Big Data-Driven Predictive Maintenance for Industrial IoT (IIoT) Systems

Abstract

Big data-driven predictive maintenance is becoming a fundamental component of IIoT systems to enable failure predication proactively and streamline the scheduling process. This work examines the intersection of machine learning, digital twin technology, and optimization techniques in the context of increasing predictive maintenance efficiency and effectiveness. Four algorithms were evaluated via live IIoT sensor reading inputs: Random Forest, XGBoost, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM). The performance outcome indicates that XGBoost achieved the highest in fault detection accuracy at 96.4%, followed by CNN at 94.8%, LSTM at 92.3%, and then Random Forest at 90.1%. A blockchain-based federated learning framework was also utilized to facilitate secure and decentralized predictive maintenance and minimize false alarms by 28% compared to conventional methods. Optimization methods such as Koopman observables and Dynamic Mode Decomposition with Control (DMDc) also enhanced system efficiency, reducing computing cost by 35%. Scalability issues with predictive maintenance in large-scale industries are confirmed as part of this study, as well as edge AI integration and reinforcement learning as probable future trends. These results form the basis of the significance of data-driven predictive maintenance in minimizing downtime, optimizing resource utilization, and facilitating cost-efficient industrial processes.