Assessing Helicopter Turbine Engine Health: A Simple Yet Robust Probabilistic Approach

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Published Nov 6, 2024
Peihua Han Qin Liang Erik Vanem Knut Erik Knutsen Houxiang Zhang

Abstract

This paper presents a data-driven approach for assessing the health of helicopter turbine engines, developed for the PHM North America 2024 Conference Data Challenge. The task involves both regression and classification to estimate the torque margin and classify engine health as either nominal or faulty. To quantify the reliability of predictions, probabilistic outputs are generated. We employ a two-stage model where the predicted torque margin serves as an input feature for health classification. For probabilistic torque margin estimation, we introduce an empirical error sampling method to generate torque margin samples, followed by a rule-based distribution selection scheme to evaluate the resulting distributions. For fault classification, logistic regression is used to provide confidence estimates, and we incorporate a score-optimized loss function during training to mitigate penalties for false negatives. Our approach demonstrates strong generalization to unseen assets, ranking 2nd in the competition with a score of 0.94, demonstrating its effectiveness in predicting health conditions and uncertainty for more informed helicopter engine management.

How to Cite

Han, P., Liang, Q., Vanem, E., Knutsen, K. E., & Zhang, H. (2024). Assessing Helicopter Turbine Engine Health: A Simple Yet Robust Probabilistic Approach. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4186
Abstract 116 | PDF Downloads 74

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Keywords

Data-Driven Approach, Logistic Regression, Custom loss function, Empirical Error Sampling

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Section
Data Challenge Papers

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