Machine Learning-based Predictive Maintenance for Industrial IoT Devices using Google Cloud AI Platform

Abstract

Predictive maintenance (PdM) in Industrial Internet of Things (IIoT) environments plays a vital role in minimizing unplanned downtime, improving operational efficiency, and spreading equipment lifespan. This paper presents a Machine Learning (ML)-based predictive maintenance basis deployed on Google Cloud AI Platform for real-time monitoring and fault prediction of manufacturing milling machine devices. The proposed system develops sensor-generated operational data, including torque, rotational speed, temperature, and tool wear, to train and evaluate multiple ML models such as Decision Tree, K-Nearest Neighbors (KNN), Gradient Boosting, Support Vector Machine (SVM), Gaussian Naïve Bayes, and Logistic Regression. The confirmed models, the Decision Tree classifier reached the highest accuracy of 99.40%, with strong cross-validation and AUC performance, indicating larger capability in detection machine failures. By fit in cloud-based AI services, the framework ensures scalable model deployment, high availability, and efficient real-time predictive analytics for manufacturing applications. Experimental findings reveal important improvements in prediction accuracy and conservation cost reduction associated to conventional reactive maintenance approaches. The study confirms the efficiency of combining IIoT sensor analytics, ML, and cloud-based AI structure for intelligent and proactive industrial conservation systems.