Within the context of preventive health maintenance in complex engineering systems, novel sensor fault detection methodologies are developed for an aircraft auxiliary power unit. Promising results at operational and sensor failure conditions are obtained for temperature and pressure sensors. In the methodology proposed, first covariance and noise analyses of sensor data are performed. Next, auto-associative and hetero-associative neural networks for sensor validation are designed and trained. These neural networks are used together to provide validation for pressure and temperature sensors. The last step consists of development of detection and identification logic for sensor faults. In spite o high noise levels, the methodology is shown to be very robust. More than 90% correct sensor failure detection is achieved when noise on the order of noise inherently present in sensor readings is added.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Neural-networks-based sensor validation and recovery methodology for advanced aircraft engines


    Contributors:
    Uluyol, O. (author) / Buczak, A.L. (author) / Nwadiogbu, E. (author)


    Publication date :

    2001


    Size :

    8 Seiten, 5 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Neural network-based sensor validation for turboshaft engines

    Moller, James / Litt, Jonathan / Guo, Ten-Huei | AIAA | 1998


    Torque Sensor Validation and Signal Recovery for Turboshaft Engines

    Byington, C. / Hoffman, J. / Lee, H. et al. | British Library Conference Proceedings | 2011


    Methodology for Control of Helicopters Aircraft Engines Technical State in Flight Modes Using Neural Networks

    Vladov, Serhii / Shmelov, Yurii / Yakovliev, Ruslan | TIBKAT | 2022

    Free access

    Advanced estimation for aircraft engines

    Viassolo, D.E. / Adibhatla, S. / Brunell, B.J. et al. | Tema Archive | 2007


    Heat recovery from aircraft engines

    Pasini, S. / Ghezzi, U. / Andriani, R. et al. | AIAA | 2000