Space Telemetry Anomaly Detection Based on Statistical PCA Algorithm
The critical concern of satellite operations is to ensure
the health and safety of satellites. The worst case in this perspective
is probably the loss of a mission, but the more common interruption
of satellite functionality can result in compromised mission
objectives. All the data acquiring from the spacecraft are known as
Telemetry (TM), which contains the wealth information related to the
health of all its subsystems. Each single item of information is
contained in a telemetry parameter, which represents a time-variant
property (i.e. a status or a measurement) to be checked. As a
consequence, there is a continuous improvement of TM monitoring
systems to reduce the time required to respond to changes in a
satellite's state of health. A fast conception of the current state of the
satellite is thus very important to respond to occurring failures.
Statistical multivariate latent techniques are one of the vital learning
tools that are used to tackle the problem above coherently.
Information extraction from such rich data sources using advanced
statistical methodologies is a challenging task due to the massive
volume of data. To solve this problem, in this paper, we present a
proposed unsupervised learning algorithm based on Principle
Component Analysis (PCA) technique. The algorithm is particularly
applied on an actual remote sensing spacecraft. Data from the
Attitude Determination and Control System (ADCS) was acquired
under two operation conditions: normal and faulty states. The models
were built and tested under these conditions, and the results show that
the algorithm could successfully differentiate between these
operations conditions. Furthermore, the algorithm provides
competent information in prediction as well as adding more insight
and physical interpretation to the ADCS operation.
[1] D.L. Iverson, R. Martin, M. Schwabacher, L. Spirkovska, W. Taylor,
R. Mackey, and J.P. Castle, “General Purpose Data-Driven System
Monitoring for Space Operations,” in Proc. of AIAA Infotech @
Aerospace Conference, Seattle, WA, October 2010.
[2] D.L. Iverson, “Data Mining Applications for Space Mission Operations
System Health Monitoring,” NASA Ames Research Center, Moffett
Field, California, 94035 Space Operations Conference, 2008.
[3] T. Yairi, M. Inui, A. Yoshiki, Y. Kawahara, and N. Takata, “Spacecraft
Telemetry Data Monitoring by Dimensionality Reduction Techniques,”
in Proc. SICE Annual Conference, Japan, 2010.
[4] I. Verzola, A.E. Lagny, and J. Biswas, “A Predictive Approach to
Failure Estimation and Identification for Space Systems Operations,” in
Proc. 13th international conference on space operations, Pasadena,
California, USA, May 2014.
[5] J. MacGregora, A. Cinarc, “Monitoring, fault diagnosis, fault-tolerant
control, and optimization: Data-driven methods,” Journal of Computers
and Chemical Engineering, vol. 47, June 2012.
[6] J. Peng, L. Fan, W. Xiao, and J. Tang, “Anomaly Monitoring Method
for Key Components of Satellite,” Scientific World Journal, vol. 2014,
Article ID 104052, January 2014.
[7] S. Lindsay, and D. Woodbridge, “Spacecraft State- of-health (SOH)
Analysis via Data Mining,” in Proc. 13th international conference on
space operations, Pasadena, California, USA, May 2014.
[8] S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis”,
Chemometrics and intelligent laboratory systems, vol. 2: 37-52, 1987.
[9] J.E. Jackson, A User’s Guide to Principal Components. Wiley, New
York, 1991.
[10] S. Wold, M. Sjostrom, and L. Eriksson, “PLS-regression: a basic tool of
Chemometrics”, Chemometrics and intelligent laboratory systems, vol.
58:109-130, 2001.
[11] L. Simar, and W. Hardle, Applied Multivariate Statistical Analysis. Tech
method and data technologies. Springer-Verlag, Berlin and Louvain-la-
Neuve, 2003.
[12] P.R. Goulding, B. Lennox, D.J. Sandoz, K.J. Smith, and Marjanovic,
“Fault detection in continuous processes using multivariate statistical
methods,” International journal of systems science, vol. 31(11), pp.
1459-1471, 2000.
[13] T.K. Ralston, G. DePuy, and J.H. Graham, “Computer-based monitoring
and fault diagnosis a chemical process case study,” Instrument Society of
America Transactions ISA, vol. 40, pp. 85-98. E, USA, 2003.
[14] Y. Zhan, and V.Makis, “A robust diagnostic model for gearboxes
subject to vibration monitoring,” Journal of Sound and Vibration, vol.
290, pp. 928–955, 2006.
