Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
Continuous learning can improve monitoring of Bernoulli-driven water separation, yet unconstrained updating can also erase the meaning of an earlier validation. This review focuses on how an adaptive AI system should operate inside a declared validity domain and recognize when it has crossed that boundary. The proposed logic combines mechanism-based residuals with streaming drift indicators: pressure-flow inconsistency, salinity response, actuator behavior, and maintenance events are evaluated jointly rather than reduced to one health score. Minor, well-characterized drift may permit recalibration; novel regimes, missing provenance, or disagreement between physical and learned models trigger shadow mode, abstention, or formal revalidation. The synthesis examines update permissions, frozen safety parameters, rollback, and audit trails so that adaptation remains reversible. It also separates alarms caused by the plant from alarms caused by a changed data pipeline. No online-control experiment is claimed. The article's contribution is a governance-aware monitoring design that treats AI adaptation as a controlled engineering change, with explicit thresholds for continued use, operator intervention, and renewed evidence collection.
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