Detecting Spacecra Anomalies Using LSTMs and
Nonparametric Dynamic Thresholding
Kyle Hundman
NASA Jet Propulsion Laboratory
California Institute of Technology
kyle.a.hundman@jpl.nasa.gov
Valentino Constantinou
NASA Jet Propulsion Laboratory
California Institute of Technology
vconstan@jpl.nasa.gov
Christopher Laporte
NASA Jet Propulsion Laboratory
California Institute of Technology
christopher.d.laporte@jpl.nasa.gov
Ian Colwell
NASA Jet Propulsion Laboratory
California Institute of Technology
ian.colwell@jpl.nasa.gov
Tom Soderstrom
NASA Jet Propulsion Laboratory
California Institute of Technology
tom.soderstrom@jpl.nasa.gov
ABSTRACT
As spacecraft send back increasing amounts of telemetry data, im-
proved anomaly detection systems are needed to lessen the mon-
itoring burden placed on operations engineers and reduce opera-
tional risk. Current spacecraft monitoring systems only target a
subset of anomaly types and often require costly expert knowl-
edge to develop and maintain due to challenges involving scale and
complexity. We demonstrate the eectiveness 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) satel-
lite and the Mars Science Laboratory (MSL) rover, Curiosity. We
also propose a complementary unsupervised and nonparametric
anomaly thresholding approach developed during a pilot implemen-
tation of an anomaly detection system for SMAP, and oer false
positive mitigation strategies along with other key improvements
and lessons learned during development.
CCS CONCEPTS
• Computing methodologies → Anomaly detection
;
Neural
networks
; Semi-supervised learning settings;
• Applied comput-
ing → Forecasting;
KEYWORDS
Anomaly detection, Neural networks, RNNs, LSTMs, Aerospace,
Time-series, Forecasting
ACM Reference Format:
Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell,
and Tom Soderstrom. 2018. Detecting Spacecraft Anomalies Using LSTMs
and Nonparametric Dynamic Thresholding. In KDD ’18: The 24th ACM
SIGKDD International Conference on Knowledge Discovery & Data Mining,
August 19–23, 2018, London, United Kingdom. ACM, New York, NY, USA,
9 pages. https://doi.org/10.1145/3219819.3219845
ACM acknowledges that this contribution was authored or co-authored by an employee,
contractor, or aliate of the United States government. As such, the United States
government retains a nonexclusive, royalty-free right to publish or reproduce this
article, or to allow others to do so, for government purposes only.
KDD ’18, August 19–23, 2018, London, United Kingdom
© 2018 Association for Computing Machinery.
ACM ISBN 978-1-4503-5552-0/18/08... $15.00
https://doi.org/10.1145/3219819.3219845
1 INTRODUCTION
Spacecraft are exceptionally complex and expensive machines with
thousands of telemetry channels detailing aspects such as tem-
perature, radiation, power, instrumentation, and computational
activities. Monitoring these channels is an important and necessary
component of spacecraft operations given their complexity and
cost. In an environment where a failure to detect and respond to
potential hazards could result in the full or partial loss of spacecraft,
anomaly detection is a critical tool to alert operations engineers of
unexpected behavior.
Current anomaly detection methods for spacecraft telemetry
primarily consist of tiered alarms indicating when values stray out-
side of pre-dened limits and manual analysis of visualizations and
aggregate channel statistics. Expert systems and nearest neighbor-
based approaches have also been implemented for a small number
of spacecraft [
13
]. These approaches have well-documented limita-
tions – extensive expert knowledge and human capital are needed
to dene and update nominal ranges and perform ongoing analysis
of telemetry. Statistical and limit-based or density-based approaches
are also prone to missing anomalies that occur within dened limits
or those characterized by a temporal element [9].
These issues will be exacerbated as improved computing and
storage capabilities lead to increasing volumes of telemetry data.
NISAR, an upcoming Synthetic Aperture Radar (SAR) satellite, will
generate around 85 terabytes of data per day and represents ex-
ponentially increasing data rates for Earth Science satellites [
1
].
Mission complexity and condensed mission time frames also call
for improved anomaly detection solutions. For instance, the Europa
Lander concept would have an estimated 20-40 days on Europa’s
surface due to high radiation and would require intensive moni-
toring during surface operations [
20
]. Anomaly detection methods
that are more accurate and scalable will help allocate limited engi-
neering resources associated with such missions.
Challenges central to anomaly detection in multivariate time
series data also hold for spacecraft telemetry. A lack of labeled
anomalies necessitates the use of unsupervised or semi-supervised
approaches. Real-world systems are usually highly non-stationary
and dependent on current context. Data being monitored are often
heterogeneous, noisy, and high-dimensional. In scenarios where
anomaly detection is being used as a diagnostic tool, a degree of
interpretability is required. Identifying the existence of a potential
issue on board a spacecraft without providing any insight into its
arXiv:1802.04431v3 [cs.LG] 6 Jun 2018
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