TY - GEN
T1 - Health monitoring framework for aircraft engine system using deep neural network
AU - Lee, Hyunseong
AU - Li, Guoyi
AU - Rai, Ashwin
AU - Chattopadhyay, Aditi
N1 - Funding Information:
The research reported in this paper was supported by funds from NASA University Leadership Initiative program (Contract No. NNX17AJ86A, Project Officer: Dr. Anupa Bajwa). The support is gratefully acknowledged.
Publisher Copyright:
© 2019 Prognostics and Health Management Society. All rights reserved.
PY - 2019/9/23
Y1 - 2019/9/23
N2 - A real-time monitoring framework is developed to detect operational anomalies in aircraft engine performance. A historical flight dataset recorded from commercial aircraft is utilized to perform the proposed method. Sampling frequency synchronization and denoise are performed on the flight dataset using signal processing techniques. A robust detection algorithm using the deep neural network is developed to capture flight performance anomalies that show significant off-nominal behavior in engine related and flight dynamic features. The accuracy and efficiency of the proposed monitoring method are validated through a demonstration of anomaly detection in the aircraft engine system associated with dynamic flight behavior.
AB - A real-time monitoring framework is developed to detect operational anomalies in aircraft engine performance. A historical flight dataset recorded from commercial aircraft is utilized to perform the proposed method. Sampling frequency synchronization and denoise are performed on the flight dataset using signal processing techniques. A robust detection algorithm using the deep neural network is developed to capture flight performance anomalies that show significant off-nominal behavior in engine related and flight dynamic features. The accuracy and efficiency of the proposed monitoring method are validated through a demonstration of anomaly detection in the aircraft engine system associated with dynamic flight behavior.
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U2 - 10.36001/phmconf.2019.v11i1.869
DO - 10.36001/phmconf.2019.v11i1.869
M3 - Conference contribution
AN - SCOPUS:85083965626
T3 - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
BT - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
A2 - Clements, N. Scott
A2 - Zhang, Bin
A2 - Saxena, Abhinav
PB - Prognostics and Health Management Society
T2 - 11th Annual Conference of the Prognostics and Health Management Society, PHM 2019
Y2 - 23 September 2019 through 26 September 2019
ER -