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Comparative analysis of machine learning methods for solving the problem of predicting failures in gas turbine engines
Authors:
Maria Lapina1
, Mikhail Kondrashov2
, Mikhail Babenko1
, Feroz Shaik3
,
B. Deepanraj3
1North-Caucasus Federal University, 355017, Stavropol, Russia
2MIREA – Russian Technological University, Russian Federation, 119454, Moscow, Russia
3Prince Mohammad Bin Fahd University, Al-Khobar, 31952, Saudi Arabia
Received: 5 June 2025
Revised: 30 August 2025
Accepted: 13 September 2025
Published: 30 September 2025
Abstract:
Gas turbine energy technologies are one of the most important components of the modern and advanced energy industry. An important task is to ensure the uninterrupted operation of the equipment in a given period; therefore, monitoring and diagnostics of the technical condition of the equipment continue to play an important role in ensuring the quality of the gas turbine engine. The article examines the work on equipment diagnostics using machine learning. It discusses various solutions for combining machine- learning methods and dealing with unbalanced data to solve the problem of predicting the failure of gas turbine equipment on a dataset that has the above disadvantages. There is a review of the solutions and methods under consideration to deal with the problems of the dataset. At the end, the authors provide a comparative table of the results of the application of the considered solutions based on the quality metrics of the Recall, Precision, F1-score classification, and PR-AUC and ROC-AUC curves.
Keywords:
Machine learning, equipment failure prediction, gas turbine engine, gas turbine power plant, data imbalance, fuzzy logic, SMOTE, Tomek Links
References:
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© 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 3
September 2025
Last Edition
Volume 10
Number 3
September 2025
How to Cite
M. Lapina, M. Kondrashov, M. Babenko, F. Shaik, B. Deepanraj, Comparative Analysis of Machine Learning Methods for Solving the Problem of Predicting Failures in Gas Turbine Engines. Applied Engineering Letters, 10(3), 2025: 171-182.
https://doi.org/10.46793/aeletters.2025.10.3.5
More Citation Formats
Using lean manufacturing to improve process efficiency in a fabrication company
Authors:
Andra Maria Popa1
, Kapil Gupta1
1University of Johannesburg, Mechanical and Industrial Engineering Technology, Johannesburg, South Africa
Received: 29 June 2024
Revised: 20 September 2024
Accepted: 26 September 2024
Published: 30 September 2024
Abstract:
This article presents a case study on improving process efficiency in a mining equipment part fabrication company. The company was facing issues concerning communication, organisation, and workflow processes. This study investigated that ineffective communication among departments was the major weakness which was responsible for the long lead or idle time. This lead time was a waste that affected the company’s productivity. A great amount of time was spent on non-value-added processes. The Kanban Centralised Communication System was implemented. Time study and value stream mapping were also used. A significant improvement in process efficiency from 34% to 85% was achieved by reducing lead time from 4200 minutes to 1680 minutes after streamlining the communication in the company using Kanban.
Keywords:
Lean manufacturing, Kanban, Optimization, Process efficiency, Production lead time, Value stream mapping
References:
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© 2024 by the author. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 3
September 2025
Last Edition
Volume 10
Number 3
September 2025
How to Cite
V.H. Quan, Research and Optimization of Sport Utility Vehicle Aerodynamic Design. Applied Engineering Letters, 9(2), 2024: 105-115.
https://doi.org/10.46793/aeletters.2024.9.2.5
More Citation Formats
Quan, V.H. (2024). Research and Optimization of Sport Utility Vehicle Aerodynamic Design. Applied Engineering Letters, 9(2), 105-115.
https://doi.org/10.46793/aeletters.2024.9.2.5
Quan, Vu Hai, “Research and Optimization of Sport Utility Vehicle Aerodynamic Design.“ Applied Engineering Letters, vol. 9, no. 2, pp. 2024, 105-115.
https://doi.org/10.46793/aeletters.2024.9.2.5
Quan, Vu Hai, 2024. “Research and Optimization of Sport Utility Vehicle Aerodynamic Design.“ Applied Engineering Letters, 9 (2):105-115.
https://doi.org/10.46793/aeletters.2024.9.2.5
Quan, V.H. (2024). Research and Optimization of Sport Utility Vehicle Aerodynamic Design. Applied Engineering Letters, 9(2), pp. 105-115.
doi: 10.46793/aeletters.2024.9.2.5.