Temat: outliers detection - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A robust fixed-lag smoothing algorithm for dynamic systems with correlated sensor malfunctions
Autorzy:: Grishin, Y. P.
Janczak, D.
Powiązania:: https://bibliotekanauki.pl/articles/199833.pdf
Data publikacji:: 2014
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: outliers
robust signal processing
nonlinear filtering
fault detection
systems with changing structures
Opis:: A new robust fixed-lag smoothing algorithm for fault-tolerant signal processing in stochastic dynamic systems in the presence of correlated sensor malfunctions has been developed. The algorithm is developed using a state vector augmentation method and the Gaussian approximation of the current estimate probability density function. The algorithm can be used in the real-time fault-tolerant control systems as well as in radar tracking systems working in the complex interference environment. The performance of the developed algorithm are evaluated by simulations and compared with smoothing and nonlinear filtering algorithms.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2014, 62, 3; 517-523
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Fault detection and isolation with robust principal component analysis
Autorzy:: Tharrault, Y.
Mourot, G.
Ragot, J.
Maquin, D.
Powiązania:: https://bibliotekanauki.pl/articles/929927.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: analiza głównych składowych
odporność
detekcja uszkodzeń
lokalizacja uszkodzeń
principal component analysis
robustness
outliers
fault detection
fault isolation
structured residual vector
variable reconstruction
Opis:: Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for fault detection and isolation. A very simple estimate derived from a one-step weighted variance-covariance estimate is used (Ruiz-Gazen, 1996). This is a 'local' matrix of variance which tends to emphasize the contribution of close observations in comparison with distant observations (outliers). Second, structured residuals are used for multiple fault detection and isolation. These structured residuals are based on the reconstruction principle, and the existence condition of such residuals is used to determine the detectable faults and the isolable faults. The proposed scheme avoids the combinatorial explosion of faulty scenarios related to multiple faults to be considered. Then, this procedure for outliers detection and isolation is successfully applied to an example with multiple faults.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2008, 18, 4; 429-442
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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