Aerospace and Electronic Systems Magazine April 2017 - 26
DOI. No. 10.1109/MAES.2017.150049
Supervised Learning Algorithms for Spacecraft Attitude
Determination and Control System Health Monitoring
Bassem Nassar, Wessam Hussein, Egyptian Armed Forces, Cairo, Egypt
Mohamed Medhat, NARSS, Cairo, Egypt
The main concern of any space mission operation is to ensure the
health and safety of spacecrafts. The worst case in this circumstance is probably the loss of a mission but the more common interruption of spacecraft functionality can result in compromised
mission objectives. Spacecraft telemetry holds information related
to state-of-health (SOH) of its subsystems. Each parameter has
information that represents a time-variant property (i.e., a status or
a measurement) to be checked. Moreover, the tremendous increase
in telemetry data volume and its complexity directs the need for
more efficient and scalable data processing systems. As a consequence, there is a continuous improvement of telemetry monitoring applications in order to reduce the time required to respond to
changes in a spacecraft's SOH. So, a fast conception of the current
state of the spacecraft is thus very important in order to respond to
failures. Furthermore, the progressive growth in spacecrafts leads
to increase of the level of standardization in spacecraft operations.
Anomaly detection algorithms, also known as outlier detection
algorithms seek to find portions of a data set that are somehow different from the rest of the data set. Some good surveys of anomaly
detection algorithms can be found in , . A supervised anomaly
detection algorithm requires training data consisting of a set of examples of anomalies, and a set of examples of nominal data. From
the data, the algorithm learns a model that distinguishes between
the nominal and the anomalous data points. Supervised anomaly
detection algorithms typically require tens or hundreds of labeled
examples of anomalies, plus a similar number of labeled examples
of nominal data points, in order to obtain adequate performance.
Unsupervised anomaly detection algorithms are trained using a
Authors' current address: MTC, Mechatronics, Kobry Elkobbah, Cairo, Egypt 11646. E-mail: (email@example.com).
The primary version of this article was partly presented at the
2015 IEEE Aerospace Conference, March 7-14, 2015, Yellowstone, Big Sky, MT.
Manuscript received April 9, 2015, revised August 25, 2015,
February 17, 2016, May 27, 2016, and ready for publication
June 1, 2016.
Review handled by M. Jah.
0885/8985/17/$26.00 © 2017 IEEE
single set of unlabeled data, most or all of which is assumed to be
nominal. It learns a model of the nominal data, which can be used
to signal an anomaly when new data fails to match the model.
The data-driven approach seeks to build a model for detecting anomalies directly from the data, rather than building it based
on human expertise. Data-driven process monitoring or statistical
process monitoring (SPM) applies multivariate statistical methods
and machine learning techniques to fault detection and diagnosis
for space operations, which has become one of the richest areas for
research and practice over the last two decades. Based on methods
from multivariate statistical analysis, SPM has found wide application in various fields including space operations. So, due to the
data-based nature of the SPM methods, it is relatively easy to apply
to real processes of rather large scale compared with other methods
based on systems theory or rigorous process models.
In this article, we propose three supervised learning algorithms
to detect anomalies in space mission operations. The first algorithm is our supervised statistical algorithm based on the projection to latent structure discriminant analysis (PLS-DA) technique.
The second is multivariate statistical software called soft independent modeling for class analogy (SIMCA-P) developed by Umetrics. The third is applying the nonlinear support vector machines
(SVMs) algorithm to the spacecraft telemetry for the first time.
We describe a novel supervised PLS-DA statistical dimensionality reduction algorithm coded in Matlab. The algorithm presents
a practical approach for fault management that includes: (i) fault
detection; (ii) fault identification; (iii) diagnosis and quality monitoring. The task of the algorithm is to model, analyze, and classify
the telemetry as well as to identify key contributors to anomalous
events automatically which lead to further details about the attitude
determination and control subsystem (ADCS) behavior. However,
this approach doesn't build a causality direction between variables,
rather it builds a correlation trend inside a bounded region. The
algorithm represents an efficient tool for ADCS anomaly detection, which gives useful information for the flight controllers and
analysts to support decision making. Also, it is the starting point
to enhance our algorithm to be coupled with other unsupervised
and supervised learning techniques to solve the relevant problems,
which can't be done in commercial software like SIMCA-P. The
contributions of this design work can be enumerated as follows.
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