Paper Title
AI-Powered Predictive Maintenance Using Iot and Blockchain - A Secure and Intelligent Framework to Minimize Downtime and Optimize Industrial Operations

Abstract
Unplanned equipment failures in critical industrial sectors (e.g., manufacturing, energy, aviation) result in excessive downtime, safety risks, and operational losses. Traditional maintenance methods lack scalability, real-time adaptability, and security for Industry 4.0 ecosystems. To address these gaps, this paper proposes a novel AI-driven predictive maintenance (Pd.M.) framework integrating IoT-enabled multi-modal sensors, Edge AI for low-latency inference, Federated Learning (FL) for privacy-preserving model training, and Block chain for tamper-proof maintenance records. Lightweight deep learning models deployed at the edge analyze sensor data in real time, while FL ensures decentralized collaboration across stakeholders without raw data sharing. Block chain smart contracts automate maintenance logging, enhancing transparency and compliance with industrial standards. The framework is validated on real-world semiconductor manufacturing datasets, demonstrating significant improvements in fault detection accuracy, maintenance cost reduction, and response times compared to rule-based methods. Additionally, the system’s decentralized design ensures scalability across smart factories and energy grids while adhering to data regulations. Future work will explore adaptive scheduling via reinforcement learning and integration with Digital Twins. This unified approach bridges the gap between secure, intelligent maintenance and industrial IoT scalability. Keywords - Predictive maintenance, Artificial intelligence, Industrial IoT, Edge computing, Federated learning, Block chain.