Remaining Useful Life Prognosis of Aircraft Brakes
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Jan 25, 2022
Theodoros Loutas
Athanasios Oikonomou
Nick Eleftheroglou
Floris Freeman
Dimitrios Zarouchas
Abstract
We investigate the performance of three different data-driven prognostic methodologies towards the Remaining Useful Life estimation of commercial aircraft brakes being continuously monitored for wear. The first approach utilizes a probabilistic multi-state deterioration mathematical model i.e. a Hidden Semi Markov model whilst the second utilizes a nonlinear regression approach through classical Artificial Neural Networks in a Bootstrap fashion in order to obtain prediction intervals to accompany the mean remaining life estimates. The third approach attempts to leverage the highly linear degradation data over time and uses a simple linear regression in a Bayesian framework. All methodologies, when properly trained with historical degradation data, achieve excellent performance in terms of early and accurate prediction of the remaining useful flights that the monitored set of brakes can safely serve. The paper presents a real-world application where it is demonstrated that even in non-complex linear degradation data the inherent data stochasticity prohibits the use of a simple mathematical approaches and asks for methodologies with uncertainty quantification.
##plugins.themes.bootstrap3.article.details##
Keywords
data-driven prognostics, aircraft systems, artificial intelligence, uncertainty quantification, prognostic perfomance
References
Acuna, D. E. & Orchard, M. E. (2017). Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery state-of-charge monitoring, Mechanical Systems and Signal Processing, 85, pp. 827-848, https://doi.org/10.1016/j.mssp.2016.08.029
Adhikari, P.P. & Buderath, M. A framework for aircraft maintenance strategy including CBM, Proceedings of the European Conference Prognostics Health Management Society 2016, pp. 1-10.
Autin, S.; De Martin, A.; Jacazio, G.; Socheleau, J.; Vachtsevanos, G. (2021), International Journal of Prognostics and Health Management, Results of a Feasibility Study of a Prognostic System for Electro-Hydraulic Flight Control Actuators, 12 (3), pp. 1-18. https://doi.org/10.36001/ijphm.2021.v12i3.2935
Che, C.; Wang, H.; Fu, Q.; Ni, X. (2019) Combining multiple deep learning algorithms for prognostic and health management of aircraft, Aerospace Science and Technology, 94, 105423. https://doi.org/10.1016/j.ast.2019.105423
Dalla Vedova, M.D.L.; Germanà, A.; Berri, P.C.; Maggiore, P. (2019). Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms. Aerospace 6 (94) https://doi.org/10.3390/aerospace6090094
Dawn, A,; Kim, N.H.; Choi, J-H. (2015) Practical options for selecting data-driven or physics-based prognostics algorithms with reviews, Reliability Engineering & System Safety, 133, pp. 223-236. https://doi.org/10.1016/j.ress.2014.09.014
Efron, B.; Tibshirani, R.J. (1993) An Introduction to the Bootstrap, Chapman and Hall, New York, https://doi.org/10.1007/978-1-4899-4541-9
Eleftheroglou, N.; Mansouri, S.S.; Loutas, T.; Karvelis, P.; Georgoulas, G.; Nikolakopoulos, G.; Zarouchas, D. (2019). Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification, Applied Energy, 254, 113677. https://doi.org/10.1016/j.apenergy.2019.113677
Eleftheroglou, N.; Zarouchas, D.; Loutas, T.; Alderliesten, R.; Benedictus, R. (2018). Structural health monitoring data fusion for in-situ life prognosis of composite structures, Reliability Engineering & System Safety, 178, pp. 40-54. https://doi.org/10.1016/j.ress.2018.04.031
El-Sayed, M.; Riad, F.; Elsafty, M.; Estaitia, Y. (2017). Algorithms of Confidence Intervals of WG Distribution Based on Progressive Type-II Censoring Samples. Journal of Computer and Communications, 5, pp. 101-116. https://doi: 10.4236/jcc.2017.57011.
