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Data Mining Applications for Space Mission Operations System Health Monitoring
Open accessAbstract
Recent developments in data mining techniques for anomaly detection make it possible to use the wealth of available archived spacecraft system data to produce advanced system health monitoring applications.These "data driven" applications are capable of characterizing and monitoring interactions between multiple parameters and can complement existing practice to provide valuable decision support for mission controllers.Data driven software tools, including Orca and the Inductive Monitoring System (IMS), have been successfully applied to mission operations for both the Space Shuttle and the International Space Station.Orca uses a nearest-neighbor approach to search for unusual data points in multivariate data sets by calculating the distance of each data point from neighboring points.The IMS tool uses a data mining technique called clustering to analyze archived spacecraft data and characterize nominal interactions between selected parameters.This characterization, or model, is compared with real time or archived system data to detect off nominal behavior.Augmenting traditional mission control software with advanced monitoring tools, such as Orca and IMS, can provide controllers with greater insight into the health and performance of the space systems under their watch.We will describe how such techniques have been applied to NASA mission control operations and discuss plans for future mission control system health monitoring software.
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“Using expert-labeled telemetry anomaly data from the Mars Science Laboratory (MSL) Curiosity rover and the Soil Moisture Active Passive (SMAP) satellite, a team of researchers at NASA demonstrated the effectiveness of Long Short-Term Memory (LSTMs) networks (a type of Recurrent Neural Network (RNN)) in overcoming issues associated with traditional AD methods for spacecraft telemetry [134].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [134]
In the citing paper
“This implies that a lack of labeled anomalies necessitates the use of unsupervised or semi-supervised approaches [134].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [134]
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Research works printed but not linked: 4
- 1Bay, S. D., andSchwabacher, M. "Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule," Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery andDataMining,2003. · DOI
- 2Iverson, D. L. "Inductive System Health Monitoring," Proceedings of The 2004 International Conference on ArtificialIntelligence(IC-AI04),CSREA,LasVegas,Nevada,June 2004.
- International Space Station Thermal, 2004, NASA Document ISS TCS TM
- 6Martin, R. A., Schwabacher, M., Oza, N., and Srivastava, A. "Comparison of Unsupervised Anomaly Detection Methods for Systems Health Management Using Space Shuttle Main Engine Data," Proceedings of the JANNAF PropulsionMeeting,JANNAF,Denver, CO,2007.
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Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68
“The IMS tool2 uses clustering to analyze archived spacecraft data and characterizes nominal interactions between selected parameters [122, 131, 133].”
BackgroundMethod
Metadata
- Authors
- Iverson, D.
- Affiliations
- Ames Research Center (US)
- Corresponding author
- Iverson, David
- Venue
- SpaceOps 2008 Conference
- Published
- 2008-05-12
- OpenAlex
- W2064158558
- Cited by
- 40
- References
- 0
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 2 · 2022: 4 · 2021: 2 · 2020: 3 · 2019: 3 · 2018: 1 · 2017: 2 · 2016: 5 · 2015: 3 · 2014: 5 · 2013: 1 · 2012: 4
- FWCI
- 1.70
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- SpaceOps Conference (SpaceOps) (publishedVersion)
- Funders
- National Aeronautics and Space Administration
- Eli Lilly and Company
- Ames Research Center
- Johnson Space Center
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Engineering and Test Systems
- Keywords
- spacecraft health monitoring, anomaly detection, data mining, mission operations, ORCA, nearest neighbor analysis, clustering, multivariate data, Space Shuttle, International Space Station, decision support
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