Casestudie Breakdown prediction Contell PILOT - Transumo

6.2 Case Study
The UMC St. Radboud provided 36 datasets. But none of them contains a real
technical malfunction. Moreover, only ten of these datasets contain data of a
connected door opening sensor. To be able to apply the suggested classification
method, door sensor data is needed to determine, whether a temperature exceeding
was caused by a door opening or not. Hence, only one out of these ten datasets can
be chosen as sample dataset.
Figure 6-2 pictures the actually selected dataset. It was selected because it contains
several interesting factors. First of all, the set maximum temperature level was
changed in March 2006, to reduce the quantity of false alarms (indicated by the red
dashed line) [Nijmengen06]. Moreover, this temperature pattern contains eyecatching
behavior. Beside some very high peaks, especially the global minimum,
occurred September 22 nd , is eye-catching, because this behavior is unique within the
whole time span. In addition to that, a change of cooling behavior of about half a
degree in the mean took place on the long run.
Figure 6-2: Temperature Overview of the Selected Sample Dataset
Aside from these interesting factors, all door openings took place between 6 o’ clock
in the morning and 10 o’ clock in the evening. This will be declared as daytime within
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