Casestudie Breakdown prediction Contell PILOT - Transumo

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current state of a monitoring device. As this classification also regards door openings, a limited possibility is achieved to recognize external influences. Hence, a combination of statistical analysis and data mining is able to significantly improve the current monitoring situation. The achieved improvements are summarized again in Table 6-6. Blue arrows represent achieved improvements by the use of statistical analysis and magenta arrows represent achieved improvements by the use of data mining. Blue dashed arrows represent estimated improvements by the use of statistical analysis that cannot be approved for sure, due to the named reasons. Table 6-6: Achieved Improvements Approach is able to classify the current state of a monitored device Approach is able to recognize significant changes of behavior on the short-run Approach is able to recognize significant changes of behavior on the long-run Approach is able to predict upcoming failures Approach is able to identify failures as soon as they are recognizable Approach is able to avoid an error of second kind in any case Approach is able to recognize external influences Approach is able to optimize the usage of cooling devices Approach offers a very quick overview of the cooling device’s accuracy 6.4 Recommendations This diploma thesis pointed out the general problems of currently applied sensor based temperature monitoring. Most problematic was the very low probability of a real technical malfunction, compared to irregular temperatures that were caused by door openings or other external influences. Hence, the idea to just evaluate the current temperature of a cooling device leads to a large number of false alarms (e.g. 158 for the selected sample dataset within one year) 102
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. <strong>Contell</strong>/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
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Technische Universität Braunschwei
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4.4 Review of Current State of Rese
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Index of Tables Table 2-1: Error of
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1 Introduction 1.1 Initial Position
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2 Sensor Based Temperature Monitori
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compressor. They are served by a ce
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the specified value, it works very
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of this, over a hundred irregular p
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a centralized one. An isolated appl
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Figure 2-8: Lack of Information Pro
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Moreover, the fridge’s filling le
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store this kind of data but only of
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3 Current Monitoring Systems The la
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device’s condition. Especially th
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a converter that is also available
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To indicate important events within
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1. Display stored data in table for
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This way of visualizing data is the
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free text. Moreover it is possible
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dependent limit and delay settings,
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Aside from these functions, the kin
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1. Temperature verification in retr
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alarms by the use of additionally a
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The third mentioned Centron Environ
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4 Current State of Research As alre
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even this improvement is still face
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Another current approach is regress
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S o n 2 = ∑ Yi −Yi n −1 i= 2
Page 57 and 58: This algorithm returns an upper and
Page 59 and 60: • Critical values • Pre-warning
Page 61 and 62: 5 Possible and Promising Ways of Da
Page 63 and 64: could be: “The mean increase of a
Page 65 and 66: The mode is the most frequent value
Page 67 and 68: Figure 5-1: Two Samples of Regressi
Page 69 and 70: 5.4.2 The Major Problems of Regress
Page 71 and 72: The next section will introduce tim
Page 73 and 74: explained in section 5.4. If such a
Page 75 and 76: pictured in Formula 5-12. Beside a
Page 77 and 78: P ( m) = P ( r ) ⋅ P ( m−r ) wi
Page 79 and 80: But the greatest problem is again t
Page 81 and 82: clustering as well as an analysis o
Page 83 and 84: The general idea of supervised lear
Page 85 and 86: sometimes data of a door opening se
Page 87 and 88: Beside the introduced notification
Page 89 and 90: A classification could be achieved
Page 91 and 92: Table 5-1: Estimated Improvements A
Page 93 and 94: As all these software products fail
Page 95 and 96: 6.2 Case Study The UMC St. Radboud
Page 97 and 98: Figure 6-3: Maximum Values at Dayti
Page 99 and 100: Figure 6-5: Minimum Values at Dayti
Page 101 and 102: Figure 6-9: Standard Deviation at D
Page 103 and 104: Table 6-3: Reported Notifications (
Page 105 and 106: Important for the determination of
Page 107: Remarkable is a comparison to the a
Page 111 and 112: 7 Summary Cooling devices within me
Page 113 and 114: Bibliography Books and Articles: [B
Page 115 and 116: [Wittenberg98] Reinhard Wittenberg,
Page 117 and 118: Appendix 1 - Implementation of Inte
Page 119 and 120: %Name and location of the source fi
Page 121 and 122: Appendix 2 - Implementation of Stat
Page 123 and 124: dailydate = [dailydate; floor(date(
Page 125 and 126: %Count Dooropenings per Day dailydo
Page 127 and 128: xlswrite(strcat(path, 'Excel\', fil
Page 129 and 130: print('-dtiff', strcat(path, 'Graph
Page 131 and 132: ar([dailydate(1):dailydate(length(s
Page 133 and 134: Appendix 3 - Implementation of Data
Page 135 and 136: Duration = 0; maxtemp = 0; elseif (
Page 137 and 138: %Contains [Duration, Number of Occu
Page 139 and 140: disp(strcat('Dooropening
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Casestudie Breakdown prediction Contell PILOT - Transumo

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