A Practical Framework for Assessing Predictive Feasibility in Industrials Systems

This technical whitepaper introduces the Predictive Feasibility Assessment (PFA) framework — a practical methodology for evaluating whether industrial signals support stable and reproducible prediction before large-scale AI deployment begins. The framework focuses on structural reproducibility, cross-run consistency, representation-dependent recovery of predictive structure, and operational deployment stability across industrial time-series systems. Using real-world examples from vibration monitoring, telemetry systems, battery degradation, turbofan degradation, and quantum calibration data, the paper demonstrates how predictive deployment failure may originate not from insufficient model complexity, but from instability in the mapping between observable signals and underlying system behavior. The framework distinguishes between:• operationally viable predictive signals• partially recoverable predictive structure• and structurally non-viable predictive systems. This publication is intended for:• predictive maintenance teams• reliability engineers• industrial AI deployment teams• operational analytics groups• and researchers working on time-series prediction under real-world conditions. A separate companion publication:“PFA Executive Guide Assessing Predictive Feasibility”provides a shorter operational overview intended for industrial decision-making and deployment-oriented evaluation.