Ezhilarasu, C.M.; Skaf, Z.; Jennions, I.K. (2019). The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities, Progress in Aerospace Sciences, 105 pp. 60-73, https://doi.org/10.1016/j.paerosci.2019.01.001
Goebel, K.; Daigle, M.; Saxena, A.; Sankararaman, S.; Roychoudhury, I.; Celaya, (2017), Prognostics: The science of prediction, CA, CreateSpace Independent Publishing Platform; 1st ed.
Jia, X.; Huang, B.; Feng, J.; Cai, H.; Lee, J. (2018). A Review of PHM Data Competitions from 2008 to 2017: Methodologies and Analytics. Proceedings of the Annual Conference of the Prognostics and Health Management Society, Philadelphia, Pennsylvania, USA.
Kallen, M.J. & van Noortwijk, J.M. (2005) Optimal maintenance decisions under imperfect inspection, Reliability Engineering and System Safety, 90 (2-3), pp. 177-185. https://doi.org/10.1016/j.ress.2004.10.004
Khosravi, A., Nahavandi, S., Creighton, D. and Atiya, A. F. (2011). Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances, IEEE Transactions on Neural Networks, 22 (9) pp. 1341-1356, doi: 0.1109/TNN.2011.2162110.
Lee, J. & Mitici, M. (2020). An integrated assessment of safety and efficiency of aircraft maintenance strategies using agent-based modelling and stochastic Petri nets, Reliability Engineering & System Safety, 202, 107052. https://doi.org/10.1016/j.ress.2020.107052
Li, R.; Verhagen, W.J.C.; Curran, R. (2020) Toward a methodology of requirements definition for prognostics and health management system to support aircraft predictive maintenance, Aerospace Science and Technology, 102, 105877. https://doi.org/10.1016/j.ast.2020.105877
Loutas, T.; Eleftheroglou, N.; Zarouchas, D. (2017) A data-driven probabilistic framework towards the in-situ prognostics of fatigue life of composites based on acoustic emission data, Composite Structures, 161, pp. 522-529. https://doi.org/10.1016/j.compstruct.2020.112386
Loutas, T.; Eleftheroglou, N.; Georgoulas, G.; Loukopoulos, P.; Mba D.; Bennett, I. (2020). Valve Failure Prognostics in Reciprocating Compressors Utilizing Temperature Measurements, PCA-Based Data Fusion, and Probabilistic Algorithms, IEEE Transactions on Industrial Electronics, 67 (6), pp. 5022-5029, doi: 10.1109/TIE.2019.2926048.
Lu, F.; Wu, J.; Huang, J.; Qiu, X. (2019). Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm, Aerospace Science and Technology, 84, pp. 661-671. https://doi.org/10.1016/j.ast.2018.09.044
Moghaddass, R.; Zuo, M. J. (2014). An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process, Reliability Engineering & System Safety, 124, pp. 92-104. https://doi.org/10.1016/j.ress.2013.11.006
Nix, D.A.; Weigend, A.S. (1995). Learning local error bars for nonlinear regression, Advances in Neural Information Processing Systems, vol. 7, G. Tesauro, D. Touretzky, and T. Leen, Eds. Cambridge, MA, USA: MIT Press, pp. 489–496.
Pierce, S. G.; Worden, K.; Bezazi, A. (2008). Uncertainty analysis of a neural network used for fatigue lifetime prediction, Mechanical Systems Signal Processing, 22 (6), pp. 1395–1411. https://doi.org/10.1016/j.ymssp.2007.12.004
Rengasamy, D.; Jafari, M.; Rothwell, B.; Chen X.; Figueredo, G. (2020). Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management, Sensors, 20 (3), 723; https://doi.org/10.3390/s20030723
Sankararaman, S. & Goebel, K. (2020) Uncertainty in prognostics and systems health management, International journal of prognostics and health management, pp.1-14 https://doi.org/10.36001/ijphm.2015.v6i4.2319.
