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Improving EGT sensing data anomaly detection of aircraft auxiliary power unit

查看参考文献35篇

Liu Liansheng 1   Peng Yu 1   Wang Lulu 2,3   Dong Yu 2,3   Liu Datong 1 *   Guo Qing 1  
文摘 The reliability of the on-wing aircraft Auxiliary Power Unit(APU) decides the cost and the comfort of flight to a large degree. The most important function of APU is to help start main engines by providing compressed air. Especially on the condition of sudden shutdown in the air,APU can offer additional thrust for landing. Therefore, its condition monitoring has drawn much attention from the academic and industrial field. Among the on-wing sensing data which can reflect its condition, Exhaust Gas Temperature(EGT) is one of the most important parameters. To ensure the reliability of EGT, one kind of data-driven anomaly detection framework for EGT sensing data is proposed based on the Gaussian Process Regression and Kernel Principal Component Analysis. The situations of one-dimensional and two-dimensional input data for EGT anomaly detection are considered, respectively. The cross-validation experiments are carried out by utilizing the real condition data of APU, which are provided by China Southern Airlines Company Limited Shenyang Maintenance Base. The anomalous stuck condition of EGT sensing data is also detected. Experimental results show that the proposed EGT sensing data anomaly detection method can achieve better performance of false positive ratio, false negative ratio and accuracy.
来源 Chinese Journal of Aeronautics ,2020,33(2):448-455 【核心库】
DOI 10.1016/j.cja.2019.10.001
关键词 Anomaly detection ; Auxiliary power unit ; Condition-based maintenance ; Data-driven framework ; Exhaust gas temperature
地址

1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150080  

2. China Southern Airlines Company Limited Shenyang Maintenance Base, Shenyang, 110169  

3. China Southern Airlines Engineering Technology Research Center, Shenyang, 110169

语种 英文
文献类型 研究性论文
ISSN 1000-9361
学科 航空
基金 国家自然科学基金 ;  中国博士后科学基金
文献收藏号 CSCD:6670709

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引证文献 7 篇

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CSCD被引 6 次

2 Zhou Xingjie Regression model for civil aero-engine gas path parameter deviation based on deep domainadaptation with Res-BP neural network Chinese Journal of Aeronautics,2021,34(1):79-90
CSCD被引 10 次

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