is collected and preprocessed in real time through the edge module, and the data analysis and neural network training are carried out by the cloud platform module to establish a fault diagnosis model and achieve accurate fault diagnosis of the pumping unit.

Benefits of technology

It improved the accuracy of pumping unit fault diagnosis, avoided safety accidents, extended equipment working time, and improved work efficiency, bringing economic benefits to the oilfield.

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Abstract

The invention relates to the technical field of underground pump fault early warning, in particular to a side cloud collaborative diagnosis early warning system for a tower type pumping unit cloud platform. The system comprises an edge end module and a cloud platform module, wherein the edge end module comprises a data acquisition unit used for acquiring operation data of the tower type pumping unit and a local diagnosis unit used for determining a fault moment; the cloud platform unit comprises a remote diagnosis and decision-making unit used for obtaining a fault response value according to the correlation degree of the same type of operation data and the importance degree of the difference of each type of operation data in an analysis period before and after the fault moment; determining a response characteristic sequence based on the fault response value of the same type of operation data at the fault moment; and training a neural network by using the neural network to obtain an oil pumping unit fault diagnosis model, and further performing diagnosis and early warning on the tower type oil pumping unit. According to the invention, the accuracy of diagnosis and early warning of the fault of the oil pumping unit is effectively improved.
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