Strategic Research & Innovation Agenda, Vol. 2, Advisory Council for Aviation Research and Innovation in Europe (ACARE), September 2012, www.acare4europe.com
Saxena A, Celaya J, Saha B, Saha S, Goebel K. (2020) Metrics for offline evaluation of prognostic performance, International. Journal Prognostics Health Management, 1, pp.1–20.
Tipping, M. Sparse Bayesian learning and the relevance vector machine, Journal of machine learning research 1, 2001, pp. 211-244.
Verstraete, D.; Droguett, E.; Modarres, M. A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics, Sensors 2020, 20(1), 176; https://doi.org/10.3390/s20010176
Adhikari, P.P. & Buderath, M. A framework for aircraft maintenance strategy including CBM, Proceedings of the European Conference Prognostics Health Management Society 2016, pp. 1-10.
Autin, S.; De Martin, A.; Jacazio, G.; Socheleau, J.; Vachtsevanos, G. (2021), International Journal of Prognostics and Health Management, Results of a Feasibility Study of a Prognostic System for Electro-Hydraulic Flight Control Actuators, 12 (3), pp. 1-18. https://doi.org/10.36001/ijphm.2021.v12i3.2935
Che, C.; Wang, H.; Fu, Q.; Ni, X. (2019) Combining multiple deep learning algorithms for prognostic and health management of aircraft, Aerospace Science and Technology, 94, 105423. https://doi.org/10.1016/j.ast.2019.105423
Dalla Vedova, M.D.L.; Germanà, A.; Berri, P.C.; Maggiore, P. (2019). Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms. Aerospace 6 (94) https://doi.org/10.3390/aerospace6090094
Dawn, A,; Kim, N.H.; Choi, J-H. (2015) Practical options for selecting data-driven or physics-based prognostics algorithms with reviews, Reliability Engineering & System Safety, 133, pp. 223-236. https://doi.org/10.1016/j.ress.2014.09.014
Efron, B.; Tibshirani, R.J. (1993) An Introduction to the Bootstrap, Chapman and Hall, New York, https://doi.org/10.1007/978-1-4899-4541-9
Eleftheroglou, N.; Mansouri, S.S.; Loutas, T.; Karvelis, P.; Georgoulas, G.; Nikolakopoulos, G.; Zarouchas, D. (2019). Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification, Applied Energy, 254, 113677. https://doi.org/10.1016/j.apenergy.2019.113677
Eleftheroglou, N.; Zarouchas, D.; Loutas, T.; Alderliesten, R.; Benedictus, R. (2018). Structural health monitoring data fusion for in-situ life prognosis of composite structures, Reliability Engineering & System Safety, 178, pp. 40-54. https://doi.org/10.1016/j.ress.2018.04.031
El-Sayed, M.; Riad, F.; Elsafty, M.; Estaitia, Y. (2017). Algorithms of Confidence Intervals of WG Distribution Based on Progressive Type-II Censoring Samples. Journal of Computer and Communications, 5, pp. 101-116. https://doi: 10.4236/jcc.2017.57011.
Ezhilarasu, C.M.; Skaf, Z.; Jennions, I.K. (2019). The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities, Progress in Aerospace Sciences, 105 pp. 60-73, https://doi.org/10.1016/j.paerosci.2019.01.001
Goebel, K.; Daigle, M.; Saxena, A.; Sankararaman, S.; Roychoudhury, I.; Celaya, (2017), Prognostics: The science of prediction, CA, CreateSpace Independent Publishing Platform; 1st ed.
Jia, X.; Huang, B.; Feng, J.; Cai, H.; Lee, J. (2018). A Review of PHM Data Competitions from 2008 to 2017: Methodologies and Analytics. Proceedings of the Annual Conference of the Prognostics and Health Management Society, Philadelphia, Pennsylvania, USA.
