Journal
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING
Volume -, Issue -, Pages 2123-2133Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3292500.3330776
Keywords
Anomaly detection; LSTMs; Aerospace; Time-series
Funding
- Korea Aerospace Research Institute (KARI) - Ministry of Science and ICT (MSIT)
- MSIT, South Korea [IITP-2019-2011-1-00783]
- NRF of Korea, MSIT [NRF-2017R1C1B5076474]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2011-1-00783-007] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Detecting an anomaly is not only important for many terrestrial applications on Earth but also for space applications. Especially, satellite missions are highly risky because unexpected hardware and software failures can occur due to sudden or unforeseen space environment changes. Anomaly detection and spacecraft health monitoring systems have heavily relied on human expertise to investigate whether they are a true anomaly or not. Also, it is practically infeasible to produce labels on data due to the enormous amount of telemetries generated from a satellite. In this work, we propose a data-driven anomaly detection algorithm for Korea Multi-Purpose Satellite 2 (KOMPSAT-2). We develop a Multivariate Convolution LSTM with Mixtures of Probabilistic Principal Component Analyzers, where our approach uses both neural networks and probabilistic clustering to improve the anomaly detection performance. We evaluated our approach with a total of 22 million telemetry samples collected for 10 months from KOMPSAT-2. We also compare our approach with other state-of-the-art approaches. We show that our proposed approach is 35.8% better in precision, and 18.2% better in F-1 score than the best baseline approach. We plan to deploy our algorithm in the second half of 2019 to actually apply real operation of KOMPSAT-2.
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