A Deep Learning Approach to Fault Detection in Industrial IoT Networks
DOI:
https://doi.org/10.62951/ijeemcs.v1i2.74Keywords:
Industrial IoT, fault detection, deep learning, recurrent neural networks, convolutional neural networks, anomaly detectionAbstract
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.
References
Abbasi, M., & Chen, Y. (2017). Deep Learning for IoT Network Security: A Review. Journal of Network and Computer Applications.
Chen, L., et al. (2019). Fault Detection and Isolation in Industrial IoT: A Comprehensive Review. Sensors.
Gupta, P., et al. (2019). A CNN-RNN Hybrid Model for Fault Detection in IoT Networks. IEEE Access.
Huang, Z., et al. (2020). Fault Detection in Smart Manufacturing Systems. Sensors.
Kim, H., et al. (2018). Industrial IoT and Machine Learning: A Survey. IEEE IoT Journal.
Li, X., et al. (2018). Anomaly Detection in IoT using Deep Learning. Procedia Computer Science.
Lu, Y., et al. (2021). Deep Learning Models for Fault Detection in IoT Networks. Journal of Industrial Engineering and Management.
Nguyen, T., et al. (2020). Efficient Fault Detection in Industrial IoT. Journal of Industrial Information Integration.
Silva, A., & Ramos, D. (2018). IoT-Based Fault Monitoring in Industrial Systems. Procedia Manufacturing.
Torres, J., et al. (2019). A Comparative Study of Deep Learning Methods for Fault Detection. Journal of Manufacturing Systems.
Wang, H., et al. (2020). Recurrent Neural Networks for Industrial IoT Fault Prediction. Computers in Industry.
Wu, J., & Li, P. (2019). Machine Learning Algorithms for Predictive Maintenance in IoT. Computers in Industry.
Xu, Y., et al. (2020). Machine Learning in Industrial IoT: Current Trends and Future Challenges. IEEE Transactions on Industrial Informatics.
Zhan, L., & Xie, M. (2021). Real-Time Fault Detection in Industrial Networks Using Deep Learning. IEEE Access.
Zhang, C., & He, J. (2019). Convolutional Neural Networks for Fault Detection. IEEE Transactions on Automation Science and Engineering.
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