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

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.