AI-Enabled Predictive Calibration Intervals and Drift Detection for NIST-Traceable Instrumentation in Safety-Critical Energy Infrastructure

Abstract

This study develops and evaluates an AI-enabled framework for setting predictive calibration intervals and detecting measurement drift while preserving the evidentiary requirements of metrological traceability, measurement uncertainty, and human technical authorization in safety-critical energy infrastructure. The research integrates calibration-history analytics, gradient-boosted quantile regression, conformal predictive intervals, reliability modeling, consequence-aware scheduling, and sequential residual monitoring. A chronological simulation of 4,200 calibration cycles representing pressure, temperature, flow, voltage, current, and timing/phasor instrumentation was used to avoid presenting synthetic outcomes as field observations. The median time-to-out-of-tolerance model achieved a mean absolute error of 3.99 months under stationary future validation and 5.80 months after an imposed 25% latent drift-rate shift, compared with 9.85 months for an instrument-class median baseline. Static 90% conformal intervals achieved 90.8% coverage before shift but fell to 84.5% after shift; a rolling correction restored coverage to 89.8%. A risk-constrained policy using the lower predictive bound and a two-month safety guard reduced simulated out-of-tolerance exposure to 4.40% while averaging 3.46 calibrations per 36 months, compared with 5.48% exposure and four calibrations under a fixed nine-month policy. Extended analyses examine class-specific reliability envelopes, feature ablation, uncertainty-budget composition, safety-guard sensitivity, and the calibration burden–risk Pareto frontier. Residual monitoring using interpretable EWMA and CUSUM statistics demonstrates how model surveillance can provide a second layer of assurance between formal calibrations. The results support a governance architecture in which AI remains advisory: traceability is established by documented calibration chains and uncertainty statements, while predictive analytics only informs interval review within externally imposed technical, regulatory, functional-safety, and cybersecurity constraints. The study provides a reproducible academic framework for future validation using real calibration histories from power-generation, grid, nuclear, and process-energy environments.