An Application of Data Driven Anomaly Identification to Spacecraft Telemetry Data
In this paper, we propose a mixed method for analyzing telemetry data from a robotic space mission. The idea is to first apply unsupervised learning methods to the telemetry data divided into temporal segments. The large clusters that ensue typically represent the nominal operations of the spacecraft and are not of interest from an anomaly detection viewpoint. However, the smaller clusters and outliers that result from this analysis may represent specialized modes of operation, e.g., conduct of a specialized experiment on board the spacecraft, or they may represent true anomalous or unexpected behaviors. To differentiate between specialized modes and anomalies, we employ a supervised method of consulting
human mission experts in the approach presented in this paper. Our longer term goal is to develop more automated methods for detecting anomalies in time series data, and once anomalies are identified, use feature selection methods to build online detectors that can be used in future missions, thus contributing to making operations more effective and improving overall safety of the mission.
How to Cite
Blanke, M., & Schr¨oder, J. (2006). Diagnosis and faulttolerant control (Vol. 691). Springer.
Blom, H., & Bar-Shalom, Y. (1988). The interacting multiple model algorithm for systems with markovian switching coefficients. Automat. Contr., vol. 33, pp. 780-783, Aug. 1988.
Chen, J., & Patton, R. J. (2012). Robust model-based fault diagnosis for dynamic systems (Vol. 3). Springer Science & Business Media.
Hanlon, P., & Maybeck, P. (2000). Multiple-model adaptive estimation using a residual correlation kalman filter bank. IEEE Trans. Aerosp. Electron. Syst., vol. 36, pp. 393-406, Apr. 2000.
Henzinger, T. A. (Ed.). (2000). The theory of hybrid automata. Springer Berlin Heidelberg.
Hine, B., Spremo, S., Turner, M., & Caffrey, R. (2010). The lunar atmosphere and dust environment explorer (ladee) mission. In Ieee aerospace conference.
Isermann, R. (2005). Model-based fault-detection and diagnosis–status and applications. Annual Reviews in Control, 29(1), 71–85.
Ji, M., Zhang, Z., Biswas, G., & Sarkar, N. (2003). Hybrid fault adaptive control of a wheeled mobile robot. Mechatronics, IEEE/ASME Transactions on,
Lee, E. A. (2008). Cyber physical systems: Design challenges. In Object oriented real-time distributed computing (isorc), 2008 11th ieee international symposium on (pp. 363–369).
Mack, D. L., Biswas, G., Koutsoukos, X. D., & Mylaraswamy, D. (2016, in press). Learning bayesian network structures to augment aircraft diagnostic reference models. IEEE Transactions on Automation Science and Engineering.
Marwedel, P. (2010). Embedded system design: Embedded systems foundations of cyber-physical systems. Springer Science & Business Media.
Niggemann, O., Biswas, G., Kinnebrew, J. S., Khorasgani, H., Volgmann, S., & Bunte, A. (2015). Data-driven monitoring of cyber-physical systems leveraging on big data and the internet-of-things for diagnosis and control. 26th International Workshop on Principles of Diagnosis, Paris, France.
Noura, H., Theilliol, D., Ponsart, J.-C., & Chamseddine, A. (2009). Fault-tolerant control systems: Design and practical applications. Springer Science & Business Media.
Qin, S. J. (2012). Survey on data-driven industrial process monitoring and diagnosis. Annual Reviews in Control, 36(2), 220–234.
Yin, S., Ding, S. X., Xie, X., & Luo, H. (2014). A review on basic data-driven approaches for industrial process monitoring. Industrial Electronics, IEEE Transactions on, 61(11), 6418–6428.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.