Casestudie Breakdown prediction Contell PILOT - Transumo

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this example. Hence, nighttime data of this chosen dataset is free from external user influences. 6.2.1 Detection of Changes in Behavior by Using Descriptive Statistics Section 5.10.1 introduced a promising way of recognizing changes in behavior by just comparing basic descriptive statistical measures. This section will now present calculated results for the selected dataset. As described in section 5.10.1, the suggested notification service compares succeeding daily daytime and nighttime values and reports irregular ones. This irregularity was defined as delta times the mean change. To obtain a better feeling for this delta, the calculated results are first of all presented in graphical form. Afterwards, two different deltas are chosen and their corresponding notifications are calculated. 80 The following figures 6-3 to 6-10 contain the calculated results of daily day- and nighttime values of the promising basic measurements. 81 To allow an easy comparison of daytime and nighttime values, they are plotted with the same scale. The results of the also suggested whole day analysis are not pictured because they do not differ significantly from the daytime results. The daytime maximum values are very irregular. In fact, they are almost comparable to the general temperature overview. But eye-catching is a very low maximum value at the end of November. The nighttime values offer a much clearer overview of the system’s general behavior due to the missing of external user influences. But again, the graph contains the eye-catching low temperature. As this exceptional value is also recognizable at nighttime, it should be reported to a person in charge. A closer look at Figure 6-2 discovers a real change of cooling behavior within that time span of nearly two days, so that this notification should be done. Beside this exceptional value the maximum nighttime values are faced with another significant change at the beginning of January. Eye-catching is, furthermore, the behavior at the end of January. Within very few days, the daily nighttime maximums increased about half a degree and nearly remained on that level for the rest of the monitoring time. This significant step also indicates a change in cooling behavior and should be notified. 80 See Table 6-2 for details 81 See section 5.10.1 for details 90
Figure 6-3: Maximum Values at Daytime Figure 6-4: Maximum Values at Nighttime 91
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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
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
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: 6.2 Case Study The UMC St. Radboud
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 and 108: Remarkable is a comparison to the a
Page 109 and 110: Interviews with several employees f
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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