TURBOFAN ENGINE HEALTH STATUS PREDICTION WITH ARTIFICIAL NEURAL NETWORK

被引:1
|
作者
Szrama, Slawomir [1 ]
Lodygowski, Tomasz [1 ]
机构
[1] Poznan Univ Tech, Aviat Div, Piotrowo 3, PL-60965 Poznan, Poland
关键词
aircraft turbofan engine; health status prediction; artificial neural network; prognostic health monitoring; engine diagnostics and health monitoring; MODEL;
D O I
10.3846/aviation.2024.22554
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The main purpose of this study is to present the concept of the aircraft turbofan engine health status prediction with artificial neural network augmentation process. The main idea of engine health status prediction is based on the engine health status parameter broadly used in the aviation industry as well as propulsion technology being the performance and safety margin. As a result of research engine health status index is calculated in order to determine the engine degradation level. The calculated parameter is then used as a response parameter for the machine learning algorithm. The case study is based on the artificial neural network which was two-layer feedforward network with sigmoid hidden neurons and linear output neurons. Network performance is evaluated using mean squared error and regression analysis. The final results are analyzed using visualization plots such as regression fit plot and histogram of errors. The greatest achievement of this elaboration is the presentation of how the entire process of engine status prediction might be augmented with the use of an artificial neural network. What is the greatest scientific contribution of the article is the fact that there are no scientific studies available, which are based on the engine real-life operating data.
引用
收藏
页码:225 / 234
页数:10
相关论文
共 50 条
  • [41] Artificial Neural Network for Screw Life Prediction
    Gao, Hongli
    Xu, Mingheng
    Wu, Xixi
    Zhao, Min
    Huang, Haifeng
    Guo, Zhiping
    2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2010, : 4328 - 4332
  • [42] Terrorism prediction using artificial neural network
    Soliman G.M.A.
    Abou-El-Enien T.H.M.
    Revue d'Intelligence Artificielle, 2019, 33 (02) : 81 - 87
  • [43] Artificial Neural Network Application to the Stroke Prediction
    Peng, Chun-Cheng
    Wang, Shih-Hao
    Liu, Syue-Ji
    Yang, Yun-Kai
    Liao, Bo-Han
    PROCEEDINGS OF THE 2ND IEEE EURASIA CONFERENCE ON BIOMEDICAL ENGINEERING, HEALTHCARE AND SUSTAINABILITY 2020 (IEEE ECBIOS 2020): BIOMEDICAL ENGINEERING, HEALTHCARE AND SUSTAINABILITY, 2020, : 130 - 133
  • [44] Prediction of roadheader performance by artificial neural network
    Avunduk, E.
    Tumac, D.
    Atalay, A. K.
    TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, 2014, 44 : 3 - 9
  • [45] Artificial Neural Network Intelligent Method for Prediction
    Trifonov, Roumen
    Yoshinov, Radoslav
    Pavlova, Galya
    Tsochev, Georgi
    MATHEMATICAL METHODS & COMPUTATIONAL TECHNIQUES IN SCIENCE & ENGINEERING, 2017, 1872
  • [46] PREDICTION OF DRAPE COEFFICIENT BY ARTIFICIAL NEURAL NETWORK
    Ghith, Adel
    Hamdi, Thouraya
    Fayala, Faten
    AUTEX RESEARCH JOURNAL, 2015, 15 (04) : 266 - 274
  • [47] Prediction of Diabetes by using Artificial Neural Network
    Sapon, Muhammad Akmal
    Ismail, Khadijah
    Zainudin, Suehazlyn
    CIRCUITS, SYSTEM AND SIMULATION, 2011, 7 : 299 - 303
  • [48] The prediction of the gas emission with artificial neural network
    Xu, Q.
    Zhou, X.
    Man, J.
    Jiang, Q.
    Jin, J.
    Zhu, Y.
    Guo, K.
    Jiao, H.
    Gao, W.
    2019 5TH INTERNATIONAL CONFERENCE ON ENERGY MATERIALS AND ENVIRONMENT ENGINEERING, 2019, 295
  • [49] Artificial neural network prediction of viruses in shellfish
    Brion, G
    Viswanathan, C
    Neelakantan, TR
    Lingireddy, S
    Girones, R
    Lees, D
    Allard, A
    Vantarakis, A
    APPLIED AND ENVIRONMENTAL MICROBIOLOGY, 2005, 71 (09) : 5244 - 5253
  • [50] Artificial Neural Network as a Tool for Backbreak Prediction
    Monjezi M.
    Hashemi Rizi S.M.
    Majd V.J.
    Khandelwal M.
    Geotechnical and Geological Engineering, 2014, 32 (01) : 21 - 30