ent runs. The assumption is that if a new behavior is
really an outlier it should be an outlier independently of the number of closest points (k) used to compute the local density. This procedure minimizes the
chances of getting false alarms. It has limitations,
however, because this approach does not consider
novelties in the combination of two or more parameters; it works on a parameter-by-parameter basis
only. It is, however, systematic in the sense that it
can be applied to every parameter.
If flight control engineers would be able to look every
day at every parameter they would be able to identi-
fy all novelties. Unfortunately, they cannot. There
are way too many parameters (in the order of several
thousands) and the trend is that this number will
increase in future missions. The objective of this pro-
totype is to automate the process of noticing novel
behavior at the parameter behavior level.
New behaviors are often signatures of anomalies
either happening now or in the way to develop.
Noticing them early is of utmost importance for
planning corrective measurements and keeping the
spacecraft healthy. We should take into account that
not every new behavior corresponds to an anomaly:
it could be related to a new environmental condition
(for example, extremely high radiation) or be totally
expected as the result of planned new operations (for
example, Venus orbit insertion).
The functionality of being able to automatically
detect anomalies has been the driver for this project.
However, we understood that we could not build
such system. The closest we can get is identifying a
new behavior as novel when compared to a set of
known behaviors. Hence the name novelty detection.
In order to get closer to our goal of being able to
automatically detect anomalies, we choose the
known behavior set so that it only contains nominal
behaviors. With this configuration, when a new
behavior is classified as novel it can only mean two
things: it is either an anomaly or a new nominal
behavior. As time passes, the set of known nominal
behaviors will grow. This has a positive impact in
reducing the number of novelty alerts, as many
behaviors will be classified as nominal.
The novelty detection prototype makes use of mission utilities and support tools (MUST) (
Martínez-Heras, Baumgartner, and Donati 2005; Baumgartner
et al. 2005) as housekeeping telemetry and ancillary
data provider. The MUST’s performance allows the
performance of advanced monitoring with novelty
We will now discuss the two major functionalities of
the novelty detection prototype. The underlying
principle is the same, but one can achieve one func-
tionality or the other depending on which configu-
ration is used.
Identification of Potential Anomalies
The main purpose of the novelty detection prototype
is to detect potential anomalies. For fulfilling this
objective we will use as a known periods set the collection of all known nominal behaviors. This way,
when a new behavior is classified as novel with a high
probability, it is very likely that it would be an anomaly. It could be still a new kind of nominal behavior
but, as time passes, this should happen less and less
Verification of Expected New Behavior
In addition to identification of potential anomalies,
the same novelty detection technique can be used to
verify the occurrence of an expected new behavior.
For instance, let’s say that certain behavior is expected as a consequence of a maneuver and we would like
to verify it. A way of doing it with the novelty detection prototype is by using the recent past as the
known behavior set. The output of the novelty detection will be the novel behaviors compared with the
recent past. These novelties should contain the
expected new behaviors and possibly other parameters. These parameters that were not initially foreseen
can be considered side effects of the expected behaviors.
Two inputs are required to run the novelty detection
prototype: periodicity and the set of known behaviors.
Periodicity is the statistical features needed to characterize a fixed-length time period and has to be computed over a large enough time period. A typical
example is to use the periodicity of the orbit or the
amount of time that the short-term planning covers.
Set of known behaviors: the novelty detection will
detect whether a new behavior is novel as compared
with the set on known behaviors specified as input.
Two options are recommended: ( 1) use all nominal
behaviors: this is ideal to perform anomaly detection;
and ( 2) use the recent past (this should be used to verify expected new behavior).
The output consists of a text file that contains the list
of parameters that are believed to be novel. They are
grouped by period and by novelty probability within
periods. Figure 4 shows an example of such an output
file. The same information is stored in a database for
further analysis by client applications.
and Current Usage
To prove the feasibility of the monitoring paradigm
with novelty detection we applied it to an already
documented anomaly that the ESA satellite XMM-