Maintenance algorithm for GEnx-B1 engine
Keywords:
maintenance algorithm, RCM, CBM, PdM, engine diagnostics, artificial intelligence, digital twin, reliability, predictive maintenance
Abstract
This article analyzes the maintenance algorithm for the GEnx-B1 engine, focusing on advanced methods for enhancing its efficiency and ensuring optimal performance throughout its operational lifespan. It examines the application of Reliability-Centered Maintenance (RCM), Condition-Based Maintenance (CBM), and Predictive Maintenance (PdM) strategies based on scientific principles, mathematical models, and real-world data. Additionally, the article explores the potential use of artificial intelligence and digital twin technologies for diagnosing engine conditions and predicting failures.
References
[1] Gartner, Inc. (2020). Predictive Maintenance and Advanced Analytics: Leveraging Digital Twins for Future-Ready Maintenance. Gartner Research.
[2] Moubray, J. (1997). Reliability-Centered Maintenance (RCM): Implementation Made Simple. Industrial Press.
[3] Mobley, R. K. (2002). An Introduction to Predictive Maintenance. Elsevier.
[4] Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. McKinsey & Company.
[5] Zhao, R., & Xie, S. (2019). "A Survey on Digital Twin for Aircraft Engines: Concepts, Applications, and Challenges." IEEE Access.
[6] Snee, R. D. (2011). Understanding and Applying Advanced Analytics in Maintenance. John Wiley & Sons.
[7] Shivakumar, S., & Rao, P. S. (2015). "Predictive Maintenance: Integration with Digital Twin for Aviation." Journal of Aerospace Engineering.
[8] Wang, T., & Zhang, C. (2020). "Condition-Based Maintenance and its Future in Aviation: A Review." Journal of Aircraft Maintenance and Engineering.
[9] Liu, H., & Li, W. (2021). "A Framework for Digital Twin-Based Predictive Maintenance in Aerospace." Aerospace Science and Technology.
[2] Moubray, J. (1997). Reliability-Centered Maintenance (RCM): Implementation Made Simple. Industrial Press.
[3] Mobley, R. K. (2002). An Introduction to Predictive Maintenance. Elsevier.
[4] Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. McKinsey & Company.
[5] Zhao, R., & Xie, S. (2019). "A Survey on Digital Twin for Aircraft Engines: Concepts, Applications, and Challenges." IEEE Access.
[6] Snee, R. D. (2011). Understanding and Applying Advanced Analytics in Maintenance. John Wiley & Sons.
[7] Shivakumar, S., & Rao, P. S. (2015). "Predictive Maintenance: Integration with Digital Twin for Aviation." Journal of Aerospace Engineering.
[8] Wang, T., & Zhang, C. (2020). "Condition-Based Maintenance and its Future in Aviation: A Review." Journal of Aircraft Maintenance and Engineering.
[9] Liu, H., & Li, W. (2021). "A Framework for Digital Twin-Based Predictive Maintenance in Aerospace." Aerospace Science and Technology.
Published
2025-06-30
How to Cite
Maturazov, I., & Shodiyev, S. (2025). Maintenance algorithm for GEnx-B1 engine. Journal of Transport, 2(2), 109-112. https://doi.org/10.56143/2181-2438-2025-2-109-112
Section
Air Traffic Control and Aircraft Maintenance
