Predictive modelling for airline technical operations
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Published
Sep 4, 2023
Dimitri Reiswich
Abstract
The digital platform AVIATAR leverages aircraft and maintenance data as well as advanced algorithms to optimize technical operations. Optimization is achieved by utilizing predictive maintenance algorithms which lead to a reduction of costs and operational incidences. The rise of AI algorithms combined with an increase of the available data to feed those algorithms provides both an opportunity as well as challenge to the traditional approaches which were in use in the aviation industry for decades. We demonstrate how AVIATAR’s data science team develops state of the art predictive maintenance models by combining advanced statistical models and AI with the unique engineering know how of Lufthansa Technik. We will show the immense value of full flight data in order to achieve this and provide an outlook into the potential future role of algorithms in the aviation industry.
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Keywords
airline operations, agile software development, decision support, Predictive health analytics, health management
References
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Yu, C., & Yao, W. (2017). Robust linear regression: A review and comparison. Communications in StatisticsSimulation and Computation, 46(8), 6261–6282.
Zhao, B., Lu, H., Chen, S., Liu, J., & Wu, D. (2017). Convolutional neural networks for time series classification. Journal of Systems Engineering and Electronics, 28(1), 162–169.
Bar-Shalom, Y., Li, X. R., & Kirubarajan, T. (2001). Estimation with applications to tracking and navigation: theory algorithms and software. John Wiley & Sons.
Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of political economy, 81(3), 637–654.
Chui, C. K., Chen, G., et al. (2017). Kalman filtering. Springer. EASA. (2021). Easa concept paper: First usable guidance for level 1 machine learning applications (Tech. Rep.). Author. https://www.easa.europa.eu/ en/easa-concept-paper-first-usable -guidance-level-1-machine-learning -applications-proposed-issue-01pdf.
FAA. (2018). Reliability program methods— standards for determining time limitations (Tech. Rep.). U.S. Department of Transportation Federal Aviation Administration. https:// www.faa.gov/regulations policies/ advisory circulars/index.cfm/go/ document.information/documentid/ 1035253.
Federal Deposit Insurance Corporation. (2005). Model governance. https://www.fdic .gov/regulations/examinations/ supervisory/insights/siwin05/ siwinter05-article1.pdf. ([Online; accessed 08-June-2023])
Federal Reserve. (2022). Approach to supervisory model development and validation.
Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2). Springer.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780. Killick, R., & Eckley, I. (2014). changepoint: An r package for changepoint analysis. Journal of statistical software, 58(3), 1–19.
Macukow, B. (2016). Neural networks–state of art, brief history, basic models and architecture. In Computer information systems and industrial management: 15th ifip tc8 international conference, cisim 2016, vilnius, lithuania, september 14-16, 2016, proceedings 15 (pp. 3–14).
Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2023). Machine learning advances for time series forecasting. Journal of economic surveys, 37(1), 76–111.
Yu, C., & Yao, W. (2017). Robust linear regression: A review and comparison. Communications in StatisticsSimulation and Computation, 46(8), 6261–6282.
Zhao, B., Lu, H., Chen, S., Liu, J., & Wu, D. (2017). Convolutional neural networks for time series classification. Journal of Systems Engineering and Electronics, 28(1), 162–169.
Section
Special Session Papers
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