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.
Principal component analysis applied to filtered signals for maintenance management
Anwendung der Hauptkomponentenanalyse gefilterter Signale für das Instandhaltungsmanagement
Quality and Reliability Engineering International ; 26 , 6 ; 523-527
2010
5 Seiten, 4 Bilder, 12 Quellen
Article (Journal)
English
Dimensionality Reduction Using Principal Component Analysis Applied to the Gradient
Online Contents | 2015
|Automotive engineering | 1987
|