A Deep Learning Approach to Fault Detection in Industrial IoT Networks
Alfina Herawati, Bagus Setyo · International Journal of Electrical Engineering Mathematics and Computer Science · 2024
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.