Title : Logic vision: an AI-driven platform for human–AI collaboration in control and safety logic design and simulation.
Abstract:
The increasing complexity of control and safety systems in upstream oil and gas facilities creates a higher risk of logic inconsistencies, delayed validation and costly rework during Factory Acceptance Testing and commissioning. Traditional Functional Logic Diagram and Cause and Effect development processes frequently depend on manual engineering practices, document-based reviews and simulation performed late in the project lifecycle. This paper introduces Logic Vision, an AI-driven engineering platform that enables human–AI collaboration for early-stage logic design, continuous validation, real-time simulation and automated generation of engineering deliverables.
Logic Vision is a browser-based platform developed by the author to integrate control and safety logic design, simulation and validation into a unified workflow accessible through a company network or offline environment. Engineers construct logic using modular components, including Boolean gates, voting arrangements such as 2oo3, timers, interlocks, sequence functions and PID control elements. These components are aligned with recognised industrial control and functional safety practices, including IEC 61131 and IEC 61508 principles.
An AI-assisted Logic Checker continuously evaluates the design, detects inconsistencies and guides engineers towards established engineering practices. The platform can generate Functional Logic Diagrams from Cause and Effect matrices, truth tables and Structured Boolean Logic. Its real-time simulation engine dynamically propagates input signals through the logic, allowing engineers to verify output responses, test failure scenarios and identify conflicts before formal testing.
The human–AI workflow transforms static logic documentation into an interactive and feedback-driven engineering process. The application of Logic Vision demonstrated improved design efficiency, enhanced traceability of input–output relationships and earlier identification of logic conflicts and failure scenarios. Automated generation of Cause and Effect matrices, truth tables and associated documentation also reduced repetitive manual effort and the potential for human error.
Logic Vision provides a practical foundation for future intelligent engineering capabilities, including automated logic synthesis, advanced design recommendations and AI-supported operational troubleshooting. By combining human engineering judgement with continuous AI-assisted validation, real-time simulation and automated deliverable generation, the platform provides a practical pathway towards safer, faster and more reliable control and safety logic engineering in oil and gas operations.
