Advancing Automation Frameworks for Safety and Compliance in Offshore Operations and Manufacturing Environments
Abstract
Advancing safety, regulatory compliance, and operational reliability is a critical challenge in offshore operations and advanced manufacturing environments. This paper proposes an innovative automation framework that integrates IoT devices and predictive analytics to address these challenges effectively. The framework is structured into three layers: data acquisition, data processing, and actionable insights, enabling real-time monitoring, predictive maintenance, and proactive decision-making. Key components such as sensors, machine learning algorithms, digital twins, and robust communication infrastructure provide the foundation for its functionality. Applications in offshore platforms and manufacturing systems demonstrate significant benefits, including improved hazard detection, minimized downtime, and enhanced compliance. The paper concludes with recommendations for adopting the framework, emphasizing stakeholder collaboration, investment in enabling technologies, and workforce training. Future research opportunities, such as integrating AI for autonomous decision-making and expanding the framework to other industries, are also identified. This study underscores the transformative potential of advanced automation frameworks in improving safety, efficiency, and sustainability in critical industrial operations.
Keywords
Automation Framework, IoT, Predictive Analytics, Offshore Operations, Advanced Manufacturing, Safety and Compliance
Citations
IRE Journals:
Chukwuemeka Chukwuka Ezeanochie , Samuel Olabode Afolabi , Oluwadayomi Akinsooto
"Advancing Automation Frameworks for Safety and Compliance in Offshore Operations and Manufacturing Environments" Iconic Research And Engineering Journals Volume 5 Issue 11 2022 Page 299-307
IEEE:
Chukwuemeka Chukwuka Ezeanochie , Samuel Olabode Afolabi , Oluwadayomi Akinsooto
"Advancing Automation Frameworks for Safety and Compliance in Offshore Operations and Manufacturing Environments" Iconic Research And Engineering Journals, 5(11)
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