Estimating Cycles to Maintenance Events For Jet Engines Using Engine-specific Measurement Residual
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This paper introduces a data-driven method for predicting remaining cycles to major maintenance events in commercial jet engines, developed for the PHM North America 2025 Data Challenge. The method leverages measurement residuals that capture sensor deviations from expected values after accounting for operating conditions with simple linear models. These residuals serve as interpretable indicators of engine health. Health indices are constructed for High Pressure Turbine and High Pressure Compressor visits, while Compressor Water Wash events are estimated through linear extrapolation.
How to Cite
##plugins.themes.bootstrap3.article.details##
Jet Engines, Remaining useful life, Data challenge, measurement residual
Ellefsen, A. L., Han, P., Cheng, X., Holmeset, F. T., Asoy, V., & Zhang, H. (2020). Online fault detection in autonomous ferries: Using fault-type independent spectral anomaly detection. IEEE Transactions on instrumentation and measurement, 69(10), 8216–8225.
Han, P., Ellefsen, A. L., Li, G., Asoy, V., & Zhang, H. (2021). Fault prognostics using lstm networks: application to marine diesel engine. IEEE Sensors Journal, 21(22), 25986–25994.
Han, P., Ellefsen, A. L., Li, G., Holmeset, F. T., & Zhang, H. (2021). Fault detection with lstm-based variational autoencoder for maritime components. IEEE Sensors Journal, 21(19), 21903–21912.
Han, P., Li, G., Skulstad, R., Skjong, S., & Zhang, H. (2020). A deep learning approach to detect and isolate thruster failures for dynamically positioned vessels using motion data. IEEE Transactions on Instrumentation and Measurement, 70, 1–11.
Han, P., Liang, Q., Vanem, E., Knutsen, K. E., & Zhang, H. (2024). Assessing helicopter turbine engine health: A simple yet robust probabilistic approach. In Annual conference of the phm society (Vol. 16).
Jiao, Z., Wang, H., Xing, J., Yang, Q., Yang, M., Zhou, Y., & Zhao, J. (2023). Lightgbm-based framework for lithium-ion battery remaining useful life prediction under driving conditions. IEEE Transactions on Industrial Informatics, 19(11), 11353–11362.
Liang, Q., Knutsen, K. E., Vanem, E., Æsøy, V., & Zhang, H. (2024). A review of maritime equipment prognostics health management from a classification society perspective. Ocean Engineering, 301, 117619.
Liang, Q., Knutsen, K. E., Vanem, E., Zhang, H., & Æsøy, V. (2023). Unsupervised anomaly detection in marine diesel engines using transformer neural networks and residual analysis. In Phm society asia-pacific conference (Vol. 4).
Liang, Q., Vanem, E., Knutsen, K. E., Æsøy, V., & Zhang, H. (2024). Anomaly detection in time series data: A novel approach using transformer neural networks for reconstruction and residual analysis. International Journal of Prognostics and Health Management, 15(3).
Liang, Q., Vanem, E., Xue, Y., Alnes, Ø., Zhang, H., Lam, J., & Bruvik, K. (2023). Data-driven state of health monitoring for maritime battery systems–a case study on sensor data from ships in operation. Ships and Offshore Structures, 1–13.
Mathew, M. S., Kandukuri, S. T., & Omlin, C. W. (2024). Soft ordering 1-d cnn to estimate the capacity factor of windfarms for identifying the age-related performance degradation. In Phm society european conference (Vol. 8, pp. 9–9).
Que, Z., & Xu, Z. (2019). A data-driven health prognostics approach for steam turbines based on xgboost and dtw. IEEE Access, 7, 93131–93138.
Vanem, E., Liang, Q., Ferreira, C., Agrell, C., Karandikar, N., Wang, S., . . . others (2023). Data-driven approaches to diagnostics and state of health monitoring of maritime battery systems. In Proceedings of the annual conference of the phm society 2023.
Zio, E. (2022). Prognostics and health management (phm): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218, 108119.

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.