Artificial Intelligence in Predictive Maintenance for Industrial IoT
Keywords:
Artificial Intelligence, Predictive Maintenance, Industrial IoT, Machine Learning, Smart ManufacturingAbstract
The adoption of the Industrial Internet of Things (IIoT) has enabled real-time monitoring of machinery, leading to increased efficiency and reduced downtime. This paper presents an AI-driven predictive maintenance system that utilizes machine learning algorithms to detect potential failures before they occur. Using historical sensor data, the proposed model achieves high accuracy in predicting equipment malfunctions, allowing timely intervention. The study demonstrates the potential of AI in optimizing industrial processes and reducing operational costs.
References
Cheng, Y., & Li, M. (2022). Deep learning for anomaly detection in industrial IoT systems. Sensors, 22(11), 4879. https://doi.org/10.3390/s22114879
Khan, R., & Gupta, P. (2020). Edge computing for predictive maintenance in smart factories. Journal of Manufacturing Processes, 56, 280-290. https://doi.org/10.1016/j.jmapro.2020.04.009
Kim, D., & Park, H. (2021). AI-driven fault detection in smart manufacturing. IEEE Transactions on Industrial Informatics, 17(3), 2215-2224. https://doi.org/10.1109/TII.2020.2973289
Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3(1), 18-23.
Lin, C., & Shen, Z. (2022). Digital twins in predictive maintenance: A review. Journal of Computational Science, 64, 101830. https://doi.org/10.1016/j.jocs.2022.101830
Ramachandran, M., & Roy, P. (2021). Blockchain-based data security in predictive maintenance. Information Systems Frontiers, 23(2), 311-327. https://doi.org/10.1007/s10796-020-10068-3
Smith, J. A., & Brown, K. (2019). Role of IoT in predictive maintenance: A case study. Journal of Industrial Engineering, 67(4), 401-415. https://doi.org/10.1002/jie.22234
Tsai, W. T., Elston, J., & Xu, Y. (2018). Machine learning approaches for predictive maintenance in Industry 4.0. International Journal of Advanced Manufacturing Technology, 94(5), 1785-1799. https://doi.org/10.1007/s00170-018-1924-5
Zhou, J., & Wang, L. (2018). AI-based optimization for predictive maintenance scheduling. Expert Systems with Applications, 97, 255-267. https://doi.org/10.1016/j.eswa.2017.12.019
Zhu, L., Rieger, B., & Liu, H. (2020). Predictive maintenance in IIoT: A survey. Computers in Industry, 123, 103307. https://doi.org/10.1016/j.compind.2020.103307
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