Aerospace and Electronic Systems Magazine April 2017 - 27

1. To the best of the authors' knowledge, it is the first time to
use the supervised statistical learning PLS-DA technique/algorithm to model, classify, and analyze the spacecraft telemetry and also, the PLS-DA models learned by the algorithm
are much easier for experts to interpret than the models produced by some competing learning algorithms such as neural
networks or SVMs.
2. The algorithm develops the model of the telemetry data in the
X-space and/or Y-space where the advantage of the contribution and control plots can be seen. These plots facilitate the detection of the variables responsible for any process and monitor
key process variables and quality over time.
3. It proposes the use of the nonlinear SVMs classification technique/algorithm for space operations anomaly detection for the
first time.

RELATED WORK
Over decades researchers have devoted considerable effort to
develop various heath monitoring systems (HMSs) for space operations. The methods used include machine learning/data mining
techniques and multivariate statistical approaches. These methods
can provide important tools for the field of intelligent monitoring
which can learn, adapt, and support decision making concerning
the system that flight experts are in charge of [3]. Much of the
previous work in fault detection for space operations has used unsupervised anomaly detection algorithms because they relied on
historical data, and the historical data generally doesn't contain
enough examples of faults to adequately train a supervised learning
algorithm. One of the advantages of our design work described in
this article is the database. It has training data with faulty cases that
facilitate us to adequately train a supervised learning algorithm.
Martin et al. [4] presented a comparison of six unsupervised
anomaly detection algorithms. They ran the algorithms using space
shuttle main engine (SSME) data from four space shuttle flights
and two test stand firings for training and used data from eight
shuttle flights and four test stand firings for validation. Although
they acknowledge that they don't have enough data to make statisAPRIL 2017

tically important comparisons of the relative performance of the
algorithms, they conclude that the algorithm with the best accuracy
appeared to be either Orca or one-class support vector machines
(OCSVM), but some reclassification is necessary in order to best
represent the new anomalies identified during analysis.
Iverson [5], [6] described an unsupervised anomaly detection
algorithm known as the inductive monitoring system (IMS) and its
use in a space shuttle application. After the STS-107 space shuttle
Columbia disaster, Iverson applied IMS to data from four temperature sensors inside the shuttle's wings. He trained it using data from
five previous space shuttle flights, and then tested it using STS-107
data. It detected an anomaly in data from the temperature sensors
on the shuttle's left wing shortly after the foam impact, suggesting
in retrospect that with the aid of IMS, flight controllers might have
been able to detect the damage to the wing much sooner than they
did. More recently, IMS has been deployed to the mission control
center at NASA Johnson Space Center, where it is being used to
monitor live data from the international space station (ISS).
Schwabacher et al. [7] presented one system failure and four
other anomalies of data from the SSME. They explain that anomalies were detected by applying four unsupervised anomaly detection algorithms Orca, GritBot, IMS, and OCSVMs linear model
which allow SVMs to retain the benefit that the algorithm finds the
globally optimal solution given the training set, while "still strong
effectively using non-linear supervised models." The authors
clarified that since different algorithms use different definitions of
anomaly, some anomalies were detected by some algorithms while
other anomalies weren't. Orca, IMS, and GritBot are relatively
simple algorithms, easy to understand and provide some sort of explanation of each anomaly in terms of the variables; therefore they
are more likely to be accepted by experts, whereas the OCSVM
algorithm is more sophisticated and difficult to understand.
Yairi et al. [8] evaluated a variety of unsupervised and supervised dimensionality reduction algorithms and compared them
without using cross-validation. The authors' point of view clarified
that the use of cross-validation might be too time-consuming when
the training set or the number of classes is large.
Verzola et al. [9] clarified that space operations are often based
on a reactive model. The main drawback of this model is associated with complexity. It is difficult to prevent failures and to execute

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