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Volume 13
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Article

Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning

by
Sadiq T. Bunyan
1,
Zeashan Hameed Khan
2,
Luttfi A. Al-Haddad
3,*,
Hayder Abed Dhahad
3,
Mustafa I. Al-Karkhi
3,
Ahmed Ali Farhan Ogaili
4 and
Zainab T. Al-Sharify
5,6
1
Ministry of Higher Education and Scientific Research, Baghdad 10066, Iraq
2
Interdisciplinary Research Center for Intelligent Manufacturing and Robotics (IRC-IMR), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia
3
Mechanical Engineering Department, University of Technology-Iraq, Baghdad 10066, Iraq
4
Mechanical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad 10052, Iraq
5
Environmental Engineering Department, Al Hikma University College, Baghdad 10052, Iraq
6
Chemical Engineering Department, Birmingham University, Birmingham B15 2TT, UK
*
Author to whom correspondence should be addressed.
Machines 2025, 13(5), 401; https://doi.org/10.3390/machines13050401
Submission received: 28 March 2025 / Revised: 1 May 2025 / Accepted: 9 May 2025 / Published: 11 May 2025
(This article belongs to the Special Issue Advances in Cutting-Edge Technologies and Practices for Engineering Asset and Maintenance Management)
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Abstract

Gas turbines play a crucial role in power generation and aviation, where effective maintenance strategies are essential to ensure reliability. Traditional condition monitoring methods often rely on scheduled inspections, leading to potential downtime and increased maintenance costs. This study presents an AI-driven approach for thermal condition monitoring and the predictive maintenance of gas turbines using machine learning. An Extreme Gradient Boosting (XGBoost)-based classification model was developed to distinguish between healthy and faulty operating conditions based on thermal load data. The dataset, collected over six months from strategically placed thermocouples in the exhaust gas section, was processed to extract key statistical features such as mean temperature, standard deviation, and skewness. The proposed XGBoost model achieved a classification accuracy (CA) of 97.2%, with an F1-score of 96.8%, precision of 97.5%, and recall of 96.1%, demonstrating its effectiveness in detecting anomalies. The results indicate that the integration of machine learning in gas turbine monitoring significantly enhances fault detection capabilities, enabling proactive maintenance strategies and reducing the risk of critical failures. This study provides valuable insights for data-driven maintenance strategies, optimizing operational efficiency and extending the lifespan of gas turbine components. Future work will focus on real-time deployment and further validation with extended datasets.
Keywords: thermal condition monitoring; predictive maintenance; gas turbine; machine learning; XGBoost classification; fault detection; data-driven maintenance thermal condition monitoring; predictive maintenance; gas turbine; machine learning; XGBoost classification; fault detection; data-driven maintenance

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MDPI and ACS Style

Bunyan, S.T.; Khan, Z.H.; Al-Haddad, L.A.; Dhahad, H.A.; Al-Karkhi, M.I.; Ogaili, A.A.F.; Al-Sharify, Z.T. Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning. Machines 2025, 13, 401. https://doi.org/10.3390/machines13050401

AMA Style

Bunyan ST, Khan ZH, Al-Haddad LA, Dhahad HA, Al-Karkhi MI, Ogaili AAF, Al-Sharify ZT. Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning. Machines. 2025; 13(5):401. https://doi.org/10.3390/machines13050401

Chicago/Turabian Style

Bunyan, Sadiq T., Zeashan Hameed Khan, Luttfi A. Al-Haddad, Hayder Abed Dhahad, Mustafa I. Al-Karkhi, Ahmed Ali Farhan Ogaili, and Zainab T. Al-Sharify. 2025. "Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning" Machines 13, no. 5: 401. https://doi.org/10.3390/machines13050401

APA Style

Bunyan, S. T., Khan, Z. H., Al-Haddad, L. A., Dhahad, H. A., Al-Karkhi, M. I., Ogaili, A. A. F., & Al-Sharify, Z. T. (2025). Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning. Machines, 13(5), 401. https://doi.org/10.3390/machines13050401

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