Kallen, M.J. & van Noortwijk, J.M. (2005) Optimal maintenance decisions under imperfect inspection, Reliability Engineering and System Safety, 90 (2-3), pp. 177-185. https://doi.org/10.1016/j.ress.2004.10.004
Khosravi, A., Nahavandi, S., Creighton, D. and Atiya, A. F. (2011). Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances, IEEE Transactions on Neural Networks, 22 (9) pp. 1341-1356, doi: 0.1109/TNN.2011.2162110.
Lee, J. & Mitici, M. (2020). An integrated assessment of safety and efficiency of aircraft maintenance strategies using agent-based modelling and stochastic Petri nets, Reliability Engineering & System Safety, 202, 107052. https://doi.org/10.1016/j.ress.2020.107052
Li, R.; Verhagen, W.J.C.; Curran, R. (2020) Toward a methodology of requirements definition for prognostics and health management system to support aircraft predictive maintenance, Aerospace Science and Technology, 102, 105877. https://doi.org/10.1016/j.ast.2020.105877
Loutas, T.; Eleftheroglou, N.; Zarouchas, D. (2017) A data-driven probabilistic framework towards the in-situ prognostics of fatigue life of composites based on acoustic emission data, Composite Structures, 161, pp. 522-529. https://doi.org/10.1016/j.compstruct.2020.112386
Loutas, T.; Eleftheroglou, N.; Georgoulas, G.; Loukopoulos, P.; Mba D.; Bennett, I. (2020). Valve Failure Prognostics in Reciprocating Compressors Utilizing Temperature Measurements, PCA-Based Data Fusion, and Probabilistic Algorithms, IEEE Transactions on Industrial Electronics, 67 (6), pp. 5022-5029, doi: 10.1109/TIE.2019.2926048.
Lu, F.; Wu, J.; Huang, J.; Qiu, X. (2019). Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm, Aerospace Science and Technology, 84, pp. 661-671. https://doi.org/10.1016/j.ast.2018.09.044
Moghaddass, R.; Zuo, M. J. (2014). An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process, Reliability Engineering & System Safety, 124, pp. 92-104. https://doi.org/10.1016/j.ress.2013.11.006
Nix, D.A.; Weigend, A.S. (1995). Learning local error bars for nonlinear regression, Advances in Neural Information Processing Systems, vol. 7, G. Tesauro, D. Touretzky, and T. Leen, Eds. Cambridge, MA, USA: MIT Press, pp. 489–496.
Pierce, S. G.; Worden, K.; Bezazi, A. (2008). Uncertainty analysis of a neural network used for fatigue lifetime prediction, Mechanical Systems Signal Processing, 22 (6), pp. 1395–1411. https://doi.org/10.1016/j.ymssp.2007.12.004
Rengasamy, D.; Jafari, M.; Rothwell, B.; Chen X.; Figueredo, G. (2020). Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management, Sensors, 20 (3), 723; https://doi.org/10.3390/s20030723
Sankararaman, S. & Goebel, K. (2020) Uncertainty in prognostics and systems health management, International journal of prognostics and health management, pp.1-14 https://doi.org/10.36001/ijphm.2015.v6i4.2319.
Strategic Research & Innovation Agenda, Vol. 2, Advisory Council for Aviation Research and Innovation in Europe (ACARE), September 2012, www.acare4europe.com
Saxena A, Celaya J, Saha B, Saha S, Goebel K. (2020) Metrics for offline evaluation of prognostic performance, International. Journal Prognostics Health Management, 1, pp.1–20.
Tipping, M. Sparse Bayesian learning and the relevance vector machine, Journal of machine learning research 1, 2001, pp. 211-244.
Verstraete, D.; Droguett, E.; Modarres, M. A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics, Sensors 2020, 20(1), 176; https://doi.org/10.3390/s20010176
Section
Technical Papers