Casestudie Breakdown prediction Contell PILOT - Transumo

Interviews with several employees from the UMC St. Radboud discovered that
currently no decision support does exist that introduces recommendations, telling
what should be done in case of such an alarm. Not even information of time and
duration of the last door opening is displayed to offer at least a hint on possible user
influence. The only thing an employee can do in case of an alarm is to inspect the
corresponding cooling device manually by having a short look at it.
In fact, the very high quantity of false alarms led to a loss in credibility of XiltriX, so
that employees tend to wait a certain time, after an alarm went off. Only in case of an
enduring alarm for a longer time period or the occurrence of an uncommon high
number of alarms during a short time interval, a manual inspection is really made in
most cases. [Nijmegen06]
As long as the stored contents are not damageable within very few minutes, this
practice is doable. But the estimation, whether the developing of a current alarm is
like most others or not, relies on experience and instinct of the operational staff. The
suggested data mining method to classify the developing of an alarm into different
alarming levels offers a higher reliability, because a decision, whether an alarm has
to be classified as really critical, is based on all available information like door
openings or past time behavior and not on unreliable user made estimations.
Hence, in my point of view this classification method should be added to XiltriX to
offer additional decision support and to reduce the number of demanded inspections.
Highly critical devices may either be excluded from this classification or assigned with
other classification parameters and additional conditions.
Contell/IKS confirms the possible improvements but fears that this classification
could lead to even higher user misbehavior, because classifications that are lower
than the highest level might be ignored. Consequently, the current user behavior to
wait for a certain time interval might be applied to the highest classification, so that a
user reaction is delayed to an unacceptable level.
The other major problem of sensor based temperature monitoring was the limited
ability to recognize changes on the short- and long-run. Up to now, only changes are
recognized that are bound to periodically occurring alarms. The suggested methods
to use statistical analysis and regression to determine changes inside normal
temperature range achieved a major improvement of this situation. Only the
determination of an appropriate delta for different kinds of cooling devices still needs
to be done in practice.
103