Spacecraft telemetry data monitoring by dimensionality reduction techniques

Proceedings of the SICE Annual Conference

Volumn , Issue , 2010, Pages 1230-1234

  Yairi, Takehisa ^a   Inui, Minoru ^a   Yoshiki, Akihiro ^a   Kawahara, Yoshinobu ^b   Takata, Noboru ^c  

^a UNIVERSITY OF TOKYO   (Japan)

^b OSAKA UNIVERSITY   (Japan)

^c JAPAN AEROSPACE EXPLORATION AGENCY   (Japan)

Author keywords

Anomaly detection; Dimensionaly reduction; Spacecraft

Indexed keywords

MAPPING; SPACECRAFT; STATISTICAL TESTS;

ANOMALY DETECTION; ANOMALY DETECTION FRAMEWORKS; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION TECHNIQUES; ELECTRICAL POWER SUBSYSTEMS; LOW-DIMENSIONAL SPACES; NONLINEAR DIMENSIONALITY REDUCTION; RECONSTRUCTION TECHNIQUES;

DATA REDUCTION;

EID: 78649305969     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. B. C. Williams and P. P. Nayak, "A model-based approach to reactive self-configuring systems," in Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, 1996, pp. 971-978.
2. D. P. Tallo, J. Durkin, and E. J. Petrik, "Intelligent fault isolation and diagnosis for communication satellite systems," Telematics and Informatics, vol. 9, no. 3-4, pp. 173-190, 1992.
3. D. DeCoste and M. B. Levine, "Automated event detection in space instruments: A case study using ipex-2 data and support vector machines," in SPIE Conference Astronamical Telescopes and Instrumentation, 2000.
4. R. Fujimaki, T. Yairi, and K. Machida, "An approach to spacecraft anomaly detection problem using kernel feature space," in Proceedings of The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-05), 2005, pp. 401-410.
5. M. Schwabacher, N. Oza, and B. Matthews, "Unsupervised anomaly detection for liquid-fueled rocket propulsion health monitoring," Journal of Aerospace Computing, Information, and Communication, vol. 6:7, pp. 464-482, 2009.
6. B. Scholkopf, A. Smola, and K.-R. Muller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, vol. 10, pp. 1299-1319, 1998.
7. H. Hoffmann, "Kernel pca for novelty detection," Pattern Recognition, vol. 40, no. 3, pp. 863-874, 2007.
8. J. T. Kwok and I.W. Tsang, "The pre-image problem in kernel methods," in Proc. of International Conference on Machine Learning, 2003, pp. 408-415.
9. M. E. Tipping and C. M. Bishop, "Mixtures of probabilistic principal component analyzers," Neural Computation, vol. 11, no. 2, pp. 443-482, 1999.
10. M. E. Tipping and C. M. Bishop, "Probabilistic principal component analysis," Journal of the Royal Statistical Society, Series B, vol. 21, no. 3, pp. 611-622, 1999.
11. N. D. Lawrence, "Gaussian process latent variable models for visualisation of high dimensional data," in Advances in Neural Information Processing Systems, 2004, pp. 329-336.
12. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. MIT Press, 2006.
13. M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Computation, vol. 15, no. 6, pp. 1373-1396, 2003.
14. M. A. Carreira-Perpinan and Z. Lu, "The laplacian eigenmaps latent variable model," in Proc. of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.