FRINK Linked Data Fragments server

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• W4387235536 abstract "Abstract This paper presents a comprehensive system to improve and enhance the field operator rounds through digitalization of routine inspection and operation tasks which are typically performed manually in most facilities. The paper will outline a combination of various systems that were developed and integrated to digitize critical gauges, level indicators and other relevant instrument data recorded for process monitoring and detecting any abnormalities. Additionally, the gauge digitization process was enabled through the use of computer vision and artificial intelligence. A robust architecture was developed that features an AI-based mobile application running on a tablet, a backend remote server that publishes on a frontend dashboard. The AI based application utilizes a tablet’s camera to digitize analog gauges and record other critical instruments. Starting by the commonly used circular analog gauges, the mobile application uses advanced computer vision techniques to predict the readings in real-time. The server was developed with a database to store the digitized data collected through the application. The dashboard was developed to analyze and visualize the data through interactive charts for monitoring, analytics, and forecasting. The developed systems were deployed in an oil and gas facility such that the current manual process was replaced by the proposed technologies presented in the paper. The system has now been deployed for 5 months and is being used on a daily basis by the facility operators. A total of approximately 25,000 records have been collected and stored in the developed backend server. Through this system the operators were enabled to take measurements and readings in challenging scenarios such as acute viewing angles, hard to reach areas, distant measurements, various lighting conditions without sacrificing data collection integrity. The AI prediction algorithm managed to predict readings of industrial analog gauges achieving accuracies greater than 90%. The collected data by the operators was visualized effectively on the dashboard providing management and engineers a mechanism to extract and verify field process measurements as needed. Finally, the implementation of the system has replaced the manual paper-based recording process fostering sustainable technological solutions. Due to the constraints of accessing cloud services in oil and gas facilities, the AI algorithm was optimized to run on an edge device such as tablet with limited computational capabilities. Moreover, the AI algorithm introduces a reliable and systematic way of data collection that eliminates randomness and normalizes human bias. Additionally, Analog gauges digitization process enables automatic cross-validation with other digital transmitter twins which can be used to detect instruments malfunctioning and process upset." @default.
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• W4387235536 date "2023-10-02" @default.
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• W4387235536 title "AI-Powered Mobility Application for Field Inspection Rounds" @default.
• W4387235536 doi "https://doi.org/10.2118/216996-ms" @default.
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