Machine Learning Algorithms for Predictive Maintenance in Industrial Systems

Abstract

The Industrial Internet of Things (IIoT) has transformed modern manufacturing by generating massive volumes of multivariate sensor data, yet traditional reactive and preventive maintenance strategies continue to cause significant unplanned downtime and high operational costs. This thesis addresses these challenges by developing three novel deep learning-based predictive maintenance (PdM) models specifically designed for resource-constrained IIoT environments. Experimental results demonstrate that the Hybrid-PdM model achieves state-of-the-art performance with 99.14% average accuracy, 0.51% false alarm rate (FAR), RUL prediction MAE of 3.55 cycles, and real-time inference latency of 12.47 ms while maintaining a model size under 5 MB. These outcomes significantly outperform traditional machine learning baselines and recent literature models, confirming the effectiveness of the proposed generative-reconstruction hybrid approach. The research contributes a complete, edge-ready PdM framework that reduces downtime by up to 50% and maintenance costs by up to 40%. It provides both theoretical advancements in generative and variational deep learning for industrial applications and immediately deployable tools for smart factories. Future extensions include physics-informed networks, federated learning, and digital twin integration