Proactive Aircraft Engine Removal Planning with Dynamic Bayesian Networks
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Aircraft engine removal for maintenance is an expensive ordeal, and planning for it while balancing fleet stability objectives is a complex multi-faceted challenge. This is further compounded by uncertainties associated with usage or condition-based maintenance approaches that are becoming prevalent. Engine removal decisions rely on accurate estimation of damage growth or remaining useful life of critical components and a framework for aggregating these component-level estimates (and their uncertainties) into an engine-level removal forecasting model. An approach to this planning challenge is to develop probabilistic prognostic digital twins tailored to engine-specific operations and calibrate/update them with inspection data from the field. To this end, this work outlines a framework involving: 1) building component-level probabilistic models capable of forecasting damage growth or remaining useful life, 2) aggregating the outputs of these component-level models into a system-level view using a Dynamic Bayesian Network (DBN), and 3) updating the states of the DBN with inspection information as and when they become available.
How to Cite
##plugins.themes.bootstrap3.article.details##
Prognostics, Probabilistic Digital Twins, Dynamic Bayesian Networks, Cumulative Damage Modeling, Aircraft Engine Maintenance
Asher, I., Ling, Y., & Wang, L. (2018). Improving sir with constrained resampling for dynamic bayesian network applications. In 2018 aiaa non-deterministic approaches conference (p. 1406).
Bartram, G., & Mahadevan, S. (2013). Dynamic bayesian networks for prognosis. In Annual conference of the phm society (Vol. 5).
Bhaduri, A., Ravi, S. K., Pidaparthi, B. K., Jacobs, R., Pandita, P., Zhang, F., . . . others (2024). A probabilistic framework for uncertainty quantification in the presence of aleatory and epistemic inputs: Application to sailplane payload delivery. In Aiaa scitech 2024 forum (p. 0943).
Choe, D.-E., Kim, H.-C., & Kim, M.-H. (2021). Sequencebased modeling of deep learning with lstm and gru networks for structural damage detection of floating offshore wind turbine blades. Renewable Energy, 174, 218–235.
Ghosh, S., Pandita, P., Atkinson, S., Subber, W., Zhang, Y., Kumar, N. C., . . . Wang, L. (2020). Advances in bayesian probabilistic modeling for industrial applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(3), 030904.
Kennedy, M. C., & O’Hagan, A. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(3), 425–464.
Li, C., Mahadevan, S., Ling, Y., Choze, S., & Wang, L. (2017). Dynamic bayesian network for aircraft wing health monitoring digital twin. Aiaa Journal, 55(3), 930–941.
Li, C., Mahadevan, S., Ling, Y., Wang, L., & Choze, S. (2017). A dynamic bayesian network approach for digital twin. In 19th aiaa non-deterministic approaches conference (p. 1566).
Luan, L., Jacobs, R., Ghosh, S., & Wang, L. (2023). Physics discovery of engineering applications with constrained optimization and genetic programming. In Turbo expo: Power for land, sea, and air (Vol. 87066, p. V11BT25A005).
Luan, L., Jacobs, R., Ghosh, S., &Wang, L. (2024). Physics informed research assistant for theory extraction (pirate) for missing physics discovery. In Aiaa scitech 2024 forum (p. 0171).
Nascimento, R. G., & Viana, F. A. (2020). Cumulative damage modeling with recurrent neural networks. AIAA Journal, 58(12), 5459–5471.
Pidaparthi, B., Li, P., & Missoum, S. (2022). Entropy-based optimization for heat transfer enhancement in tubes with helical fins. Journal of Heat Transfer, 144(1), 012001.
Pidaparthi, B., & Missoum, S. (2019). Stochastic optimization of nonlinear energy sinks for the mitigation of limit cycle oscillations. AIAA journal, 57(5), 2134–2144.
Pidaparthi, B., & Missoum, S. (2023). A multi-fidelity approach for reliability assessment based on the probability of classification inconsistency. Journal of Computing and Information Science in Engineering, 23(1), 011008.
Pidaparthi, B., Missoum, S., Xu, B., & Li, P. (2023). Helical fins for concentrated solar receivers: Design optimization and entropy analysis. Journal of Energy Resources Technology, 145(12), 121706.
Ravi, S. K., Dong, P., & Wei, Z. (2022). Data-driven modeling of multiaxial fatigue in frequency domain. Marine Structures, 84, 103201.
Ravi, S. K., Pandita, P., Ghosh, S., Bhaduri, A., Andreoli, V., & Wang, L. (2023). Probabilistic transfer learning through ensemble probabilistic deep neural network. In Aiaa scitech 2023 forum (p. 1479).
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine runto- failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., . . . Hu, Z. (2022). A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies. Structural and Multidisciplinary Optimization, 65(12), 354.
Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., . . . Hu, Z. (2023). A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives. Structural and multidisciplinary optimization, 66(1), 1.
Tipping, M. E. (2001). Sparse bayesian learning and the relevance vector machine. Journal of machine learning research, 1(Jun), 211–244.
Tsilifis, P., Ravi, S. K., Zhang, F., Pandita, P., Bhaduri, A., Kulkarni, A., & Wang, L. (2023). Towards sparse and interpretable classification for damage assessment of jet engine components. In Turbo expo: Power for land, sea, and air (Vol. 87066, p. V11BT25A002).
VanStone, R., Gooden, O., & Krueger, D. (1988). Advanced cumulative damage modeling. Materials Laboratory, Air Force Wright Aeronautical Laboratories, Air Force
Wu, J.-Y., Wu, M., Chen, Z., Li, X.-L., & Yan, R. (2021). Degradation-aware remaining useful life prediction with lstm autoencoder. IEEE Transactions on Instrumentation and Measurement, 70, 1–10.
Zhang, S., Jacobs, R., Ghosh, S., Kulkarni, A., & Wang, L. (2022). Automated data-driven physics discovery of turbine component damage. In Turbo expo: Power for land, sea, and air (Vol. 86076, p. V08BT25A008).
Zhao, R., Wang, J., Yan, R., & Mao, K. (2016). Machine health monitoring with lstm networks. In 2016 10th international conference on sensing technology (icst) (pp. 1–6).
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.