Casestudie Breakdown prediction Contell PILOT - Transumo

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for i = startposition:length(import.data(:,2)-1); if (import.data(i,6) == 1) & (import.data(i+1,6) == 0); Dooropeningtime = [Dooropeningtime; [date(i), round((date(i+1)- date(i))*60*60*24)]]; else if (import.data(i,6) == 1) & (import.data(i+1,6) == 1); jumplength = 0; while (import.data(i+jumplength,6) == 1) & (i + jumplength 0; DoorProbability = [DoorProbability; [i, OccurenceTemp, (OccurenceTemp/length(Dooropeningtime(:,1)))*100, sum(DoorProbability(1:length(DoorProbability(:,1)),3)) + (OccurenceTemp/length(Dooropeningtime(:,1)))*100]]; end OccurenceTemp = 0; end end %-----Display Information for a Certain Percentage----- Percentagelimit = 50; disp(strcat('Dooropening
disp(strcat('Dooropening
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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
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This algorithm returns an upper and
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• Critical values • Pre-warning
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5 Possible and Promising Ways of Da
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could be: “The mean increase of a
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The mode is the most frequent value
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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
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 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: %Contains [Duration, Number of Occu
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