Casestudie Breakdown prediction Contell PILOT - Transumo

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The visual analysis of these graphs discovered that the selected dataset contains several changes in behavior. Most significant are the global minimum on September, 22 nd , and the change in temperature level on the end of November. As well eyecatching but not that significant are the mentioned changes right in the middle of September and the general rise in temperature in the year 2006. Looking at the graphs indicated, furthermore, that the notification of changes based on daytime data is not promising, due to the high number of external influences. After this graphical overview the numerical analysis will be evaluated by testing two different deltas. Meaningful results can be obtained by choosing five or ten as delta. The lower delta leads to earlier notifications, the higher delta only notifies higher deviations. Table 6-2 pictures the mean deviations in nighttime data as well as the minimum deviations that lead to a notification using the corresponding delta. Table 6-2: The Chosen Deltas Mean Deviation 5 x Mean Deviation 10 x Mean Deviation Maximum 0,027 0,135 0,27 Minimum 0,043 0,215 0,43 Mean 0,035 0,175 0,35 Standard Deviation 0,011 0,055 0,11 Table 6-3 pictures the calculated results. A yellow marked cell indicates a notification, due to a delta of five. If a delta of ten was predefined, only the red marked values would be notified. Using a delta of ten would have notified the most eye-catching changes in September and November. A delta of five would lead to 45 notifications. In fact, most of them are caused by the standard deviation and are not bound to significant changes in general behavior. Hence, the standard deviation should only be used with a high delta or left out. The residual measures can also be used with a delta of five. The made notifications in July, January and February can actually be traced back on small changes in cooling behavior, so that such a notification is right. Hence, comparing nighttime measures from succeeding time intervals enables the recognition of changes on the short-run (on daily basis). The notification level can be adapted by choosing a higher or a smaller delta. Presumably, different deltas for different kinds of machines have to be chosen to find the right balance between too many and too few notifications. 96
Table 6-3: Reported Notifications (Based on Nighttime Data) Maximum Minimum Mean Standard Deviation 15.06.2005 0 0 0,1 0,1 18.07.2005 0 0,3 0 0 19.07.2005 0 0 0,2 0,1 21.07.2005 0 0 0,1 0,1 23.07.2005 0 0 0,1 0,1 24.07.2005 0,1 0 0,1 0,1 03.08.2005 0,1 0 0 0,1 04.08.2005 0 0,1 0 0,1 10.08.2005 0 0 0,1 0,1 19.08.2005 0,1 0 0 0,1 20.08.2005 0,1 0 0 0,1 25.08.2005 0 0 0 0,1 26.08.2005 0 0 0 0,1 30.08.2005 0 0,3 0,1 0,1 20.09.2005 0 1,3 0,2 0,3 21.09.2005 0 0 0,4 0 22.09.2005 0,1 1 0,4 0,3 26.09.2005 0 0,1 0 0,1 27.09.2005 0 0,1 0 0,1 24.10.2005 0 0,2 0,2 0 28.11.2005 0 0,3 0,1 0,1 29.11.2005 0,3 0 0,2 0,1 30.11.2005 0,4 0 0,1 0,1 01.12.2005 0,1 0,4 0,3 0,1 05.01.2006 0,3 0 0,2 0 06.01.2006 0,2 0 0,1 0,1 10.01.2006 0 0 0,2 0 11.01.2006 0 0 0 0,1 12.01.2006 0 0 0,1 0,1 24.01.2006 0,2 0,2 0,2 0 26.01.2006 0 0 0 0,1 27.01.2006 0 0 0 0,1 01.02.2006 0 0 0 0,1 07.02.2006 0,1 0,2 0,1 0,1 23.02.2006 0,2 0 0,1 0,1 24.02.2006 0,1 0,1 0,2 0 03.03.2006 0 0 0,1 0,1 08.03.2006 0 0,2 0,1 0,1 11.03.2006 0,1 0 0,1 0,1 12.03.2006 0,1 0,1 0,1 0,1 15.03.2006 0,2 0 0,1 0 01.05.2006 0,2 0 0,1 0 11.05.2006 0 0,2 0,1 0,1 17.05.2006 0,1 0,2 0,1 0,1 23.05.2006 0,2 0 0,1 0 97
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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
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 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: Figure 6-9: Standard Deviation at D
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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