Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
An AI controller validated on one Bernoulli-driven separator can become unreliable after a sensor replacement, channel modification, software update, or change in feedwater chemistry. This review defines operational resilience as the ability to detect such loss of validity, continue safely, and recover through controlled revalidation. It proposes a layered validity domain covering hydraulic regime, material state, measurement quality, data preprocessing, model version, and decision authority. Change-point detection and uncertainty monitoring identify emerging drift; a physics-informed digital twin tests whether observed deviations are mechanistically plausible; and a model registry links each alert to the data and configuration on which approval was based. Revalidation triggers are graded rather than binary, ranging from recalibration and shadow evaluation to human takeover or shutdown. The analysis also distinguishes service availability from evidential validity: a running model is not necessarily a trustworthy model. Based on a structured synthesis of the cited sources, the article offers no new field trial. Its contribution is a practical AI assurance scheme in which resilience depends on explicit boundaries, recoverable records, and preassigned responses to change.
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