Distributed Embedded Intelligence for Hierarchical Health Monitoring and Fault Diagnosis in Complex Technical Systems

This architecture and position paper examines how complex technical systems combine distributed sensing, embedded intelligence, prognostics and health management, and digital-twin services, but these capabilities are often implemented as separate functions. This paper proposes a distributed embedded intelligence (DEI) framework that coordinates diagnostic responsibility across five hierarchical levels, from smart sensing to system-level health management and lifecycle services. Diagnostic functions are assigned according to a lowest-feasible-intelligence-level principle that accounts for data availability, context, timing, computational resources, model validity, connectivity, and authority. Local and fused health states retain fault hypotheses, functional capacity, uncertainty, confidence, and provenance, while hierarchical fusion and event-driven escalation determine whether an event can be resolved locally, requires additional evidence, or must be transferred to a higher level. Digital-twin and fleet services provide prognosis, scenario analysis, maintenance planning, and controlled model support without replacing essential real-time diagnosis. The framework is illustrated through progressive degradation of an aircraft hydraulic pump and an architecture-level comparison of centralized, edge-assisted, and DEI configurations. An illustrative calculation using assumed message sizes, event rates, and execution times suggests potential reductions in communication demand and detection-and-confirmation latency; preservation of lower-level capability during loss of higher-level services is a design objective. The study provides an architectural template proposed for adaptation to other systems for resilient distributed health management; the quantitative results are conditional architectural estimates, without experimental validation or demonstrated aircraft performance.