Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning
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 prese...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/5/401 |
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| author | Sadiq T. Bunyan Zeashan Hameed Khan Luttfi A. Al-Haddad Hayder Abed Dhahad Mustafa I. Al-Karkhi Ahmed Ali Farhan Ogaili Zainab T. Al-Sharify |
| author_facet | Sadiq T. Bunyan Zeashan Hameed Khan Luttfi A. Al-Haddad Hayder Abed Dhahad Mustafa I. Al-Karkhi Ahmed Ali Farhan Ogaili Zainab T. Al-Sharify |
| author_sort | Sadiq T. Bunyan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e3f55b2c76e4475e996c8af6e50909f6 |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-e3f55b2c76e4475e996c8af6e50909f62025-08-20T01:56:28ZengMDPI AGMachines2075-17022025-05-0113540110.3390/machines13050401Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine LearningSadiq T. Bunyan0Zeashan Hameed Khan1Luttfi A. Al-Haddad2Hayder Abed Dhahad3Mustafa I. Al-Karkhi4Ahmed Ali Farhan Ogaili5Zainab T. Al-Sharify6Ministry of Higher Education and Scientific Research, Baghdad 10066, IraqInterdisciplinary Research Center for Intelligent Manufacturing and Robotics (IRC-IMR), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi ArabiaMechanical Engineering Department, University of Technology-Iraq, Baghdad 10066, IraqMechanical Engineering Department, University of Technology-Iraq, Baghdad 10066, IraqMechanical Engineering Department, University of Technology-Iraq, Baghdad 10066, IraqMechanical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad 10052, IraqEnvironmental Engineering Department, Al Hikma University College, Baghdad 10052, IraqGas 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.https://www.mdpi.com/2075-1702/13/5/401thermal condition monitoringpredictive maintenancegas turbinemachine learningXGBoost classificationfault detection |
| spellingShingle | Sadiq T. Bunyan Zeashan Hameed Khan Luttfi A. Al-Haddad Hayder Abed Dhahad Mustafa I. Al-Karkhi Ahmed Ali Farhan Ogaili Zainab T. Al-Sharify Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning Machines thermal condition monitoring predictive maintenance gas turbine machine learning XGBoost classification fault detection |
| title | Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning |
| title_full | Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning |
| title_fullStr | Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning |
| title_full_unstemmed | Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning |
| title_short | Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning |
| title_sort | intelligent thermal condition monitoring for predictive maintenance of gas turbines using machine learning |
| topic | thermal condition monitoring predictive maintenance gas turbine machine learning XGBoost classification fault detection |
| url | https://www.mdpi.com/2075-1702/13/5/401 |
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