AI-Driven Predictive Maintenance Framework for Industrial IoT Systems using Google Cloud

Abstract

Analytical preservation has become an vital strategy in Industrial Internet of Things (IIoT) locations for enlightening equipment reliability, dropping unforeseen machine failures, and enlightening trade productivity. This paper suggests an AI-driven predictive maintenance framework using the Google Cloud AI Platform for smart monitoring and fault estimate of engineering refining machines. The planned framework assembles and procedures real-time device data such as vibration, temperature, turning speed, and instrument wear from IIoT-enabled plans. Three machine learning algorithms, namely Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are practical and assessed to classify potential equipment letdowns before disappointment occurrence. Among the manufacturing models, the XGBoost classifier achieved the highest prediction accuracy of 99.18% with strong accuracy, recall, and AUC performance, on behalf of superior ability in early fault finding and analytical analytics. The grouping of Google Cloud AI services allows walkable model training, cloud-based supply, real-time specialist care, and efficient data organization for smart manufacturing applications. New results show significant improvements in upkeep efficiency, decrease in working downtime, and lower upkeep costs associated with traditional sensitive conservation approaches. The study highlights the productivity of joining IIoT sensor analytics, cloud computation, and progressive artificial intelligence methods for evolving smart and proactive industrial conservation arrangements in Industry 4.0 surroundings.