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

Clive Asuai, Collins Tobore Atumah and Aghoghovia Agajere Joseph-Brown
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Clive Asuai: Department of Computer Science, Delta State Polytechnic, Otefe-Oghara, Nigeria
Collins Tobore Atumah: Department of Computer Science, Delta State Polytechnic, Otefe-Oghara, Nigeria
Aghoghovia Agajere Joseph-Brown: Department of Mechanical Engineering, Delta State Polytechnic, Otefe-Oghara, Nigeria

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 4, 383-395

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.

Date: 2025
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