AI-Based Predictive Maintenance in Industrial IoT with Real-Time Data Processing

Sung Min Kyung

AI-Based Predictive Maintenance in Industrial IoT with Real-Time Data Processing

Authors

Sung Min Kyung

Industrial Data Scientist, South Korea.

Keywords:

Predictive Maintenance, Industrial IoT (IIoT), Artificial Intelligence, Real-Time Data Processing, Edge Computing, Machine Learning, Condition Monitoring

Synopsis

Predictive maintenance powered by artificial intelligence (AI) is transforming industrial operations by minimizing downtime, optimizing resource utilization, and enabling cost-efficient maintenance strategies. The integration of real-time data processing within Industrial Internet of Things (IIoT) systems allows for immediate condition monitoring and decision-making based on sensor data, improving system reliability and operational continuity. This paper discusses recent advances in AI-driven predictive maintenance systems, focusing on their application within IIoT frameworks. Emphasis is placed on real-time analytics, data fusion techniques, and system architecture that enables continuous monitoring. Furthermore, existing literature is critically reviewed to identify knowledge gaps, followed by a proposal of a high-level model integrating edge computing and deep learning for effective predictive maintenance.

References

(1) Zhang, Y., Liu, T., and Wang, X. Edge-Enabled LSTM Networks for Real-Time Predictive Maintenance in Industrial IoT. IEEE Internet of Things Journal, vol. 10, no. 2, 2023, pp. 2456–2467.

(2) Sirimalla A. Autonomous Performance Tuning Framework for Databases Using Python and Machine Learning. J Artif Intell Mach Learn & Data Sci 2023 1(4), 3139-3147. DOI: doi.org/10.51219/JAIMLD/adithya-sirimalla/642

(3) Goyal, M., Rani, S., and Kaur, A. Federated Learning for Secure Predictive Maintenance in IIoT Environments. Journal of Industrial Information Integration, vol. 27, 2022, p. 100287.

(4) Khan, R., Rehman, M. H., Zangoti, H. M., et al. Industrial Internet of Things: Recent Advances, Enabling Technologies and Open Challenges. Computers & Electrical Engineering, vol. 81, 2020, p. 106522.

(5) Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., et al. A Systematic Literature Review of Machine Learning Methods Applied to Predictive Maintenance. Computers & Industrial Engineering, vol. 137, 2019, p. 106024.

(6) Lee, J., Davari, H., Singh, J., and Pandhare, V. Industrial Artificial Intelligence for Industry 4.0-Based Manufacturing Systems. Manufacturing Letters, vol. 18, 2018, pp. 20–23.

(7) Susto, G. A., Schirru, A., Pampuri, S., et al. Machine Learning for Predictive Maintenance: A Multiple Classifier Approach. IEEE Transactions on Industrial Informatics, vol. 11, no. 3, 2015, pp. 812–820.

(8) Ahmad, R., and Kamaruddin, S. An Overview of Time-Based and Condition-Based Maintenance in Industrial Application. Computers & Industrial Engineering, vol. 63, no. 1, 2012, pp. 135–149.

(9) Qi, Q., and Tao, F. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access, vol. 6, 2018, pp. 3585–3593.

(10) Liu, R., Yang, B., Zio, E., and Chen, X. Artificial Intelligence for Fault Diagnosis of Rotating Machinery: A Review. Mechanical Systems and Signal Processing, vol. 108, 2018, pp. 33–47.

(11) Pang, Z., Luvisotto, M., and Dzung, D. Wireless High-Performance Communications: The Challenges and Opportunities of a New Target. IEEE Industrial Electronics Magazine, vol. 11, no. 3, 2017, pp. 20–25.

(12) Sirimalla, A. (2022). End-to-end automation for cross-database DevOps deployments: CI/CD pipelines, schema drift detection, and performance regression testing in the cloud. World Journal of Advanced Research and Reviews, 14(3), 871–889. https://doi.org/10.30574/wjarr.2022.14.3.0555

(13) Yoon, J., Zameer, M., and Kim, D. Recurrent Neural Network-Based Predictive Maintenance for Industrial Systems. IEEE Access, vol. 8, 2020, pp. 219206–219217.

Published

July 20, 2025

Series

Research paper

License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.