Blockchain-Based Secure Digital Twins for Predictive Maintenance in Autonomous Cyber-Physical Systems

Abstract

The integration of Digital Twins (DTs) in Cyber-Physical Systems (CPS) has revolutionized predictive maintenance by enabling real-time monitoring, simulation, and forecasting. However, security and trust remain significant challenges in autonomous CPS environments due to decentralized control and dynamic interactions. This paper proposes blockchain-based architecture to secure Digital Twins and ensure data integrity, transparency, and trustworthiness in predictive maintenance workflows. The proposed framework leverages smart contracts for secure event triggering, tamper-proof logs for maintenance actions, and decentralized identity (DID) mechanisms for authentication. A case study on an autonomous industrial robotic arm demonstrates the effectiveness of the proposed system in ensuring secure, resilient, and reliable predictive maintenance.