Casestudie Breakdown prediction Contell PILOT - Transumo

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medianvector = [medianvector; median(interpolation(startposition:startposition + jumplength - 1,2))]; modevector = [modevector; mode(interpolation(startposition:startposition + jumplength - 1,2))]; stdvector = [stdvector; std(interpolation(startposition:startposition + jumplength - 1,2))]; %Day- and Nighttime... nighttemp = []; daytemp = []; for j= startposition:startposition + jumplength - 1; if (mod(interpolation(j,1) + second, 1) >= daybegin) && (mod(interpolation(j,1) + second, 1)
xlswrite(strcat(path, 'Excel\', filename, '- Median'), [interpolateddailydate-693960, round(medianvector*10)/10, round(mediandaytime*10)/10, round(mediannighttime*10)/10], 'Median', 'A2'); xlswrite(strcat(path, 'Excel\', filename, '- Mode'), [interpolateddailydate-693960, round(modevector*10)/10, round(modedaytime*10)/10, round(modenighttime*10)/10], 'Mode', 'A2'); xlswrite(strcat(path, 'Excel\', filename, '- Standard Deviation'), [interpolateddailydate-693960, round(stdvector*10)/10, round(stddaytime*10)/10, round(stdnighttime*10)/10], 'Standard Deviation', 'A2'); xlswrite(strcat(path, 'Excel\', filename, '- Doordopenings'), [interpolateddailydate-693960, dailydooropenings, daytimedooropenings, nighttimedooropenings], 'Dooropenings', 'A2'); %Total Values totalmean = mean(interpolation(:,2)); totalmedian = median(interpolation(:,2)); totalmode = mode(interpolation(:,2)); totalstd = std(interpolation(:,2)); totaldooropenings = sum(interpolation(:,3)); %-----Temperature Distribution----- %Count total occurrences of single values %"Round" Command necessary in MATLAB. Otherwise some comparisons fail! %Contains[Temperature, Minutes of Occurence] totalOC = []; for i = min(interpolation(:,2)):0.1:max(interpolation(:,2)); totalOC = [totalOC; [i, sum(interpolation(:,2) == round(i*10)/10)]]; end %-----Plot Statements----- %Temperature Overview plot(date, import.data(:,2), 'k'); hold on; plot(interpolation(:,1), interpolation(:,4), '--b') plot(interpolation(:,1), interpolation(:,5), '--r') datetick('x',20, 'keeplimits'); title 'Temperature Overview'; xlabel 'Date'; ylabel 'Temperature (°C)'; axis([min(date) max(date) min(import.data(:,2)) max(import.data(:,2))]); hold off; print('-dtiff', strcat(path, 'Graphs\', filename, '- Temperature Overview.tif')); %Maximum Values per Day bar([dailydate(1):dailydate(length(maxvector))], maxvector, 'k'); hold on; plot(interpolation(:,1), interpolation(:,4), '--b') plot(interpolation(:,1), interpolation(:,5), '--r') datetick('x',20, 'keeplimits'); title 'Maximum Values per Day'; xlabel 'Date'; ylabel 'Temperature (°C)'; axis([dailydate(1) dailydate(length(maxvector)) min(maxvector) max(maxvector)]); A-121
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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
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 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: %Count Dooropenings per Day dailydo
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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