Active data selection for sensor networks with faults and changepoints

Proceedings - International Conference on Advanced Information Networking and Applications, AINA

Volumn , Issue , 2010, Pages 533-540

  Osborne, Michael A ^a   Garnett, Roman ^a   Roberts, Stephen J ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Active data selection; Bayesian methods; Changepoint detection; Fault detection; Gaussian processes; Sensor networks; Sensor selection; Time series prediction

Indexed keywords

ACTIVE DATA; BAYESIAN METHODS; CHANGE POINT DETECTION; GAUSSIAN PROCESSES; SENSOR SELECTION; TIME SERIES PREDICTION;

DATA REDUCTION; FAULT DETECTION; FORECASTING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); SENSOR NETWORKS;

BAYESIAN NETWORKS;

EID: 77954323256     PISSN: 1550445X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AINA.2010.36     Document Type: Conference Paper

References (17)

1. D. MacKay, "Information-based objective functions for active data selection," Neural Computation, vol. 4, no. 4, pp. 590-604, 1992.
2. S. Seo, M. Wallat, T. Graepel, and K. Obermayer, "Gaussian process regression: Active data selection and test pointrejection," in Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, vol. 3, 2000.
3. M. Basseville and I. Nikiforov, Detection of abrupt changes: Theory and application. Prentice Hall, 1993.
4. B. Brodsky and B. Darkhovsky, Nonparametric Methods in Change-Point Problems. Springer, 1993.
5. P. Green, "Reversible jump Markov chain Monte Carlo computation and Bayesian model determination," Biometrika, vol. 82, no. 4, p. 711, 1995.
6. M. Csorgo and L. Horvath, Limit theorems in change-point analysis. John Wiley & Sons, 1997.
7. J. Chen and A. Gupta, Parametric Statistical Change Point Analysis. Birkháuser Verlag, 2000.
8. R. P. Adams and D. J. MacKay, "Bayesian online changepoint detection," University of Cambridge, Cambridge, UK, Tech. Rep., 2007, arXiv:0710.3742v1 [stat.ML].
9. P. Fearnhead and Z. Liu, "On-line inference for multiple changepoint problems," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 69, no. 4, pp. 589-605, 2007.
10. T. Kobayashi and D. Simon, "Application of a bank of Kalman filters for aircraft engine fault diagnosis," in Proceedings of ASME Turbo Expo 2003, Power for Land, Sea, and Air, June 16-19 2003, Atlanta, Georgia, USA, 2003.
11. V. Aggarwal, K. Nagarajan, and K. Slatton, "Estimating failure modes using a multiple-model Kalman filter," ASPL, Tech. Rep. No. Rep-2004-03-001, 2004. [Online]. Available: http://www.aspl.ece.ufl.edu/reports/ Rep-2004-03-001.pdf.
12. S. Reece, C. Claxton, D. Nicholson, and S. J. Roberts, "Multi-Sensor Fault Recovery in the Presence of Known and Unknown Fault Types," in Proceedings of the 12th International Conference on Information Fusion (FUSION 2009), Seattle, USA, 2009.
13. R. Garnett, M. A. Osborne, and S. Roberts, "Sequential Bayesian prediction in the presence of changepoints," in Proceedings of the 26th Annual International Conference on Machine Learning. ACM New York, NY, USA, 2009.
14. M. A. Osborne, A. Rogers, S. Ramchurn, S. J. Roberts, and N. R. Jennings, "Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes," in International Conference on Information Processing in Sensor Networks 2008, April 2008, pp. 109-120. [Online]. Available: http://eprints.ecs.soton.ac.uk/ 15122/.
15. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. MIT Press, 2006.
16. R. Garnett, M. A. Osborne, S. Reece, A. Rogers, and S. Roberts, "Sequential Bayesian prediction in the presence of changepoints and faults," University of Oxford, Tech. Rep., 2009, available at http://www.robots.ox.ac.uk/~mosb/PARG0901.pdf.
17. C. E. Rasmussen and Z. Ghahramani, "Bayesian Monte Carlo," in Advances in Neural Information Processing Systems, S. Becker and K. Obermayer, Eds. Cambridge, MA: MIT Press, 2003, vol. 15.