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Erschienen in:
Implementing Next-Generation Relay Services at Mars in an International Relay Network Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Statistical Methods for Outlier Detection in Space Telemetries

verfasst von : Clémentine Barreyre, Loic Boussouf, Bertrand Cabon, Béatrice Laurent, Jean-Michel Loubes

Erschienen in: Space Operations: Inspiring Humankind's Future

Verlag: Springer International Publishing

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Abstract

Satellites monitoring is an important task to prevent the failure of satellites. For this purpose, a large number of time series are analyzed in order to detect anomalies. In this paper, we provide a review of such analysis focusing on methods that rely on features extraction. In particular, we set up features based on fixed functional bases (Fourier, wavelets, kernel bases...) and databased bases (PCA, KPCA). The outlier detection methods we apply on those features can be distance- or density-based. Those algorithms will be tested on real telemetry data.

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Vorheriges Kapitel Recommendations Emerging from an Analysis of NASA’s Deep Space Communications Capacity
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Literatur
1.
Martínez-Heras, J.-A., Donati, A., Kirsch, M. G., & Schmidt, F. (2012). New telemetry monitoring paradigm with novelty detection. In SpaceOps Conference (pp. 11–15). Sweden: Stockholm.
2.
Fuertes, S., Picart, G., Tourneret, J.-Y., Chaari, L., Ferrari, A., & Richard, C. (2016). Improving spacecraft health monitoring with automatic anomaly detection techniques. In 14th International Conference on Space Operations (p. 2430).
3.
Thomas, A., Feuillard, V., & Gramfort, A. (2015). Calibration of one-class SVM for MV set estimation. In IEEE International Conference on, IEEE Data Science and Advanced Analytics (DSAA) 36678 2015 (pp. 1–9).
4.
Rabenoro, T. (2015). Outils statistiques de traitement d’indicateurs pour le diagnostic et le pronostic des moteurs d’avions. Ph.D. thesis, Université Paris 1 Panthéon Sorbonne.
5.
Abdel-Sayed, M., Duclos, D., Faÿ, G., Lacaille, J., & Mougeot, M. (2016). NMF-based decomposition for anomaly detection applied to vibration analysis. 6, 73–81.
6.
Antoniadis, A., Brossat, X., Cugliari, J., & Poggi, J.-M. (2013). Clustering functional data using wavelets. International Journal of Wavelets, Multiresolution and Information Processing, 11(01), 1350003.MathSciNetCrossRef
7.
Tarpey, T., & Kinateder, K. K. J. (2003). Clustering functional data. Journal Classification, 20(1), 93–114.MathSciNetCrossRef
8.
Auder, B., & Fischer, A. (2012). Projection-based curve clustering. Journal Statistical Computation Simulation, 82(8), 1145–1168.MathSciNetCrossRef
9.
Pan, J., Chen, J., Zi, Y., Li, Y., & He, Z. (2016). Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment. Mechanical Systems and Signal Processing, 72, 160–183.CrossRef
10.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 15.CrossRef
11.
Ramsay, J. O., & Silverman, B. W. (2005). Functional data analysis (2nd ed.). New York: Springer Series in Statistics, Springer.MATH
12.
Daubechies, I. (1992). Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics (Vol. 61). Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
13.
Coifman, R. R., & Wickerhauser, M. V. (1992). Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory, 38(2), 713–718.CrossRef
14.
Steinwart, I. (2001). On the influence of the kernel on the consistency of support vector machines. Journal of Machine Learning Research, 2, 67–93.MathSciNetMATH
15.
Mercer, J. (1909). Functions of positive and negative type and their connection with the theory of integral equations. London Philosophical Transactions Royal Society.
16.
Schölkopf, B., & Smola, A. J. (2000). Learning with Kernels. Cambridge: MIT Press.MATH
17.
Schölkopf, B., Smola, A. J., & Müller, K.-R. (2005). Kernel principal component analysis. Artificial Neural Network.
18.
Schlindwein, F., & Evans, D. (1992). Autoregressive spectral analysis as an alternative to Fast Fourier transform analysis of Doppler ultrasound signals. Doppler Physics and Signal Processing.
19.
Vantini, S. (2012). On the definition of phase and amplitude variability in functional data analysis. Test, 21, 676–696.MathSciNetCrossRef
20.
Wang, K., & Gasser, T. (1997). Alignment of curves by dynamic time warping. Annals of Statistics, 25, 1251–1276.MathSciNetCrossRef
21.
Gamboa, F., Loubes, J.-M., & Maza, E. (2007). Semi-parametric estimation of shifts. Electronic Journal of Statistics, 1, 616–640.MathSciNetCrossRef
22.
Dimeglio, C., Gallón, S., Loubes, J.-M., & Maza, E. (2014). A robust algorithm for template curve estimation based on manifold embedding. Computational Statistics & Data Analysis, 70, 373–386.MathSciNetCrossRef
23.
Hawkins, D. M. (1980). Identification of outliers (Vol. 11). Berlin: Springer.CrossRef
24.
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411–423.MathSciNetCrossRef
25.
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. In ACM sigmod record (Vol. 29-2, pp. 93–104). ACM.
26.
Cadre, B., Pelletier, B., & Pudlo, P. (2013). Estimation of density level sets with a given probability content. Journal of Nonparametric Statistics, 25(1), 261–272.MathSciNetCrossRef
27.
Schölkopf, B., Platt, J. C., Shawe-Taylor, J. C., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computing, 13(7), 1443–1471.CrossRef
28.
Shawe-Taylor, J., & Zlicar, B. (2015). Novelty detection with one-class support vector machines. Advances in Statistical Models for Data Analysis.
29.
Liu, F., Ting, K., & Zhou, Z.-H. (2008). Isolation forest. Data Mining.
30.
Barreyre, C., Laurent, B., Loubes, J.-M., & Cabon, B. (2016). Détection d’événements atypiques dans des données fonctionnelles. Les journées de la Statistique.
31.
Boussouf, L., Bergelin, B., Scudeler, D., Graydon, H., Stamminger, J., Rosnet, P., et al. (2018). Big data based operations for space systems. In 2018 SpaceOps Conference 2018 (p. 2506).
Metadaten
Titel
Statistical Methods for Outlier Detection in Space Telemetries
verfasst von
Clémentine Barreyre
Loic Boussouf
Bertrand Cabon
Béatrice Laurent
Jean-Michel Loubes
Copyright-Jahr
2019
Buch
Space Operations: Inspiring Humankind's Future

Print ISBN: 978-3-030-11535-7

Electronic ISBN: 978-3-030-11536-4

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-11536-4_20

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