We propose a multivariate trending for aircraft fault detection. Multivariate trending generate fault indicators using output sensor data, is one of black-box approach. We use convex polygon for the computation of a rough shape or extent of the normal data set. Quickhull algorithm is used for the hull finding because it is simpler and uses less memory. It is assumed that the normal data points are in general position, so that their convex hull is a simple complex. We represent a d-dimensional convex hull by its vertices and (d-1)-dimensional faces. From multivariate trend analysis, if we find the measurements have the tendency to leave the convex polygon, this measurement can be labeled as a fault. If a new point is above all hyperplane of the convex hull, it is outside the convex polygon.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multivariate fault detection with convex hull


    Contributors:
    Luo, M. (author)


    Publication date :

    2004-01-01


    Size :

    348764 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multivariate Fault Detection with Convex Hull

    Luo, M. / IEEE / AIAA | British Library Conference Proceedings | 2004


    Wiggle hull design having a concave and convex planing hull

    ALMETER JOHN | European Patent Office | 2016

    Free access

    Detecting textured objects using convex hull

    Lu, K. / Pavlidis, T. | British Library Online Contents | 2007


    Convex Hull Detection of a Scattering System in an Inhomogeneous Background

    Capozzoli, A. / D Elia, G. / Vinetti, P. et al. | British Library Conference Proceedings | 2006


    Fast SVM Training Based on Thick Convex-hull

    Zhang, Hong-da / Wang, Xiao-dan / Xu, Hai-Long et al. | IEEE | 2008