@inproceedings{609829dc17904f3aa3a21a88acd151fe,
title = "Degradation Trend Construction of Aircraft Engine Using Complex Network Model",
abstract = "Health condition monitoring (HCM) of an aircraft engine is crucial to enhance its reliability running. In this paper, a novel method is proposed using the complex model to monitor its degradation trend. With the help of the data collected by multi-sensors, a complex dynamic model is built using the data-driven approach, which aims to achieve the purpose of HCM of aircraft engine. First, the Gath-Geva fuzzy clustering method is utilized for health condition division. Second, the network model based on correlation analysis is conducted. Finally, the dynamic improved logistic model is developed to describe the changes of sensors data of aircraft engine degradation trend. To verify the effectiveness of the proposed method, simulated aircraft gas turbofan engine data is utilized for validation. The results demonstrate that our method is effective to track its degradation process of aircraft gas turbofan engine.",
keywords = "Aircraft engine, Complex network, Correlation analysis, Health condition monitoring",
author = "Yongsheng Huang and Yongbo Li and Khandaker Noman and Shun Wang",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 ; Conference date: 20-10-2021 Through 23-10-2021",
year = "2023",
doi = "10.1007/978-3-030-99075-6_42",
language = "英语",
isbn = "9783030990749",
series = "Mechanisms and Machine Science",
publisher = "Springer Science and Business Media B.V.",
pages = "519--528",
editor = "Hao Zhang and Guojin Feng and Hongjun Wang and Fengshou Gu and Sinha, {Jyoti K.}",
booktitle = "Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering",
}