An Improved Framework for Predictive Maintenance in Industry 4.0 And 5.0 Using Synthetic Iot Sensor Data and Boosting Regressor For Oil and Gas Operations.
Article Sidebar
Main Article Content
Abstract: Predictive Maintenance (PdM) plays a pivotal role in Industry 4.0 and 5.0 by minimizing equipment downtime and optimizing performance. However, limitations such as scarce fault data, data quality issues, and model interpretability hinder its effectiveness. This study presents a machine learning-based PdM framework tailored for Vortex Oil and Gas Nigeria Ltd., leveraging synthetic sensor data and eXtreme Boost (XGBoost) regression to predict Remaining Useful Life (RUL) of industrial equipment. Using simulated data from 50 machines over 300 operational cycles, the model achieved strong performance metrics, with an RMSE of 40.73 and MAE of 32.38. A four-layer system architecture—comprising data acquisition, edge processing, cloud analytics, and user interface—enabled real-time monitoring and decision-making. The results underscore the system’s capacity to detect early failure trends and support proactive maintenance, aligning with the goals of intelligent, sustainable, and human-centric industrial operations. This research contributes a scalable, data-driven PdM solution suitable for environments with limited real-world fault data.
Downloads
References
Vega-Márquez, B., Rubio-Escudero, C., & Nepomuceno-Chamorro, I. (2022). Generation of synthetic data with conditional generative adversarial networks. Logic Journal of the IGPL, 30(2), 252–262. https://doi.org/10.1093/jigpal/jzaa059 DOI: https://doi.org/10.1093/jigpal/jzaa059
Pelaez, J. R., Aguiar, M. A., Destro, R. C., Kovacs, Z. L., & Simoes, M. G. (2001). PdM oriented neural network system - PREMON. In IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (pp. 49-52). IEEE. https://doi.org/10.1109/IECON.2001.976452 DOI: https://doi.org/10.1109/IECON.2001.976452
Mikołajewska, E., Mikołajewski, D., Mikołajczyk, T., & Paczkowski, T. (2025). Generative AI in AI-based digital twins for fault diagnosis for PdM in Industry 4.0/5.0. Applied Sciences, 15(6), 3166. https://doi.org/10.3390/app15063166 DOI: https://doi.org/10.3390/app15063166
Rane, N. L., Kaya, O., & Rane, J. (2024). Artificial intelligence, machine learning, and deep learning technologies as catalysts for industry 4.0, 5.0, and society 5.0. In Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0 (pp. 1-27). Deep Science Publishing. https://doi.org/10.70593/978-81-981271-8-1_1 DOI: https://doi.org/10.70593/978-81-981271-8-1_1
Rane, N. L., Kaya, O., & Rane, J. (2024). Advancing industry 4.0, 5.0, and society 5.0 through generative artificial intelligence like ChatGPT. In Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0 (pp. 137-161). Deep Science Publishing. https://doi.org/10.70593/978-81-981271-8-1_7 DOI: https://doi.org/10.70593/978-81-981271-8-1_7
Rane, N. L., Kaya, O., & Rane, J. (2024). Artificial intelligence and big data analytics for the advancement of industry 4.0, 5.0, and society 5.0. In Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0 (pp. 162-179). Deep Science Publishing. https://doi.org/10.70593/978-81-981271-8-1_8 DOI: https://doi.org/10.70593/978-81-981271-8-1_8
Hosnia, H. (2025). PdM in the era of Industry 5.0: Challenges and opportunities. Journal of Materials and Engineering, 3(4), 376-382. https://doi.org/10.61552/JME.2025.04.004
Taş, Ü. (2024). Advancing PdM: A comprehensive case study through Industry 4.0. International Journal of Automotive Engineering and Technologies, 27(1), 49-52. https://doi.org/10.18245/ijaet.1543509 DOI: https://doi.org/10.18245/ijaet.1543509
Baroud, S. Y., Yahaya, N. A., & Elzamly, A. M. (2024). Cutting-edge AI approaches with MAS for PdM in Industry 4.0: Challenges and future directions. Journal of Applied Data Sciences, 5(2), 455-473. https://doi.org/10.22624/AIMS/BHI/V11N1P1 DOI: https://doi.org/10.47738/jads.v5i2.196
Koulla Moulla, D., Mnkandla, E., Aboubakar, M., Abba Ari, A. A., & Abran, A. (2024). PdM-FSA: PdM framework with fault severity awareness in Industry 4.0 using machine learning. International Journal of Electrical and Computer Engineering (IJECE), 14(6), 7211-7223. https://doi.org/10.11591/ijece.v14i6.pp7211-7223 DOI: https://doi.org/10.11591/ijece.v14i6.pp7211-7223
Okofu, S. N., Asuai, C., Okumoku-Evroro, O., & Maureen, A. (2025). Development of an enhanced point of sales system for retail business in developing countries. Journal of Behavioral Informatics, Digital Humanities and Development Research. https://doi.org/10.22624/AIMS/BHI/V11N1P1
Clive, A., Nana, O. K., & Destiny, I. E. (2024). Optimizing credit card fraud detection: A multi-algorithm approach with artificial neural networks and gradient boosting model. International Research Journal of Modernization in Engineering Technology and Science, 6(12), 2582-5208.
Akazue, M., Asuai, C., Abel, E., Edith, O., & Ufiofio, E. (2023). CYBERSHIELD: Harnessing ensemble feature selection technique for robust distributed denial of service attacks detection. Kongzhi yu Juece/Control and Decision, 38(03), 28. NorthEast University.
Maureen, A., Irene, D., Abel, E., Asuai, C., & Ufuoma, J. (2023). Unmasking fraudsters: Ensemble features selection to enhance random forest fraud detection. Journal of Computing Theories and Applications, 1(2), 201-211. LPPM and Intelligent System Research Lab Dian Nuswantoro University Semarang. DOI: https://doi.org/10.33633/jcta.v1i2.9462
Clive, A., & Gideon, G. (2023). Enhanced brain tumor image classification using convolutional neural network with attention mechanism. International Journal of Trend in Research and Development, 10(6), 5. IJTRD.
Akazue, M., Oweimieotu, A. E., Edje, A. E., & Asuai, C. (2024). Designing a hybrid genetic algorithm trained feedforward neural network for mental health disorder detection. Journal of Digital Innovations & Contemporary Research in Science, Engineering & Technology, 12(1), 49-62. https://doi.org/10.22624/AIMS/DIGITAL/V11N4P4 DOI: https://doi.org/10.22624/AIMS/DIGITAL/V11N4P4
Oweimieotu, A. E., Akazue, M. I., Edje, A. E., & Asuai, C. (2024). Development of a real-time phishing detection website via a triumvirate of information retrieval, natural language processing, and machine learning modules. International Journal of Trend in Research and Development, 11(1).
Clive, A. E., & Giroh, G. Y. (2023). Enhanced brain tumor image classification using convolutional neural network with attention mechanism. International Journal of Trend in Research and Development, 10(6), 178. https://www.ijtrd.com
Clive, A., Giroh, G., & Obinor, W. (2024). Hybrid quantum-classical strategies for hydrogen variational quantum eigensolver optimization. Iconic Research and Engineering Journal, 7(12), 458-462.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.