In the oil drilling project, especially based on the "daily fee" contract model, the quality management of machinery and equipment and site monitoring are the key factors for the success of the project. Drilling operations are affected by a variety of variables, such as geological environment, operating parameters, owner requirements and equipment performance, which often lead to the accumulation of quality hazards and safety risks. The operation status of equipment not only directly affects the efficiency and safety of drilling operations, but also determines the market competitiveness and brand image of enterprises in the long run. How to realize real-time monitoring and dynamic management of equipment quality through intelligent monitoring technology has become the core challenge to improve project execution effect and guarantee project quality. The intelligent monitoring system provides accurate equipment operation data and timely detection of potential problems through data-driven analysis, enabling preventive maintenance and optimized management decisions, minimizing risk and wastage, and providing continuous quality assurance and efficient management support for projects. The application of this method not only improves the science and accuracy of site management, but also provides a practical reference for the intelligent transformation of the oil drilling industry.

Yuxin Wang. Quality Control and Preventive Maintenance Site Management of Oil Drilling Machinery Equipment Based on Intelligent Monitoring. Academic Journal of Engineering and Technology Science (2025) Vol. 8, Issue 1: 50-55. https://doi.org/10.25236/AJETS.2025.080108.

[1] Liu F, Li Y. Intelligent monitoring system of oil tank liquid level based on infrared thermal imaging [J]. IEEE Transactions on Industrial Informatics, 2023. DOI:10.1117/12.2672730.

[2] Sun T, Mei X, Sun X, et al. Real-Time Monitoring and Control of the Breakthrough Stage in Ultrafast Laser Drilling Based on Sequential Three-Way Decision [J]. IEEE Transactions on Industrial Informatics, 2023, 19:5422-5432. DOI:10.1109/TII.2022.3165302. 

[3] Wan Q, Chen L, Zhu M. A reliability-oriented integration model of production control, adaptive quality control policy and maintenance planning for continuous flow processes[J]. Computers & Industrial Engineering, 2023, 176: 108985. DOI:10.1016/j.cie.2023.108985.

[4] Jin W. Monitoring and Management of Green Information in Water Ecological Environment Based on Sensors and Big Data [J]. Measurement: Sensors, 2024, 34. DOI:10.1016/j.measen.2024.101255.