An information processing theory framework for intelligent fault diagnosis and predictive maintenance

Abstract

Introduction: Due to complex degradation processes and data-level, model-level, and system-level variations, industrial assets operate under high uncertainty. Existing PdM approaches still lack a unifying theoretical lens to align the uncertainty with technological and organizational capabilities. This paper aims to develop an IPT-grounded model, linking IPR and IPC for intelligent fault diagnosis and prescriptive maintenance. Methods: The research design combines the elements of system-level technical benchmarking, organizational surveys, and case-based validation in a mixed-method approach. The methodology follows from operationalizing IPT constructs by mapping the sources of uncertainty, defining the dimensions of IPR, identifying mechanisms such as digital twins, multi-sensor fusion, federated/edge learning, multi-agent orchestration, and evaluating the “fit” between IPR-IPC using measurable indicators. Results: The study develops a comprehensive multi-layer IPT framework comprising theoretical constructs, directional propositions, a translation layer converting the predictions to prescriptive maintenance actions, and an IPT Fit index for performance assessment. It also extends propositions on mechanism complementarity and provides scenario-based mechanism choice guidance under different archetypes of uncertainty. Discussion and conclusion: It then shows how fit between IPR and IPC enhances diagnostic accuracy, lead time, decision quality, and operational performance. It introduces practical design rules: diagnose IPR prior to selecting mechanisms, design complementary modules, engineer translation workflows, and track the fit as a performance KPI. The research positions IPT as a core logic to drive the design of adaptive, explainable, operationally effective PdM systems, and one that provides explicit pathways for its empirical validation in future work.