Fusion and Comparison of Prognostic Models for Remaining Useful Life of Aircraft systems

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Published Oct 26, 2023
Shuai Fu Nicolas P. Avdelidis Angelos Plastropoulos Ip-Shing Fan

Abstract

Changes in the performance of an aircraft system will straightforwardly affect the safe operation of the aircraft, and the technical requirements of Prognostics and Health Management (PHM) are highly relevant. Remaining Useful Life (RUL) prediction, part of the core technologies of PHM, is a cutting-edge innovation being worked on lately and an effective means to advance the change of upkeep support mode and work on the framework's security, unwavering quality, and economic reasonableness. This paper summarizes a detailed preliminary literature review and comparison of different prognostic approaches and the forecasting methods' taxonomy, the methodology's details, and provides its application to aircraft systems. It also provides a brief introduction to the predictive maintenance concept and condition-based maintenance (CBM). This article uses several predictive models to predict RUL and classifies conventional regression algorithms according to the similarity in function and form of the algorithms. More classical algorithms in each category are selected to compare the prediction results, and finally, the combined effects of the RUL prediction are obtained by weighted fusion, accuracy, and compatibility. The performance of the proposed models is assessed based on evaluations of RUL acquired from the hybrid and individual predictive models. This correlation depends on the most current prognostic metrics. The outcomes show that the proposed strategy develops precision, robustness, and adaptability. Hence, the work in this paper shall enrich the advancement of predictive maintenance and modern innovation of prognostic development.

How to Cite

Fu, S., P. Avdelidis, N. ., Plastropoulos, A., & Fan, I.-S. (2023). Fusion and Comparison of Prognostic Models for Remaining Useful Life of Aircraft systems. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3505
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Keywords

prognostis and health management, remaining useful life, hybrid prognostics, predictive maintenance, condition based maintenance, aircraft systems

References
Behbahani, A. R., von Moll, A., Zeller, R., & Ordo, J. (2014). Aircraft integration challenges and opportunities for distributed intelligent control, power, thermal management, diagnostic and prognostic systems. SAE Technical Papers, 2014-Septe(September). https://doi.org/10.4271/2014-01-2161

Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2021). Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics. Data, 6(1), 1–14. https://doi.org/10.3390/data6010005

Daigle, M., & Goebel, K. (2011). Multiple damage progression paths in model-based prognostics. IEEE Aerospace Conference Proceedings. https://doi.org/10.1109/AERO.2011.5747574

Daigle, M. J., & Goebel, K. (2013). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 43(3), 535–546. https://doi.org/10.1109/TSMCA.2012.2207109

García Nieto, P. J., García-Gonzalo, E., Sánchez Lasheras, F., & de Cos Juez, F. J. (2015). Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliability Engineering and System Safety, 138, 219–231. https://doi.org/10.1016/j.ress.2015.02.001

Garga, A. K., McClintic, K. T., Campbell, R. L., Yang, C.-C., Lebold, M. S., Hay, T. A., & Byington, C. S. (2001). Hybrid reasoning for prognostic learning in CBM systems. IEEE Aerospace Conference Proceedings, 6, 62957–62969. https://doi.org/10.1109/AERO.2001.931316

Heimes, F. O. (2008). Recurrent neural networks for remaining useful life estimation. 2008 International Conference on Prognostics and Health Management, PHM 2008. https://doi.org/10.1109/PHM.2008.4711422
Jacome, A., Hissel, D., Heiries, V., Gerard, M., & Rosini, S. (2019). A review of model-based prognostic for proton exchange membrane fuel cell under automotive load cycling. 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings. https://doi.org/10.1109/VPPC46532.2019.8952411

Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012

Javed, K., Gouriveau, R., & Zerhouni, N. (2015). A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering. IEEE Transactions on Cybernetics, 45(12), 2626–2639. https://doi.org/10.1109/TCYB.2014.2378056

Kai Goebel, Matthew J. Daigle, Abhinav Saxena, Shankar Sankararaman, Indranil Roychoudhury, & Jose Celaya. (2017). Prognostics: The Science of Making Predictions. CreateSpace Independent Publishing Platform.

Kim, S., Choi, J.-H., & Kim, N. H. (2021). Challenges and opportunities of system-level prognostics. Sensors, 21(22). https://doi.org/10.3390/s21227655

Kothamasu, R., Huang, S. H., & Verduin, W. H. (2009). System health monitoring and prognostics - A review of current paradigms and practices. In Handbook of Maintenance Management and Engineering. https://doi.org/10.1007/978-1-84882-472-0_14

Liao, L., & Köttig, F. (2014). Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Transactions on Reliability, 63(1), 191–207. https://doi.org/10.1109/TR.2014.2299152

Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008). Model-based prognostic techniques applied to a suspension system. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 38(5), 1156–1168. https://doi.org/10.1109/TSMCA.2008.2001055

Mudholkar, G. S., Asubonteng, K. O., & Hutson, A. D. (2009). Transformation of the bathtub failure rate data in reliability for using Weibull-model analysis. Statistical Methodology, 6(6), 622–633. https://doi.org/10.1016/j.stamet.2009.07.003

Muller, A., Suhner, M.-C., & Iung, B. (2008). Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliability Engineering and System Safety, 93(2), 234–253. https://doi.org/10.1016/j.ress.2006.12.004

Ochella, S., & Shafiee, M. (2020). Artificial intelligence in prognostic maintenance. Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019, 3424–3431. https://doi.org/10.3850/978-981-11-2724-3_0188-cd

Orsagh, R. F., Sheldon, J., & Klenke, C. J. (2003). Prognostics/diagnostics for gas turbine engine bearings. IEEE Aerospace Conference Proceedings, 7, 1165–1173. https://doi.org/10.1109/AERO.2003.1234152

Prakash, O., & Samantaray, A. K. (2016). Model-based diagnosis and prognosis of hybrid dynamical systems with dynamically updated parameters. In Bond Graphs for Modelling, Control and Fault Diagnosis of Engineering Systems, Second Edition. https://doi.org/10.1007/978-3-319-47434-2_6

Ren, H., Chen, X., & Chen, Y. (2017). Structural Health Monitoring and Influence on Current Maintenance. Reliability Based Aircraft Maintenance Optimization and Applications, 173–184. https://doi.org/10.1016/B978-0-12-812668-4.00009-5

Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. 2008 International Conference on Prognostics and Health Management, PHM 2008. https://doi.org/10.1109/PHM.2008.4711414

Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering, 2015. https://doi.org/10.1155/2015/793161

Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2007). Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Intelligent Fault Diagnosis and Prognosis for Engineering Systems, 1–434. https://doi.org/10.1002/9780470117842

Vogl, G. W., Weiss, B. A., & Helu, M. (2019). A review of diagnostic and prognostic capabilities and best practices for manufacturing. Journal of Intelligent Manufacturing, 30(1), 79–95. https://doi.org/10.1007/s10845-016-1228-8

Zhang, Q., Tse, P. W.-T., Wan, X., & Xu, G. (2015). Remaining useful life estimation for mechanical systems based on similarity of phase space trajectory. Expert Systems with Applications, 42(5), 2353–2360. https://doi.org/10.1016/j.eswa.2014.10.041
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Poster Presentations