Casestudie Breakdown prediction Contell PILOT - Transumo

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Table 2-1: Error of First and Second Kind H 0 is correct H 0 is wrong Acceptance H 0 Right decision Error of second kind Rejection H 0 Error if first kind Right decision The succeeding subsection will introduce the major problem, sensor based temperature monitoring is faced with and its consequences on first and second error. 2.4.1 The Lack of Information Problem Currently the major problem within the setting of sensor based temperature monitoring of cooling devices is a lack of information. All well known systems in the market only attach a single temperature sensor to a fridge. That is why in most cases only the current temperature of a cooling device is available for analyzing purposes. Advanced systems like the below introduced XiltriX offer, for instance, the possibility to add an additional door sensor. So, there is at least a second piece of information available. In fact, there are many factors that have an influence on the temperature inside a fridge. Figure 2-8 specifies some of the factors and illustrates the problem of current systems. Of course, it would be possible to add additional sensors to every monitored device. But their quantity is always kept small to minimize expenses [Nijmegen06]. For example, every temperature sensor for XiltriX causes additional costs of about 500€ [Weerdesteyn06]. This leads to a one sensor usage, sometimes in combination with a door opening sensor. This lack of information problem causes the cooling device to be a black box and disables the finding of real causes of temperature deviations. Especially the needed information, whether a fridge is significantly externally influenced within a certain time cannot be obtained for sure. 7 This problem leads to potential sources of error, when analyzing temperature sequences. As an error of second kind has to be avoided in any case, the quantity of first kind errors increases within situations of unknown influences. 7 See section 2.4.2 for details 12
Figure 2-8: Lack of Information Problem Caused by Sensor Based Temperature Monitoring A second problem even increases the lack of information. It is caused by the unknown behavior between two single measuring points. Figure 2-9 exemplifies a rising and falling of temperature between two of these points. Analyzing this data in retrospect would disregard this actual behavior. Furthermore, a graphical and a numerical analysis would assume a constant temperature within this interval, as indicated by the red dashed line. Figure 2-9: The Problem of Unknown Behavior between Two Single Data Points 13
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 and 14: the specified value, it works very
Page 15 and 16: of this, over a hundred irregular p
Page 17: a centralized one. An isolated appl
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
Page 69 and 70:
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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