Early detection of air leakage in IoT-connected compressors using enhanced data sampling with deep learning.

The persistent challenge of air leakage in smart factories continues to impose significant costs and operational inefficiencies. Conventional solutions, such as infrared detectors, suffer from drawbacks, demanding additional manpower for detection and incurring monetary losses during equipment downtime. Addressing the urgent need for early air leakage detection in manufacturing plants amid the ongoing digital transformation, this paper introduces an end-to-end framework that jointly handles class imbalance and provides uncertainty-aware predictions. At its core, we propose a novel unsupervised-enhanced data sampling method (UEDSM) to preserve data structure while alleviating imbalance, integrated with a dropout-enabled neural network (ALDNet) that applies Monte Carlo Dropout for robust inference. The effectiveness of our method is validated through a comprehensive series of experiments, incorporating real-time physical monitoring of two air compressors within a manufacturing plant. Beyond minimizing resource wastage and human intervention, our solution achieves over 95% accuracy and an F1-score above 80%, enabling reliable leakage detection several minutes in advance. These results highlight the practical viability of our approach for deployment in edge environments, contributing to improved efficiency, reduced resource wastage, and enhanced resilience in smart manufacturing.