Blockchain-anchored Digital Twin Traceability for Prescriptive Maintenance: A Conceptual Framework and Proof-of-Concept

Abstract

Prescriptive maintenance (PsM) recommends concrete interventions from asset condition and operational constraints, increasingly relying on digital twins (DTs). In multi-stakeholder industrial settings, however, twin states, prognostic models, prescriptions, and execution outcomes are rarely linked by a verifiable audit trail. This paper presents ChainTwin-PsM, a compact conceptual framework for blockchain-anchored digital twin traceability that supports auditable PsM decisions. We define a minimal set of Traceable Twin Events (TTEs) spanning twin instantiation, state commitment, model registration, prediction, prescription, and execution feedback, together with hybrid on-chain/off-chain anchoring principles. A lightweight proof-of-concept simulates a multi-asset fleet with limited maintenance capacity and conflicting operator-service-provider incentives. It demonstrates that blockchain-backed service-level commitments can lower system cost and risk relative to weakly enforced coordination. The work is intentionally scoped as a framework-plus-PoC contribution rather than a state-of-the-art prognostics study, and keeps a C-MAPSS-compatible health interface for a subsequent data-driven extension.