port characterizations and anomaly investigation
using the DrMUST (Martínez-Heras et al. 2012,
Martínez-Heras et al. 2009) plug-in. A screen capture
of the novelty detection display for an expected new
behavior is shown in figure 6.
The problem of automatically finding unusual
behavior has been addressed by other researchers in
a number of fields, both space and nonspace.
For instance, A. Patterson-Hine and colleagues
(Patterson-Hine et al. 2001) uses a model-based
approach to detect anomalies in helicopters; however, model-based approaches require a big upfront
engineering effort. In this project we have focused on
approaches that require as little engineering effort as
E. Keogh and colleagues (Keogh, Lin, and Fu 2005)
describe the algorithm HOT SAX in order to automatically and efficiently find time series discords.
Time series discords are defined as subsequences of
longer time series that are maximally different to all
the rest of the time series subsequences. Its major
advantage is its simplicity as it just requires a single
input: the length of the subsequence (in our case it
would be the period length). While HOT SAX is successful at finding the ranking of discords for time
series, it is of little use to spacecraft engineers that
need to understand if these discords are relevant or
not, especially when they are monitoring thousands
of parameters of different nature. In addition, it is difficult to know how important a top-ranked discord of
a certain parameter is in relation to the other parameters’ top-ranked discords.
D. L. Iverson and colleagues (Iverson et al. 2012)
present the inductive monitoring system (IMS),
which uses clustering to characterize normal itera-
Figure 5. Nominal Behavior.
February – April, OOL on 13 July. This thermostat has been properly working showing the same behavior for 10 years. However, it started
to have a strange behavior since mid-May 2009 and it was only noticed two months after (July 2009) when it crossed the lower limit. For
this type of anomaly, the out-of-limits checks are not effective because, paradoxically, the behavior of the anomaly was “more in limits”
than before. The proposed novelty detection monitoring technique could find this anomaly two months before the out-of-limit alarm triggered.
T6073 THL Tank1 EXT Eng
19February200901March200911March2009 21Marchy200931March200910April200920April200930April200910May200920May200930May20099June200919June200929June2009 July200919July2009
Nominal cycle Thermostat dithering FAILURE