Hostname: page-component-77f85d65b8-9nbrm Total loading time: 0 Render date: 2026-03-28T09:37:03.463Z Has data issue: false hasContentIssue false
  • English
  • Français

Improved machine learning models with a similarity-based approach for remaining useful life prediction

Published online by Cambridge University Press:  04 November 2024

F. Isbilen Open the ORCID record for F. Isbilen [Opens in a new window] ,
O. Bektas Open the ORCID record for O. Bektas [Opens in a new window] ,
R. Avsar Open the ORCID record for R. Avsar [Opens in a new window]  and
M. Konar Open the ORCID record for M. Konar [Opens in a new window]
F. Isbilen
Affiliation:
Aircraft Technology, Rumeli University, Istanbul, Türkiye
O. Bektas
Affiliation:
Faculty of Engineering and Natural Sciences, Istanbul Medeniyet University, Istanbul, Türkiye
R. Avsar
Affiliation:
Faculty of Engineering and Natural Sciences, Istanbul Medeniyet University, Istanbul, Türkiye
M. Konar*
Affiliation:
Faculty of Aeronautics and Astronautics, Erciyes University, Kayseri, Türkiye
*
Corresponding author: M. Konar; Email: mkonar@erciyes.edu.tr
Get access Rights & Permissions [Opens in a new window]

Abstract

Cost efficiency is a critical factor in the competitive aviation sector. These efficiency factors force airline operators to develop new approaches in their organizations. Predictive maintenance helps to build scheduling maintenance programs for airline operators or MROs. Scheduled maintenance programs benefit cost efficiency in the aviation sector. Predictive maintenance methods predict the failure time of any equipment. Predictions can be made by analyzing the sensor values from equipment.

In this paper, we predicted the remaining useful life (RUL) of turbofan engines using machine learning models and a similarity-based approach. Sensor datasets from the Prognostics Data Repository of NASA, called CMAPPS, were utilized. Using the FD0002 sub-dataset, a health index (HI) was created, and models were trained. Once the models were trained, train and test HIs were estimated. The predicted test HI was matched with the predicted train HI based on a similarity-based approach, and then a RUL prediction was made. The results obtained were compared with the actual results to calculate the accuracy, and the algorithm that resulted in the maximum accuracy was identified.

We selected six machine learning algorithms and also created an ensemble model by averaging the predictions of six machine learning algorithms for comparing prediction accuracy. The different algorithms were compared to obtain the prediction model with the closest prediction of remaining useful lifecycle in terms of the number of life cycles. This experiment showed us the effect of the similarity-based approach on the basic version of machine learning models for RUL prediction.

