where having it too small would lead to many false
alarms and having the threshold too big will lead to
missing new behaviors.
The proposed novelty detection monitoring
approach has been successfully validated with XMM
anomalies, finding them before they were triggered
by the out-of-limits alarms, sometimes as early as two
months in advance. Currently, the XMM mission
uses the novelty detection prototype to detect new
behaviors on about 2000 parameters on a daily basis.
We would like to highlight that monitoring with
novelty detection is not mission specific but generic.
We can easily adapt it to any ESOC controlled mission since we use MUST (Martínez-Heras et al. 2005,
Baumgartner et al. 2005) as the data provider.
We believe that every mission will benefit from the
adoption of the novelty detection monitoring paradigm as complement to the classic out-of-limits
mechanism. Being able to know which few parameters (out of several thousands) exhibit a new behavior helps flight control engineers to efficiently direct
their monitoring efforts. The ESA’s patents group has
decided to protect the proposed monitoring paradigm by filing an international patent (WO2013
Baumgartner, A.; Martínez-Heras, J.; Donati, A.; Quintana,
M. 2005. MUST — A Platform for Introducing Innovative
Technologies in Operations. Paper presented at the 2005
International Symposium on Artificial Intelligence, Robot-
ics and Automation for Space, Munich, Germany, 5–9 Sep-
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. 2000. LOF:
Identifying Density-Based Local Outliers. In Proceedings of
the 2000 ACM SIGMOD International Conference on Management of Data (ACM SIGMOD Record), 93–104. New York:
Association for Computing Machinery. dx.doi.org/10.1145/
Iverson, D. L.; Martin, R.; Schwabacher, M.; Spirkovska, L.;
Taylor, W.; Mackey, R.; Castle, J. P.; Baskaran, V. 2012. General Purpose Data-Driven Monitoring for Space Operations.
Journal of Aerospace Computing, Information, and Communication 9( 2): 26–44. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 226–233. Piscataway, NJ: Institute of Electrical and Electronics Engineers.
Keogh, E.; Lin, J.; and Fu, A. 2005. HOT SAX: Efficiently
Finding the Most Unusual Time Series Subsequence. In
Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 226–233. Piscataway, NJ: Institute of Electrical and Electronics Engineers.
Kirsch, M.; Martin, J.; Pantaleoni, M.; Southworth, R.;
Schmidt, F.; Webert, D.; Weissmann, U. 2012. Cage Instability of XMM-Newton’s Reaction Wheels Discovered During the Development of an Early Degradation Warning System. Paper presented at the 12th International Conference
on Space Operations (SpaceOps 2012), Stockholm, Sweden,
Kriegel, H. P.; Kröger, P.; Schubert, E.; and Zimek, A. 2009.
LoOP: Local Outlier Probabilities. In Proceedings of the 18th
ACM Conference on Information and Knowledge Management,
1649–1652. New York: Association for Computing Machinery. dx.doi.org/10.1145/1645953.1646195
Martínez-Heras, J.; Baumgartner, A.; Donati, A. 2005. MUST:
Mission Utility and Support Tools. Paper presented at the
Data Systems in Aerospace Conference (DASIA 2005), Edinburgh, Scotland, 30 May– 2 June.
Martínez-Heras, J. A.; Donati, A.; Sousa, B.; Fischer, J. 2012.
DrMUST — A Data Mining Approach for Anomaly Investigation. Paper presented at the 12th International Conference on Space Operations (SpaceOps 2012), Stockholm,
Sweden, 11–15 June.
Martínez-Heras, J. A.; Yeung, K.; Donati, A.; Sousa, B.; Keil,
N. 2009; DrMUST: Automating the Anomaly Investigation
First-Cut. Paper presented at the IJCAI-09 Workshop on Artificial Intelligence in Space. Pasadena, California, USA, 17–18
Oliveira, H.; Lais, A.; Francisco, T.; Donati, A. 2012. Enabling
Visualization of Large Telemetry Datasets. Paper presented
at the 12th International Conference on Space Operations
(SpaceOps 2012), Stockholm, Sweden, 11–15 June.
Pantaleoni, M.; Kirsch, M.; Martin, J.; Weissmann, U.;
Krusenstiern, N. 2010. The New Operational Strategy for
XMM-Newton’s Tank Thermal Control, After Low Temperature Protection Thermostat Failure. Paper presented at the
10th International Conference on Space Operations
(SpaceOps 2010), Huntsville, AL, 25–30 Apr.
Patterson-Hine, A.; Hindson, W.; Sanderfer, D.; Deb, S.;
Domagala, C. 2001. A Model-Based Health Monitoring and
Diagnostic System for the UH- 60 Helicopter. In Proceedings
of the American Helicopter Forum, Volume 57, No. 2, 1552–
1564. Alexandria, VA: American Helicopter Society, Inc.
José Martínez-Heras is a software engineer/researcher at the
Advanced Operations Concepts Office at the European
Space Agency. His main interest consists of enhancing monitoring and diagnostics in space operations. His research
includes the enhancement of remote observability, availability of space data, reduction of information overload, and
data mining and machine learning applied to space opeara-tions. In recent years he was the main coauthor of four
patents related to data compression, diagnostics, and monitoring. Martínez-Heras holds a master of computer science
engineering degree from the Univerity of Málaga, Spain.
Alessandro Donati has 25 years of experience in satellite
operations at the European Space Operations Centre of ESA
in Darmstadt, Germany. In 2001 he founded and has since
led a skunk-work style managed team devoted to introducing and exploiting advanced enabling technology in support of innovative mission operations concepts. The operational tasks covered by the applied research include
planning, scheduling, diagnostic, resources management,
behavior modeling, and forecasting. The technologies of
interest include artificial intelligence, data mining, and soft
computing, among others. In recent years he was coauthor
of three patents in the area of data compression and diagnosis. Donati holds a master of electronic engineering
degree from the University of Rome La Sapienza.