Leveraging AI for Predictive Maintenance with Minimizing Downtime in Telecommunications Networks

Ali Abdulkareem, Sarah; Al-Hiyali, Mohammed Isam; Kudaiberdi Gaparalievich, Kozhobekov; Abbas, Saad Jabbar; Magbol Alwan, Ghanim

doi:10.22034/jipm.2025.728396

Leveraging AI for Predictive Maintenance with Minimizing Downtime in Telecommunications Networks

Document Type : Original Article

Authors

Sarah Ali Abdulkareem ¹

Mohammed Isam Al-Hiyali ²

Kozhobekov Kudaiberdi Gaparalievich ³

Saad Jabbar Abbas ⁴

Ghanim Magbol Alwan ⁵

¹ Al-Turath University, Baghdad 10013, Iraq

² Al-Mansour University College, Baghdad 10067, Iraq

³ Osh State University, Osh City 723500, Kyrgyzstan

⁴ Al-Rafidain University College Baghdad 10064, Iraq

⁵ Madenat Alelem University College, Baghdad 10006, Iraq

10.22034/jipm.2025.728396

Abstract

ABSTRACT
Background: Telecommunications networks are exposed to numerous issues concerning equipment and that causes network outage, which proves very expensive. Basic maintenance methodologies like reactive or even scheduled preventive maintenance cannot cope up with the increasing trends in the facilities of telecom companies.
Objective: The article examines how AI is applied to support predictive maintenance so that telecommunication networks can perform as intended with reduced downtime.
Methods: The review of existing AI algorithms is presented, focusing on the ML models and deep learning methods. Network operations and maintenance logs are analyzed for data to assess the capabilities of the AI models in terms of prediction. It identifies and analyses such quantifiable parameters as the failure rate prediction accuracy and the response time cut.
Results: Computerisation of the forecast maintenance revealed a corresponding decrease in equipment failure incidences and generally reduced time lost due to unscheduled stops. Through the improved network performance, the response to potential threats was quicker than before and services became more reliable and inexpensive to offer.
Conclusion: To reduce network outages, reduce network vulnerability, and maximize the efficiency of telecommunications operations, the use of AI-based predictive maintenance can be viewed as a prospect. As technology advances, newer versions of AI algorithms will provide improved predictive strength and incorporation into the telecommunications system.

Keywords

KEYWORDS: Predictive Maintenance

Artificial Intelligence (AI)

Machine Learning

Telecom Networks

Downtime Reduction

Network Reliability

Deep Learning

Failure Prediction

Operational Efficiency

Network Optimization

References

Alghamdi, M., Alomari, S., and Alkatheri, M. (2024). Adopting Government Cyber Security Initiatives - A study of SMEs in Saudi Arabia. International Journal for Scientific Research, 3 (10). https://doi.org/10.59992/ijsr.2024.v3n10p11

Arpilleda, J. (2023). Exploring the Potential of AI and Machine Learning in Predictive Maintenance of Electrical Systems. International Journal of Advanced Research in Science, Communication and Technology, 751-756. https://doi.org/10.48175/IJARSCT-12365

Balmer, R. E., Levin, S. L., and Schmidt, S. (2020). Artificial Intelligence Applications in Telecommunications and other network industries. Telecommunications Policy, 44 (6), 101977. https://doi.org/https://doi.org/10.1016/j.telpol.2020.101977

Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., and Safaei, B. (2020). Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability, 12 (19). https://doi.org/10.3390/su12198211.

Dhanraj, D., Sharma, A., Kaur, G., Mishra, S., Naik, P., and Singh, A. (2023). Comparison of Different Machine Learning Algorithms for Predictive Maintenance. 2023 International Conference for Advancement in Technology (ICONAT), 24-26 Jan. https://doi.org/10.1109/ICONAT57137.2023.10080334.

Dhyani, B. (2021). Predicting Equipment Failure in Manufacturing Plants: An AI-driven Maintenance Strategy. Mathematical Statistician and Engineering Applications, 70, 1326-1334. https://doi.org/10.17762/msea.v70i2.2324

Faris, M., Jasim, I., and Qasim, N. (2021). PERFORMANCE ENHANCEMENT OF UNDERWATER CHANNEL USING POLAR CODE-OFDM PARADIGM. International Research Journal of Science and Technology, 3 (9), 55-62. https://www.irjmets.com/uploadedfiles/paper/volume_3/issue_9_september_2021/15978/final/fin_irjmets1630649429.pdf

Giannakidou, S., Radoglou-Grammatikis, P., Koussouris, S., Pertselakis, M., Kanakaris, N., Lekidis, A., Kaltakis, K., et al. (2022). 5G-Enabled NetApp for Predictive Maintenance in Critical Infrastructures. 2022 5th World Symposium on Communication Engineering (WSCE), 16-18 Sept. https://doi.org/10.1109/WSCE56210.2022.9916037.

