IJEEMCS - International Journal of Electrical Engineering, Mathematics and Computer Science - Vol. 1 Issue. 2 (2024)

A Deep Learning Approach to Fault Detection in Industrial IoT Networks

Alfina Herawati, Bagus Setyo,

Abstract

Industrial IoT (IIoT) networks, critical for automation and smart manufacturing, are susceptible to faults due to their complexity and the large number of connected devices. This paper introduces a deep learning-based approach for early fault detection in IIoT networks. By leveraging recurrent neural networks (RNNs) and convolutional neural networks (CNNs), the system effectively identifies anomalies in real-time, helping to reduce system downtime and enhance operational efficiency in industrial settings.