Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

TL;DR

This paper presents a novel anomaly detection system for spacecraft telemetry data using LSTM neural networks combined with a nonparametric thresholding method, improving detection accuracy and reducing false positives.

Contribution

It introduces a new LSTM-based anomaly detection approach with a nonparametric thresholding technique and false positive mitigation strategies for spacecraft telemetry.

Findings

Effective detection of anomalies in satellite telemetry data

Reduced false positives through new thresholding and mitigation methods

Demonstrated on SMAP and MSL datasets

Abstract

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.