Architectural Models for Automated Diagnostic Logging, Fault Detection, and Data Persistence in Industrial Embedded Systems

Abstract

Industrial embedded systems such as programmable logic controllers, motor drives, smart field instruments, and remote terminal units are increasingly expected to explain their own behavior. To date, the convergence of Industry 4.0, the Industrial Internet of Things (IIoT), and functional safety regulation had turned diagnostic capability from an afterthought into a first-class architectural concern, yet design knowledge remained fragmented across automotive, process automation, energy, and enterprise IT communities. This study addresses that fragmentation through an integrative literature review and conceptual modeling approach. A corpus of 118 sources published to date, comprising standards, seminal technical works, and multidisciplinary applied studies, was screened against explicit inclusion criteria, coded in a concept matrix, and synthesized thematically. The synthesis identifies nine requirements that govern diagnostic logging, fault detection, and data persistence on resource-constrained hardware, including real-time non-interference, bounded memory, flash endurance, power-loss tolerance, safety integrity, and cybersecurity. From these findings the paper develops a conceptual model that links antecedent requirements to six architectural mechanisms and six diagnostic outcomes through six testable propositions, and instantiates it as a five-layer reference architecture with component models for a diagnostic event manager, a tiered hybrid fault detection engine, and a journaled persistence manager. Worked examples show that flash endurance and power-fail hold-up energy impose hard constraints that must be addressed at design time. The discussion compares the model with domain-specific practice and derives theoretical, practical, managerial, and standardization implications. Limitations of the integrative design are acknowledged, and a research agenda is proposed covering empirical validation of the propositions, embedded machine learning, log integrity, and semantic interoperability.