A Unified Conceptual Model for Integrating Hardware Reliability and Software-Based Fault Diagnostics

Abstract

Hardware reliability engineering and software-based fault diagnostics have matured as largely separate disciplines. Reliability engineering predicts how often and when physical components will fail, using population statistics, physics-of-failure models and design-time analyses such as FMEA and fault trees. Fault diagnostics, increasingly implemented in software, detects, isolates and identifies faults in individual systems at run time from sensor data, error counters and logs. Because the two communities use different vocabularies, time scales and units of analysis, their outputs are rarely combined. This paper addresses that gap through a conceptual research design that combines an integrative literature review with model building. A corpus of 120 sources published to date, spanning reliability foundations, fault diagnosis and prognostics, industrial integrity management, power systems, security monitoring, IT service reliability, risk analytics and human factors, was screened and synthesized thematically. The synthesis identifies eight dimensions on which the two disciplines diverge and four integration gaps. From these, the paper develops a unified conceptual model of five layers (physical reliability, observation, software diagnosis, integration, and decision) linked by bottom-up evidence flows and top-down prior and context flows, and anchored in a shared fault ontology based on the fault, error and failure chain. A probabilistic formalization treats design-time reliability models as priors, diagnostic outputs as likelihoods with explicit coverage and false-alarm characteristics, and the integration layer as the producer of posterior hazard rates and remaining useful life estimates, while a fleet feedback loop recalibrates both reliability and diagnostic parameters. An illustrative server memory scenario shows moderate and strong diagnostic evidence reducing predicted median life from about 47,000 hours to about 33,000 and 23,000 hours respectively. The paper discusses theoretical and practical implications, states its limitations, and sets out an agenda for empirical validation.