Optimizing Predictive Maintenance in Industrial IoT Networks Using Machine Learning

Predictive maintenance (PdM) in Industrial Internet of Things (IIoT) environments enables proactive fault detection by analyzing real-time sensor data streams. This study presents a comparative evaluation of three machine learning (ML) algorithms—Support Vector Machines (SVM), Decision Trees (DT), and Artificial Neural Networks (ANN)—implemented within a unified IIoT predictive-maintenance framework. A synthetic multivariate sensor dataset was generated using controlled fault injection, Gaussian noise modeling, and stratified sampling across multiple operating regimes. Each model was trained and validated using five-fold cross-validation to ensure statistical robustness. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, along with operational key performance indicators (KPIs) related to downtime and maintenance-cost reduction. A maintenance policy simulator mapped prediction outputs to maintenance decisions and estimated cost savings using Monte Carlo evaluation. Experimental results show that ANN achieved the highest prediction accuracy (94.8 ± 0.6%) and produced the greatest operational gains (50% downtime reduction and 32% maintenance-cost reduction), outperforming SVM and DT. The proposed architecture incorporates edge preprocessing, cloud analytics, and automated alerting, offering scalability for real-world industrial deployments. The findings demonstrate that ML-driven PdM significantly improves asset reliability and reduces operational expenditure in IIoT systems. All simulation scripts and synthetic data generators used in this study are available to support reproducibility and benchmarking.