Self-Supervised Deep Learning For Predictive Maintenance In Industrial Iot Systems

Abstract

The rise of Industrial Internet of Things (IIoT) has transformed traditional manufacturing and industrial processes by enabling real-time monitoring, data-driven decision-making, and automation. However, ensuring system reliability through timely fault detection and predictive maintenance remains a key challenge due to the scarcity of labeled data and the complexity of sensor-driven environments. This paper investigates the application of self-supervised deep learning (SSDL) techniques for predictive maintenance in IIoT systems. Unlike supervised learning, self-supervised methods leverage vast amounts of unlabeled sensor data to learn robust feature representations, which can subsequently be fine-tuned for downstream tasks such as fault prediction and anomaly detection. We explore contrastive learning, masked modeling, and temporal pretext tasks adapted to time-series industrial data, and compare their performance on benchmark IIoT datasets. Experimental results demonstrate that SSDL models outperform traditional supervised models under low-label regimes, improve generalization across heterogeneous devices, and reduce dependency on domain-specific feature engineering. The findings suggest that self-supervised deep learning can significantly advance predictive maintenance capabilities in smart factories, leading to reduced downtime, optimized operations, and increased safety.