AI Safety Systems and Risk Analytics for High-Hazard Engineering Systems

Abstract

Engineering systems with high hazards (nuclear power plants, oil and gas installations, aerospace
platforms, and large-scale chemical facilities) are used under the conditions when a failure may
have disastrous outcomes. The conventional safety assurance and risk evaluation techniques,
although functional, tend to be constrained by the fact that they use static modeling assumptions
and that they respond slowly to dynamic, complex operating conditions. This paper evaluates how
artificial intelligence (AI) safety systems and more sophisticated risk analytics can be used to
improve the prevention, detection, and mitigation of hazards in engineering fields with high safety
standards. It examines how machine learning, probabilistic reasoning and data-driven anomaly
detection can come together with the classic risk assessment models, such as fault tree analysis,
HAZOP and reliability engineering models, to allow predictive and adaptive safety management.
The abstract also brings to the fore AI-enabled system architectures that utilize real-time sensor
information, digital twins, and intelligent decision support systems to enhance situational
awareness and operational resilience. The critical issues concerning the model validation,
explainability, human-AI interaction, and regulation compliance are also discussed. Altogether, the
paper highlights how AI-based safety and risk analytics can be used to shift the engineering
systems characterized by high risks toward the reactive safety paradigm to the proactive and
anticipatory risk governance paradigm.