Estimating the health of turbine engine based on the relationship between torque margin and density altitude
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
We present an anomaly detection method developed for the PHM North America 2024 Conference Data Challenge. This competition is aimed at estimating the health of helicopter turbine engines (PHM Society, 2024). The task includes the estimation of the torque margin (regression) and the health state (binary classification) of turbine engines. We developed an estimation model using a hybrid algorithm that combines data-based machine learning and domain knowledge-based processing. Our method achieved scores over 0.99 for both the testing and validation datasets. based on the calculation rules provided by PHM Society. These results were ranked first among all the participating teams.
How to Cite
##plugins.themes.bootstrap3.article.details##
anomaly detection, machine learning, domain knowledge, rule-based processing
Bechhoefer, E., & Hajimohammadali, M. (2023). Process for Turboshaft Engine Performance Trending. Proceedings of the Annual Conference of the PHM Society 2023. Vol. 15, no.1. doi: 10.36001/phmconf.2023.v15i1.3490
Nakamura, T., Imamura, M., Mercer, R., & Keogh, E. (2020). MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives. 2020 IEEE international conference on data mining (ICDM). doi:10.1109/ICDM50108.2020.00147
Nakamura, T., Mercer, R., Imamura, M., & Keogh, E. (2023). MERLIN++: parameter-free discovery of time series anomalies. Data Mining and Knowledge Discovery, vol. 37, pp. 670-709. doi:10.1007/s10618-022-00876-7
Kato, Y., Kato, T., & Tanaka, T. (2023). Anomaly Detection in Spacecraft Propulsion System using Time Series Classification based on K-NN. Proceedings of the Asia Pacific Conference of the PHM Society 2023. vol. 4, no. 1. doi: 10.36001/phmap.2023.v4i1.3675
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.