[1] D.L. Iverson, R. Martin, M. Schwabacher, L. Spirkovska, W. Taylor,
R. Mackey, and J.P. Castle, “General Purpose Data-Driven System
Monitoring for Space Operations,” in Proc. of AIAA Infotech @
Aerospace Conference, Seattle, WA, October 2010.
[2] D.L. Iverson, “Data Mining Applications for Space Mission Operations
System Health Monitoring,” NASA Ames Research Center, Moffett
Field, California, 94035 Space Operations Conference, 2008.
[3] T. Yairi, M. Inui, A. Yoshiki, Y. Kawahara, and N. Takata, “Spacecraft
Telemetry Data Monitoring by Dimensionality Reduction Techniques,”
in Proc. SICE Annual Conference, Japan, 2010.
[4] I. Verzola, A.E. Lagny, and J. Biswas, “A Predictive Approach to
Failure Estimation and Identification for Space Systems Operations,” in
Proc. 13th international conference on space operations, Pasadena,
California, USA, May 2014.
[5] J. MacGregora, A. Cinarc, “Monitoring, fault diagnosis, fault-tolerant
control, and optimization: Data-driven methods,” Journal of Computers
and Chemical Engineering, vol. 47, June 2012.
[6] J. Peng, L. Fan, W. Xiao, and J. Tang, “Anomaly Monitoring Method
for Key Components of Satellite,” Scientific World Journal, vol. 2014,
Article ID 104052, January 2014.
[7] S. Lindsay, and D. Woodbridge, “Spacecraft State- of-health (SOH)
Analysis via Data Mining,” in Proc. 13th international conference on
space operations, Pasadena, California, USA, May 2014.
[8] S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis”,
Chemometrics and intelligent laboratory systems, vol. 2: 37-52, 1987.
[9] J.E. Jackson, A User’s Guide to Principal Components. Wiley, New
York, 1991.
[10] S. Wold, M. Sjostrom, and L. Eriksson, “PLS-regression: a basic tool of
Chemometrics”, Chemometrics and intelligent laboratory systems, vol.
58:109-130, 2001.
[11] L. Simar, and W. Hardle, Applied Multivariate Statistical Analysis. Tech
method and data technologies. Springer-Verlag, Berlin and Louvain-la-
Neuve, 2003.
[12] P.R. Goulding, B. Lennox, D.J. Sandoz, K.J. Smith, and Marjanovic,
“Fault detection in continuous processes using multivariate statistical
methods,” International journal of systems science, vol. 31(11), pp.
1459-1471, 2000.
[13] T.K. Ralston, G. DePuy, and J.H. Graham, “Computer-based monitoring
and fault diagnosis a chemical process case study,” Instrument Society of
America Transactions ISA, vol. 40, pp. 85-98. E, USA, 2003.
[14] Y. Zhan, and V.Makis, “A robust diagnostic model for gearboxes
subject to vibration monitoring,” Journal of Sound and Vibration, vol.
290, pp. 928–955, 2006.
@article{"International Journal of Electrical, Electronic and Communication Sciences:71258", author = "B. Nassar and W. Hussein and M. Mokhtar", title = "Space Telemetry Anomaly Detection Based on Statistical PCA Algorithm", abstract = "The critical concern of satellite operations is to ensure
the health and safety of satellites. The worst case in this perspective
is probably the loss of a mission, but the more common interruption
of satellite functionality can result in compromised mission
objectives. All the data acquiring from the spacecraft are known as
Telemetry (TM), which contains the wealth information related to the
health of all its subsystems. Each single item of information is
contained in a telemetry parameter, which represents a time-variant
property (i.e. a status or a measurement) to be checked. As a
consequence, there is a continuous improvement of TM monitoring
systems to reduce the time required to respond to changes in a
satellite's state of health. A fast conception of the current state of the
satellite is thus very important to respond to occurring failures.
Statistical multivariate latent techniques are one of the vital learning
tools that are used to tackle the problem above coherently.
Information extraction from such rich data sources using advanced
statistical methodologies is a challenging task due to the massive
volume of data. To solve this problem, in this paper, we present a
proposed unsupervised learning algorithm based on Principle
Component Analysis (PCA) technique. The algorithm is particularly
applied on an actual remote sensing spacecraft. Data from the
Attitude Determination and Control System (ADCS) was acquired
under two operation conditions: normal and faulty states. The models
were built and tested under these conditions, and the results show that
the algorithm could successfully differentiate between these
operations conditions. Furthermore, the algorithm provides
competent information in prediction as well as adding more insight
and physical interpretation to the ADCS operation.", keywords = "Space telemetry monitoring, multivariate analysis,
PCA algorithm, space operations.", volume = "9", number = "6", pages = "637-9", }