Blockchain-Enhanced AI Framework for Secure Industrial Predictive Maintenance

Abstract

The rapid growth of Industry 4.0 has transformed traditional manufacturing systems into connected cyber-physical environments. These systems continuously produce large amounts of operational and sensor data, which are vital for predictive maintenance and informed decision-making. However, the reliability, integrity, and security of this data remain significant challenges. This paper introduces a blockchain-based industrial monitoring framework that integrates Artificial Intelligence (AI) for predictive analytics with blockchain for decentralized and tamper-proof data management. The framework enhances transparency, traceability, and secure collaboration among stake-holders by maintaining verified records of equipment health and maintenance activities. Smart contracts also automate fault alerts, compliance checks, and maintenance scheduling, removing the need for centralized authorities. Experimental results on industrial datasets demonstrate better data integrity, lower risk of cyber threats, and improved predictive accuracy. The proposed hybrid architecture offers a scalable, auditable, and secure foundation for next-generation industrial systems.