Hashim, N., Mohsim, A. H., Rafeeq, R. M., and Pyliavskyi, V. (2019). New approach to the construction of multimedia test signals. International Journal of Advanced Trends in Computer Science and Engineering, 8 (6), 3423-3429. https://doi.org/10.30534/ijatcse/2019/117862019

Hoffmann, M. A., and Lasch, R. (2023). Tackling Industrial Downtimes with Artificial Intelligence in Data-Driven Maintenance. ACM Comput. Surv., 56 (4), Article 82. https://doi.org/10.1145/3623378

Hrnjica, B., and Softic, S. (2020). Explainable AI in Manufacturing: A Predictive Maintenance Case Study. Advances in Production Management Systems. Towards Smart and Digital Manufacturing, 66-73. https://doi.org/10.1007/978-3-030-57997-5_8

Iatsykovska, U., Khlaponin. Y., Qasim, N., Khlaponin, D., Trush, I., Karpiński, M. (2018). Operation analysis of statistical information telecommunication networks using neural network technology. IEEE. Conferences on Intelligent Data Acquisition and Advanced Computing Systems, 460 (1), 199-203. https://doi.org/10.1051/e3sconf/202346004003

Kamel, M., Nour, M., Awad, M., Essa, M., and Abdelbaki, N. (2021). Novel Data Mining Approach Predicting Alerts in The Telecom Industry. 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), 5-7 Dec. 2021. https://doi.org/10.1109/ICICIS52592.2021.9694179.

Lee, W. J., Wu, H., Yun, H., Kim, H., Jun, M. B. G., and Sutherland, J. W. (2019). Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data. Procedia CIRP, 80, 506-511. https://doi.org/10.1016/j.procir.2018.12.019

Li, M., Huo, M., Cheng, X., and Xu, L. (2020). Research and Application of AI in 5G Network Operation and Maintenance. 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 17-19 Dec. 2020. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00212.

Madasamy, S., Shankar, B. P., Yadav, R. K., Jayalakshmi, K, P. (2023). A Machine Learning Approach in Predictive Maintenance in the IoT Enabled Industry 4.0. 2023 4th International Conference on Smart Electronics and Communication (ICOSEC), 20-22 Sept. https://doi.org/10.1109/ICOSEC58147.2023.10276226.

Mallouk, I., Sallez, Y., and Majd, B. A. E. (2021). Machine learning approach for predictive maintenance of transport systems. 2021 Third International Conference on Transportation and Smart Technologies (TST), 27-28 May. https://doi.org/10.1109/TST52996.2021.00023.

Math, M. (2023). Exploring Effective Approaches To Minimize Downtime In Final Assembly Line Of Braking Systems. Journal of Namibian Studies : History Politics Culture, 35. https://doi.org/10.59670/jns.v35i.4246

Minea, M., Dumitrescu, C. M., and Minea, V. L. (2021). Intelligent Network Applications Monitoring and Diagnosis Employing Software Sensing and Machine Learning Solutions. Sensors, 21 (15). https://doi.org/10.3390/s21155036.

Mohan, R., Roselyn, J. P., and Uthra, R. A. (2023). LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation. Journal of Quality in Maintenance Engineering, 29 (4), 763-798. https://doi.org/10.1108/JQME-07-2022-0041

Nacchia, M., Fruggiero, F., Lambiase, A., and Bruton, K. (2021). A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector. Applied Sciences, 11 (6). https://doi.org/10.3390/app11062546.

Ohalete, N., Aderibigbe, A., Ani, E., Ohenhen, P., and Akinoso, A. (2023). Advancements in predictive maintenance in the oil and gas industry: A review of AI and data science applications. World Journal of Advanced Research and Reviews, 20, 167-181. https://doi.org/10.30574/wjarr.2023.20.3.2432

Ong, K. S. H., Niyato, D., and Yuen, C. (2020). Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach. 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), 2-16 June 2020. https://doi.org/10.1109/WF-IoT48130.2020.9221098.

Qasim, N., Shevchenko, Y.P., and Pyliavskyi, V. (2019). Analysis of methods to improve energy efficiency of digital broadcasting. Telecommunications and Radio Engineering, 78 (16), 1457-1469. https://doi.org/10.1615/TelecomRadEng.v78.i16.40

Ran, Y., Zhou, X., Lin, P., Wen, Y., and Deng, R. (2019). A Survey of Predictive Maintenance: Systems, Purposes and Approaches. https://doi.org/10.48550/arXiv.1912.07383

Raparthi, M., Dodda S. B., and Maruthi, S. (2023). Predictive Maintenance in IoT Devices using Time Series Analysis and Deep Learning. Dandao Xuebao Journal of Ballistics, 35 (3), 01-10. https://doi.org/10.52783/dxjb.v35.113

Resende, C., Folgado, D., Oliveira, J., Franco, B., Moreira, W., Oliveira-Jr, A., Cavaleiro, A., et al. (2021). TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance. Sensors, 21 (14). https://doi.org/10.3390/s21144676.

Salih, M. M., Khaleel, B. M., Qasim, N. H., Ahmed, W. S., Kondakova, S., and Abdullah, M. Y. (2024). Capacity, Spectral and Energy Efficiency of OMA and NOMA Systems. 2024 35th Conference of Open Innovations Association (FRUCT). https://doi.org/10.23919/FRUCT61870.2024.10516394.

Shakir, M. A., Abass, H. K., Jelwy, O. F., Al-Bayati, H. N. A., Salman, S. M., Mikhav, V., and Bodnar, N. (2024). Developing Interpretable Models for Complex Decision-Making. 2024 36th Conference of Open Innovations Association (FRUCT). https://doi.org/10.23919/FRUCT64283.2024.10749922.

Shetty, R. B. (2018). Predictive Maintenance in the IoT Era. In Prognostics and Health Management of Electronics, 589-612. https://doi.org/https://doi.org/10.1002/9781119515326.ch21

Shin, W., Han, J., and Rhee, W. (2021). AI-assistance for predictive maintenance of renewable energy systems. Energy, 221, 119775. https://doi.org/10.1016/j.energy.2021.119775