Keywords

CMAPPSPredictive maintenanceMachine learningSimilarity base

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1332 , February 2025 , pp. 485 - 505
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Al-Abdaly, N.M., Al-Taai, S.R., Imran, H. and Ibrahim, M. Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation, East.-Euro. J. Enterpr. Technol., 2021, 5, (7), p 113.Google Scholar
An, D., Kim, N.H. and Choi, J.H. Practical options for selecting data-driven or physics-based prognostics algorithms with reviews, Reliab. Eng. Syst. Saf., 2015, 133, pp 223–236.CrossRefGoogle Scholar
Awad, M. and Khanna, R. Support Vector Regression, In Efficient Learning Machines. Apress, Berkeley, CA, 2015.CrossRefGoogle Scholar
Baohui, J., Chunhui, X. and Yaohua, L. Study on optimization method of aircraft maintenance plan based on longest path, J. Appl. Sci., 2013, 13, (16), pp 3354–3357.CrossRefGoogle Scholar
Bektas, O. An adaptive data filtering model for remaining useful life estimation. (Doctoral dissertation, University of Warwick), 2018.Google Scholar
Bektas, O., Marshall, J. and Jones, J.A. Comparison of computational prognostic methods for complex systems under dynamic regimes: A review of perspectives, Arch. Comput. Methods Eng., 2020, 27, (4), pp 999–1011.CrossRefGoogle Scholar
Chao, M.A., Kulkarni, C., Goebel, K. and Fink, O. Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics, Data, 2021, 6, (1), p 5.CrossRefGoogle Scholar
Coble, J. and Hines, J.W. Identifying optimal prognostic parameters from data: a genetic algorithms approach, In Annual Conference of the PHM Society, vol. 1, (1), 2009.CrossRefGoogle Scholar
Fei, N., Gao, Y., Lu, Z. and Xiang, T. Z-score normalization, hubness, and few-shot learning, In Proceedings of the IEEE/CVF International Conference on Computer Vision, 142–151, 2021.CrossRefGoogle Scholar
Karaburun, N.N., Arık Hatipoğlu, S. and Konar, M. SOC estimation of Li-Po battery using machine learning and deep learning methods, J. Aviat., 2024, 8, (1), pp 26–31.CrossRefGoogle Scholar
Kavitha, S., Varuna, S. and Ramya, R. A comparative analysis on linear regression and support vector regression, In 2016 Online International Conference on Green Engineering and Technologies (IC-GET), pp 1–5, 2016.Google Scholar
Kinnison, H.A. and Siddiqui, T. Aviation Maintenance Management (Second Edition), 2013.Google Scholar
Li, F., Zhang, L., Chen, B., Gao, D., Cheng, Y., Zhang, X., Yang, Y., Gao, K., Huang, Z. and Peng, J. A light gradient boosting machine for remainning useful life estimation of aircraft engines, In 21st International Conference on Intelligent Transportation Systems (ITSC), pp 3562–3567, 2018.CrossRefGoogle Scholar
Marill, K.A. Advanced statistics: Linear regression, part I: Simple linear regression, Acad. Emer. Med., 2004, 11, (1), pp 87–93.CrossRefGoogle ScholarPubMed
Mathew, V., Toby, T., Singh, V., Rao, B. and Kumar, M. Prediction of Remaining Useful Lifetime (RUL) of turbofan engine using machine learning, In 2017 IEEE International Conference on Circuits and Systems (ICCS), pp 306–311, 2017.CrossRefGoogle Scholar
Maulana, F., Starr, A. and Ompusunggu, A.P. Explainable data-driven method combined with Bayesian filtering for remaining useful lifetime prediction of aircraft engines using NASA CMAPSS datasets, Machines, 2023, 11, (2), p 163.CrossRefGoogle Scholar
Noronha, G. and Singal, V. Financial health and airline safety, Manag. Decis. Econ., 2004, 25, (1), pp 1–16.CrossRefGoogle Scholar
Patro, S.G.K. and Sahu, K.K. Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462, 2015.Google Scholar
Rodriguez, J.D., Perez, A. and Lozano, J.A. Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans. Pattern Anal. Mach. Intell., 2009, 32, (3), pp 569–575.CrossRefGoogle Scholar
Saxena, A.; Goebel, K.; Simon, D. and Eklund, N. Damage propagation modeling for aircraft engine run-to-failure simulation, In 2008 International Conference on Prognostics and Health Management, p 19, 2008.Google Scholar
NASA. Turbofan Engine Degradation Simulation”, Prognostics Center of Excellence Data Repository, 2008, http://ti.arc.nasa.gov/projects/data_prognostics (accessed on January 2024).Google Scholar
Orel, V.V. Model of transition from MSG2 to MSG3 maintenance logic for airlines, 2024.Google Scholar
Segal, M.R. Machine learning benchmarks and random forest regression, UCSF: Center for Bioinformatics and Molecular Biostatistics, 2004, pp 1–14.Google Scholar
Singh, S.K., Kumar, S. and Dwivedi, J.P. A novel soft computing method for engine Remaining Useful Life prediction, Multimedia Tools Appl., 2019, 78, (9), pp 4065–4087.CrossRefGoogle Scholar
Freund, Y. and Mason, L. The alternating decision tree learning algorithm, In ICML 1999, pp 124–133, 1999.Google Scholar
Chen, X., Jin, G., Qiu, S., Lu, M. and Yu, D. Direct Remaining Useful Life Estimation Based on Random Forest Regression, In 2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai), p 17, 2020.Google Scholar
Verhulst, T., Judt, D., Lawson, C., Chung, Y., Al-Tayawe, O. and Ward, G. Review for state-of-the-art health monitoring technologies on airframe fuel pumps, Int. J. Prognost. Health Manag., 2022, 13, (1), pp 1–20.CrossRefGoogle Scholar
Zelaya, C.V.G. Towards explaining the effects of data preprocessing on machine learning, In 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp 2086–2090, 2019.Google Scholar
Zhang, Y. and Haghani, A. A gradient boosting method to improve travel time prediction, Transp. Res. C: Emerg. Technol., 2015, 58, pp 308–324.CrossRefGoogle Scholar
Wang, T., Yu, J., Siegel, D. and Lee, J. A similarity-based prognostics approach for remaining useful life estimation of engineered systems, In 2008 International Conference on Prognostics and Health Management, pp 1–6, 2008.CrossRefGoogle Scholar