A study shows how intelligent monitoring systems enable real-time data collection and predictive maintenance in oil drilling operations. By detecting equipment issues early and supporting data-driven management, the approach reduces safety risks, minimizes downtime, and improves operational efficiency in complex drilling environments.
— Oil drilling operations take place in high-intensity and complex environments where the performance and stability of machinery directly affect operational quality and safety. Many enterprises rely on regular inspections and emergency maintenance, a reactive management model that often fails to identify potential problems in time. Minor equipment issues in long-duration drilling can quickly escalate, causing delays and potential safety risks. Recent research in the Academic Journal of Engineering and Technology Science discusses how intelligent monitoring systems provide real-time equipment data and timely detection of potential problems, enabling preventive maintenance and offering continuous quality assurance and management support for drilling projects.
The study describes how intelligent monitoring systems continuously collect equipment parameters such as temperature, pressure, and vibration through real-time sensing devices. These systems analyze real-time data to identify irregular patterns and anticipate potential failures. Continuous monitoring enables predictive maintenance, helping prevent unplanned downtime, reduce safety risks, and avoid unnecessary maintenance costs. Data-driven monitoring improves visibility into equipment conditions and supports more informed maintenance decisions.
Site management is strengthened by establishing a systematic and scientific management system that incorporates safety, maintenance, and operational requirements. According to the study, managers must consider the balance between equipment carrying capacity and operation intensity to avoid premature failure caused by excessive load. The construction management process relies on scientific decision-making based on field conditions, supported by information management systems that collect and analyze real-time operating data. Real-time monitoring and early warnings strengthen operational safety by flagging issues early and enabling timely intervention. Condition-based maintenance helps extend equipment lifespan and decrease unexpected failures. Together, these measures improve coordination, optimize resource allocation, and support efficient and safe drilling operations.
Contributing author Yuxin Wang holds a Master of Science in Electrical Engineering from the University of Michigan and a Bachelor of Science in Electrical Engineering from Washington State University. His academic training includes experience in circuit design, multi-layer PCB development, data acquisition systems, and modeling work in automotive control. Professionally, Wang has worked as a Data Engineer and Data Analyst, where he developed automated data collection pipelines, integrated multi-source datasets into visualization dashboards, and built analytical tools supporting performance tracking and decision-making. He also has hands-on engineering experience in circuit board design, troubleshooting, and coordinating fabrication processes. These combined technical and analytical capabilities form the foundation of his contributions to the study.
Through the application of intelligent monitoring systems and data-driven preventive maintenance, this research enhances equipment quality management by providing real-time operating data, detecting potential problems in advance, and supporting timely intervention. The study demonstrates that continuous monitoring, early warning mechanisms, and scientifically planned maintenance cycles can reduce production stagnation, lower safety risks, and extend the service life of drilling equipment. By improving transparency in equipment status and enabling more accurate decision-making, these measures strengthen site management and support safer and more efficient drilling operations.
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Name: Yuxin Wang
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Organization: Yuxin Wang
Website: https://scholar.google.com/citations?hl=en&user=MyV3r4gAAAAJ
Release ID: 89180079
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