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
the spacecraft, or they may represent true anomalous or unexpected
behaviors. To differentiate between specialized modes
and anomalies, we employ a supervised method of consulting
human mission experts in the approach presented in this
paper. Our longer term goal is to develop more automated
methods for detecting anomalies in time series data, and once
anomalies are identified, use feature selection methods to
build online detectors that can be used in future missions, thus
contributing to making operations more effective and improving
overall safety of the mission.