An application of data driven anomaly identification to spacecraft telemetry data

In this paper, we propose a mixed method for analyzing
telemetry data from a robotic space mission. The idea is
to first apply unsupervised learning methods to the telemetry
data divided into temporal segments. The large clusters
that ensue typically represent the nominal operations of the
spacecraft and are not of interest from an anomaly detection
viewpoint. However, the smaller clusters and outliers that result
from this analysis may represent specialized modes of
operation, e.g., conduct of a specialized experiment on board

Year of Publication
Conference Name
Annual Conference of the Prognostics and Health Management Society 2016
Date Published
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