A Layered Framework for AI-Managed Power and Industrial Systems
Keywords:
IPredictive Resilience Modelling, AI-Governed Control Systems, Operational Continuity Management, Multilayer Predictive Monitoring, Failure Mode Mitigation, Decision Boundary Stability, Infrastructure Disruption Prevention, Resilience Data Engineering, Least-Intrusive Automation, Power Generation Resilience.Abstract
Predictive resilience modelling underpins operational continuity in industrial and power generation infrastructures. These systems can sustain stable operation under a range of disruptions because the likelihood of the most foreseeable failure modes is continuously monitored and, where necessary, mitigated before it materializes.
Two complementary models clarify the discussion of AI-governed resilience. The first, a data architecture layer, focuses on resilience-enhancing feature engineering — identifying and structuring the data signals most relevant to anticipating risk. The second, a multilayer predictive model, monitors key risk drivers across layers, with each layer designed to detect and avert specific, predictable operational breaches or failures.
AI-governed control operates at a further level: it determines the minimum degree of automated intervention and external oversight needed to preserve operational resilience. Predictive models define the decision boundaries of operational stability — the range within which internal and external shocks remain manageable and do not threaten system integrity. As long as operations remain within these boundaries, automated actions do not compromise the reliability of the broader safety and control system; instead, they support the operability, responsiveness, and continuity of all other planned interventions, while remaining as minimally intrusive as possible.
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