Advanced AI Techniques for Safety and Risk Evaluation in High-Hazard Engineering Systems

Abstract

The safety and risk analytics developed through AI have become more and more crucial to the operation of high-hazard engineering systems with complicated functions and close dependencies between the elements and harsh outcomes in case of failure. These systems, which cut across various industries, including oil and gas, nuclear power, chemical processing, air transportation, and vital infrastructure, produce vast amounts of heterogeneous sensor and control systems data and operational log data. The artificial intelligence methods, such as machine learning, deep learning, and probabilistic modeling, make it possible to make a more sophisticated analysis of this data to facilitate proactive risk detection, real-time anomaly detection, and predictive maintenance. Using the AI-based analytics as the part of the safety management systems, the organizations can shift to the proactive risk elimination instead of the reactive response to the incidents, enhancing the reliability of the systems and their resilience to the operational risks. Nonetheless, the issues of data quality, model transparency, system integration, and regulatory compliance remain to have an impact on adoption. This paper will analyze the uses, capabilities, and constraints of the AI-based safety and risk analytics in the high-hazard engineering systems, pointing out how these tools contribute to better decision-making and safer industry processes.