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Intelligent Helicopter Turbine Engine Fault Diagnosis Using Multi-Head Attentionoa mark
  • Park, Yong Hun ;
  • Oh, Hwan In ;
  • Kim, In Tae ;
  • Lee, So Jung ;
  • Moon, Se Hee ;
  • Park, Gyu Jin ;
  • Park, Jeong Kyu ;
  • Jung, Joon Ha
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Publication Year
2024-11-05
Journal
Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Publisher
Prognostics and Health Management Society
Citation
Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, Vol.16 No.1
Mesh Keyword
Bayesian regressionCatastrophic accidentsEngine torqueFault diagnosis methodFaults diagnosisHealth management systemsOperational dataPowerPrognostic and health managementUncertainty
All Science Classification Codes (ASJC)
Information SystemsElectrical and Electronic EngineeringHealth Information ManagementComputer Science Applications
Abstract
A turbine engine provides power to the helicopter, enabling the helicopter to travel and hover in the air. Since the rotorcraft operates at high altitudes, ensuring safety and maintaining a healthy operational status are crucial at all times. Therefore, a prognostics and health management (PHM) system for the turbine engine must be implemented to predict any anomalies or faults to prevent catastrophic accidents. This research proposes a novel fault diagnosis method for helicopter turbine engines based on operational data acquired from actual aircraft. First, the proposed method predicts engine torque using other operational data while accounting for uncertainty. A Bayesian regression approach is employed to predict the engine torque. The torque margin, defined as the difference between the actual torque and the estimated torque, is then used to diagnose engine faults. Specifically, a multi-head attention mechanism is incorporated to capture interactions between various engine parameters. Additionally, domain adaptation techniques are applied to enhance the model's generalization performance, ensuring robustness across diverse operating conditions. The proposed method is validated using seven different datasets, each acquired from a helicopter engine. Four datasets were used for training, while the remaining three were allocated for testing and validation. The results indicated that the proposed method accurately predicted torque. Furthermore, the fault diagnosis showed promising results, leading to a 3rd-place finish in the 2024 PHM Society Data Challenge in terms of validation score.
ISSN
2325-0178
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37156
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85210238045&origin=inward
DOI
https://doi.org/10.36001/phmconf.2024.v16i1.4193
Journal URL
http://www.phmsociety.org/conferences
Type
Conference
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2023-00240714) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2024-00466279).
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