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

Industrial diagnostic systems increasingly combine machine-learning classifiers, temporal models, uncertainty surrogates, and explanation methods. Their operational use requires a separate mechanism that determines whether the available evidence is valid, mutually consistent, sufficient for a recommendation, or should instead lead to abstention or human escalation. This paper introduces the Diagnostic Evidence Governance Agent (DEGA), a deterministic agent that separates evidence generation from workflow governance. DEGA operates on immutable evidence objects, follows an explicit finite-state workflow, applies a higher-priority SafetyGuard, and records a hash-linked audit trace supporting deterministic replay. The architecture is demonstrated in an offline case study using eight DUDU-BLDC acquisitions, 200 non-overlapping windows, 28 current- and speed-based features, three classifiers, and four spline representations. Across 16 dependent acquisition-disjoint assignment views, mean acquisition-level macro-F1 was 0.875 for Logistic Regression and 0.667 for both tree-based classifiers. The pipeline produced 768 complete EvidenceBundles with reproducible scientific hashes. Explanation rankings were assignment-sensitive, with a mean top-10 Jaccard similarity of 0.318. Twelve representative DEGA executions ended in three escalations and nine no-decision outcomes, with complete audit chains and deterministic replay. The study demonstrates auditable, fail-closed governance of AI-supported diagnostic evidence without delegating workflow control to an AI agent.