Casestudie Breakdown prediction Contell PILOT - Transumo

The visual analysis of these graphs discovered that the selected dataset contains
several changes in behavior. Most significant are the global minimum on September,
22 nd , and the change in temperature level on the end of November. As well eyecatching
but not that significant are the mentioned changes right in the middle of
September and the general rise in temperature in the year 2006. Looking at the
graphs indicated, furthermore, that the notification of changes based on daytime data
is not promising, due to the high number of external influences.
After this graphical overview the numerical analysis will be evaluated by testing two
different deltas. Meaningful results can be obtained by choosing five or ten as delta.
The lower delta leads to earlier notifications, the higher delta only notifies higher
deviations. Table 6-2 pictures the mean deviations in nighttime data as well as the
minimum deviations that lead to a notification using the corresponding delta.
Table 6-2: The Chosen Deltas
Mean Deviation 5 x Mean Deviation 10 x Mean Deviation
Maximum 0,027 0,135 0,27
Minimum 0,043 0,215 0,43
Mean 0,035 0,175 0,35
Standard Deviation 0,011 0,055 0,11
Table 6-3 pictures the calculated results. A yellow marked cell indicates a notification,
due to a delta of five. If a delta of ten was predefined, only the red marked values
would be notified.
Using a delta of ten would have notified the most eye-catching changes in
September and November. A delta of five would lead to 45 notifications. In fact, most
of them are caused by the standard deviation and are not bound to significant
changes in general behavior. Hence, the standard deviation should only be used with
a high delta or left out. The residual measures can also be used with a delta of five.
The made notifications in July, January and February can actually be traced back on
small changes in cooling behavior, so that such a notification is right.
Hence, comparing nighttime measures from succeeding time intervals enables the
recognition of changes on the short-run (on daily basis). The notification level can be
adapted by choosing a higher or a smaller delta. Presumably, different deltas for
different kinds of machines have to be chosen to find the right balance between too
many and too few notifications.
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