Lifecycle Reliability and Translation for Bernoulli-Driven Water Separation: From Laboratory Evidence to Operational Control

Abstract

Reliability evidence for Bernoulli-driven separation is often concentrated at initial performance, whereas operational decisions depend on how hydraulic efficiency, selectivity, contamination, and equipment condition evolve together. This review develops a lifecycle view that connects laboratory measurements to AI-assisted prognostics and control. Time-series models and survival-oriented learning are considered for estimating degradation and maintenance need, but their outputs are anchored to the Bernoulli mechanism and accompanied by uncertainty, data coverage, and failure provenance. The synthesis follows evidence through design, fabrication, commissioning, operation, maintenance, and retirement, showing where batch variability or feedback from prior interventions can invalidate a model trained on early-life data. It recommends multimodal health indicators, censored-data handling, regime-specific calibration, and decision thresholds tied to inspection or replacement rather than abstract prediction accuracy. The cited literature is used as a structured evidence base; no new lifetime test is introduced. The result is a translation framework in which AI supports lifecycle decisions while operators can reconstruct what the model knew, why it acted, and when its reliability claim must be reconsidered.