Casestudie Breakdown prediction Contell PILOT - Transumo

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Figure 2-5: Temperature Sequence of a -80°C Passive Freezer [DEMO06] Figure 2-6: Temperature Sequence of a -20°C Active Freezer [DEMO06] Hence, the temperature sequence of a corresponding monitored cooling device changes to a more irregular pattern. Types and origins of external influences will be identified in section 2.4.2. Up to then, the following example should just give a general idea of temperature sequences in practice. Figure 2-7 shows the behavior of a properly working cryogenic -180°C freezer in practice. The data originates from the UMC St. Radboud (University hospital of Nijmegen, the Netherlands) and represents typical behavior for that kind of devices. 4 In contrast to previous examples, this figure pictures a larger time slice. These ten months are chosen to give an impression of practical behavior on the long run. Recognizable is a baseline at about -183°C. Due to the different scaling, the figure does not picture the single cooling cycles any more, although they do exist. Instead 4 The university hospital of Nijmegen provided their datasets only as a copy from their XiltriX system. That is, why Matlab was used to draw this graph. Beside the slightly different appearance, the data would look the same in Xiltrix. 8
of this, over a hundred irregular peaks are pictured that cannot be traced back to typical technical behavior. In fact, not a single peak is caused by a technical malfunction [Nijmegen06]. Hence, monitoring systems have to be able to figure out, whether such a peak is caused by a technical malfunction or by external influences. Figure 2-7: Temperature Sequence of a Cryogenic Freezer in Practical Use [UMC06] 2.2.5 Behavior in Case of a Malfunction Unfortunately, the provided 36 datasets of the UMC St. Radboud do not contain a single technical malfunction [UMC06], [Nijmegen06]. In fact, most cooling devices operate for years without having a single failure, which leads to a very low probability of a technical malfunction. Hence, it is not possible to introduce a sample pattern here. Nevertheless, the following criteria are identified to hint a malfunction in case of not being influenced externally [Weerdesteyn06]: 1. Form and shape of the temperature sequence changes significantly on the short-run 2. Form and shape of the temperature sequence changes significantly on the long-run 3. Temperature exceeds the range of normal operation 9
Page 1 and 2: Technische Universität Braunschwei
Page 3 and 4: 4.4 Review of Current State of Rese
Page 5 and 6: Index of Tables Table 2-1: Error of
Page 7 and 8: 1 Introduction 1.1 Initial Position
Page 9 and 10: 2 Sensor Based Temperature Monitori
Page 11 and 12: compressor. They are served by a ce
Page 13: the specified value, it works very
Page 17 and 18: a centralized one. An isolated appl
Page 19 and 20: Figure 2-8: Lack of Information Pro
Page 21 and 22: Moreover, the fridge’s filling le
Page 23 and 24: store this kind of data but only of
Page 25 and 26: 3 Current Monitoring Systems The la
Page 27 and 28: device’s condition. Especially th
Page 29 and 30: a converter that is also available
Page 31 and 32: To indicate important events within
Page 33 and 34: 1. Display stored data in table for
Page 35 and 36: This way of visualizing data is the
Page 37 and 38: free text. Moreover it is possible
Page 39 and 40: dependent limit and delay settings,
Page 41 and 42: Aside from these functions, the kin
Page 43 and 44: 1. Temperature verification in retr
Page 45 and 46: alarms by the use of additionally a
Page 47 and 48: The third mentioned Centron Environ
Page 49 and 50: 4 Current State of Research As alre
Page 51 and 52: even this improvement is still face
Page 53 and 54: Another current approach is regress
Page 55 and 56: 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
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5.4.2 The Major Problems of Regress
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The next section will introduce tim
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explained in section 5.4. If such a
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pictured in Formula 5-12. Beside a
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P ( m) = P ( r ) ⋅ P ( m−r ) wi
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But the greatest problem is again t
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clustering as well as an analysis o
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The general idea of supervised lear
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sometimes data of a door opening se
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Beside the introduced notification
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A classification could be achieved
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Table 5-1: Estimated Improvements A
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As all these software products fail
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6.2 Case Study The UMC St. Radboud
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Figure 6-3: Maximum Values at Dayti
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Figure 6-5: Minimum Values at Dayti
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Figure 6-9: Standard Deviation at D
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Table 6-3: Reported Notifications (
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Important for the determination of
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Remarkable is a comparison to the a
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Interviews with several employees f
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7 Summary Cooling devices within me
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Bibliography Books and Articles: [B
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[Wittenberg98] Reinhard Wittenberg,
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Appendix 1 - Implementation of Inte
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%Name and location of the source fi
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Appendix 2 - Implementation of Stat
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dailydate = [dailydate; floor(date(
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%Count Dooropenings per Day dailydo
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xlswrite(strcat(path, 'Excel\', fil
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print('-dtiff', strcat(path, 'Graph
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ar([dailydate(1):dailydate(length(s
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Appendix 3 - Implementation of Data
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Duration = 0; maxtemp = 0; elseif (
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%Contains [Duration, Number of Occu
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disp(strcat('Dooropening
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Casestudie Breakdown prediction Contell PILOT - Transumo

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