Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
Open accessAbstract
As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.
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For large data sets, the training kernel matrix of SVM grows quadratically with data set size, making SVM training very slow.
“However, for large data sets the training kernel matrix grows in quadratic form with the size of the data set, thus making the training of SVM on large data sets a very slow process [135].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [135]
For Case 2, SVM performed better than ANN because of SVM's generalization capability, optimal solution and discriminative power, especially when the number of input data is small.
“However, for Case 2 SVM performed better that ANN because of SVM's extraordinary generalization capability, along with its optimal solution and discriminative power; especially when the number of input data is small [135].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [135]
SVM has significant weaknesses including algorithmic complexity affecting training time on large data sets, development of optimal classifiers for multi-class problems, and performance on unbalanced data sets.
“Generally, SVM has some significant weaknesses, among which are: algorithmic complexity that affects the training time of the classifier in large data sets, development of optimal classifiers for multi-class problems and the performance of SVMs in unbalanced data sets [135].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [135]
TVGeAN was evaluated against four widely cited MTS benchmarks: C-MPASS, SMD, MSL, and SMAP for supervised and unsupervised learning tasks.
“Present a comprehensive evaluation of the TVGeAN model using four widely cited MTS benchmarks for supervised and unsupervised learning tasks: C-MPASS [35], SMD [36], MSL, and SMAP [37].”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [37]
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- Authors
- Hundman, K., Constantinou, V., Laporte, C., Colwell, I., & Soderstrom, T.
- Affiliations
- California Institute of Technology (US)
- Venue
- Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2018-07-19
- Pages
- 387-395
- OpenAlex
- W2786827964
- Cited by
- 1,771
- References
- 2
- Citations by year
- 2026: 315 · 2025: 388 · 2024: 343 · 2023: 297 · 2022: 192 · 2021: 135 · 2020: 63 · 2019: 37 · 2018: 1
- FWCI
- 24.08
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Keywords
- telemetry data, long short-term memory networks, recurrent neural networks, unsupervised anomaly detection, spacecraft monitoring
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