Fault Detection and Localization in Industrial IoT Systems using Deep Learning

Abstract

In the ever-evolving landscape of Industrial Internet of Things (IoT) systems, the need for robust fault detection and localization has become paramount. Existing methods, while commendable, have faced certain limitations that necessitate the development of more sophisticated solutions. This work embarks on a journey to address these challenges and presents a novel approach to fault detection and localization, leveraging the formidable power of Deep Learning process. The limitations of existing methodologies primarily revolve around their inability to provide the required precision and accuracy in detecting and localizing faults within complex IoT deployments. These methods often struggle to handle the diverse contextual datasets that characterize modern industrial settings. Additionally, their computational efficiency often leaves much to be desired, hindering real-time fault response mechanisms. In response to these limitations, our proposed model introduces a hybrid framework. It combines Vector AutoRegressive Moving Average with Exogenous Variables (VARMAx) for precise fault localization and Convolutional Neural Networks (CNN) for robust fault detection. This fusion harnesses the strengths of both approaches, providing a comprehensive solution for fault management in industrial IoT systems. The advantages of this model are twofold. First, VARMAx offers exceptional accuracy in pinpointing the exact location of faults within the system, facilitating swift corrective actions. Second, the integration of CNN enhances fault detection by effectively capturing patterns and anomalies in the data, resulting in a highly responsive fault detection system. Notably, this model not only outperforms existing methods with a 3.9% boost in precision, 4.5% increase in accuracy, 4.9% higher recall, 8.5% improved AUC, but also boasts a remarkable 5.9% enhancement in computational speed, ensuring real-time responsiveness. The profound impact of this work extends to the realms of industrial automation and IoT system reliability. By addressing the limitations of existing approaches and introducing a robust fault detection and localization model, this paper paves the way for safer and more efficient industrial operations. The improved precision, accuracy, and speed of fault detection will minimize downtime, reduce maintenance costs, and ultimately elevate the performance and reliability of industrial IoT systems to new heights.