📅 30 June 2024
DOI: 10.62951/ijeemcs.v1i2.74
A Deep Learning Approach to Fault Detection in Industrial IoT Networks
International Journal of Electrical Engineering, Mathematics and Computer Science
Asosiasi Riset Teknik Elektro dan Informatika Indonesia (ARTEII)
📄 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.
🔖 Keywords
#Industrial IoT; fault detection; deep learning; recurrent neural networks; convolutional neural networks; anomaly detection
ℹ️ Informasi Publikasi
Tanggal Publikasi
30 June 2024
Volume / Nomor / Tahun
Volume 1, Nomor 2, Tahun 2024
📝 HOW TO CITE
Alfina Herawati; Bagus Setyo, "A Deep Learning Approach to Fault Detection in Industrial IoT Networks," International Journal of Electrical Engineering, Mathematics and Computer Science, vol. 1, no. 2, Jun. 2024.
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