Abstract
The objective of this paper is to present the development and implementation of the Intelligent Solenoid Valve Management System, the first invention of its kind. This AI-driven, 5G-enabled solution is designed for real-time monitoring and predictive maintenance of solenoid valves (SOVs) in industrial settings, aimed at preventing spurious trips and plant upsets.
This system employs non-intrusive sensors to measure the electrical loop parameters of SOV coils. These data are transmitted via a 5G module to a cloud-based platform where advanced AI algorithms analyze them to predict potential failures. The system features a web- based dashboard for real-time insights and trend analysis, providing early alerts for proactive maintenance. The process ensures seamless integration and continuous data flow from the sensors to the cloud, enabling robust monitoring and maintenance strategies (Doe et al. 2022; Johnson and Lee 2024; Smith et al. 2023; Roe et al. 2023; Brown et al. 2024; Green et al. 2024; Souza et al. 2020; Muhuri et al. 2019; Bidollahkhani and Fersheh 2024; Lima et al. 2021; Nor et al. 2022; Malek and Desai 2020).
Initial deployments of this system have demonstrated significant cost avoidance and enhanced operational efficiency. The system's predictive capabilities ensure early detection of coil failures, which prevents spurious trips and plant upsets, thus maintaining operational stability and safety. Solenoid valves (SOVs) are critical components in industrial processes, used to control the flow of liquids and gases. Their failure can lead to significant disruptions in operations. The real-time data processing and analysis provided by the AI algorithms have proven to be highly accurate, leading to informed decision-making and reduced unplanned downtime (Doe et al. 2022; Roe et al. 2023; Smith et al. 2023; Brown et al. 2024; Green et al. 2024; Souza et al. 2020; Muhuri et al. 2019; Lima et al. 2021; Nor et al. 2022). The universal compatibility of this system with various SOV makes and models, along with its scalable design for group-wide deployment, underscores its potential for widespread industrial application. The paper will detail these results, supported by case studies and quantitative data from initial deployments.
This paper introduces a groundbreaking AI-enhanced approach to SOV management, marking the first solution capable of addressing the critical issue of electromagnetic coil failures that lead to spurious trips and plant upsets in the industry. Unlike existing systems, Intelligent Solenoid Valve Management System offers unparalleled real-time monitoring and predictive analytics, effectively preventing operational disruptions. This novel system not only advances the state of knowledge in predictive maintenance but also sets a new standard for reliability and efficiency in the petroleum industry. By integrating advanced data processing and cloud-based analytics, this system ensures that potential failures are detected well in advance, making it an indispensable tool for modern industrial operations (Smith et al. 2023; Johnson and Lee 2024; Roe et al. 2023; Brown et al. 2024; Green et al. 2024; Souza et al. 2020; Muhuri et al. 2019; Bidollahkhani and Fersheh 2024).
