DEGA: A Deterministic Diagnostic Evidence Governance Agent for Industrial IoT—A DUDU-BLDC Case Study

Abstract

Industrial diagnostic systems require a governance mechanism that determines whether heterogeneous evidence is admissible, mutually consistent, and sufficient for an automatic action. This paper presents the Diagnostic Evidence Governance Agent (DEGA), a deterministic governance layer built from explicit finite-state machine states, replaceable routing policies, an authoritative SafetyGuard, and hash-linked audit with deterministic replay. The bounded DUDU-BLDC case study retains diagnostic evidence from eight acquisitions and extends the evaluation to governance-profile sensitivity and state coverage. Across 15,360 retained real-evidence case-policy routes, no automatic recommendation was issued; 9216 routes reached Decision Check; and the mandatory-explanation rule triggered 7904 times. Under predeclared contract-satisfied simulated profiles, 2924 recommendations and 12,436 escalations were produced, with no recommended route violating the internal governance-consistency proxy. The results show that explanation availability is a binding governance condition and that recommendation-path reachability can be evaluated without weakening the remaining SafetyGuard gates; the simulation is restricted to explanation availability and admissibility metadata and does not validate feature attribution, recommendation correctness, or industrial deployment.