Estimating the health of turbine engine based on the relationship between torque margin and density altitude

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Published Nov 6, 2024
Kosei Ozeki Takahiko Masuzaki Takeru Shiraga Koji Wakimoto Takaaki Nakamura

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

Ozeki, K., Masuzaki, T., Shiraga, T., Wakimoto, K., & Nakamura, T. (2024). Estimating the health of turbine engine based on the relationship between torque margin and density altitude. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4191
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Keywords

anomaly detection, machine learning, domain knowledge, rule-based processing

References
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Section
Data Challenge Papers