This paper presents an approach for detecting and identifying faults in railway infrastructure components. The method is based on pattern recognition and data analysis algorithms. Principal component analysis (PCA) is employed to reduce the complexity of the data to two and three dimensions. PCA involves a mathematical procedure that transforms a number of variables, which may be correlated, into a smaller set of uncorrelated variables called 'principal components'. In order to improve the results obtained, the signal was filtered. The filtering was carried out employing a state-space system model, estimated by maximum likelihood with the help of the well-known recursive algorithms such as Kalman filter and fixed interval smoothing. The models explored in this paper to analyse system data lie within the so-called unobserved components class of models.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Principal component analysis applied to filtered signals for maintenance management


    Additional title:

    Anwendung der Hauptkomponentenanalyse gefilterter Signale für das Instandhaltungsmanagement




    Publication date :

    2010


    Size :

    5 Seiten, 4 Bilder, 12 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English






    Principal Component Clustering Analysis Method Applied to Road Traffic Safety

    Jia, Yuanhua / Wu, Guifu / Chang, Sheng | ASCE | 2011


    Principal component analysis of acousto-ultrasonic signals for structural health monitoring

    Shark, L.K. / Zhang, Y. / Boller, C. et al. | Tema Archive | 2008


    Use of principal component analysis for correlation analysis between vibration and acoustical signals

    Leuridan,J. / Roesems,D. / Otte,D. et al. | Automotive engineering | 1987