Casestudie Breakdown prediction Contell PILOT - Transumo

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ar([dailydate(1):dailydate(length(daytimedooropenings))], daytimedooropenings, 'k'); datetick('x',20, 'keeplimits'); title 'Dooropenings at Daytime'; xlabel 'Date'; ylabel 'Number of Dooropenings'; axis([dailydate(1) dailydate(length(dailydooropenings)) min(dailydooropenings) max(dailydooropenings)]); print('-dtiff', strcat(path, 'Graphs\', filename, '- Dooropenings at Daytime.tif')); %Dooropenings at Nighttime bar([dailydate(1):dailydate(length(nighttimedooropenings))], nighttimedooropenings, 'k'); datetick('x',20, 'keeplimits'); title 'Dooropenings at Nighttime'; xlabel 'Date'; ylabel 'Number of Dooropenings'; axis([dailydate(1) dailydate(length(dailydooropenings)) min(dailydooropenings) max(dailydooropenings)]); print('-dtiff', strcat(path, 'Graphs\', filename, '- Dooropenings at Nighttime.tif')); end A-126
Appendix 3 – Implementation of Data Mining Methods This section will introduce the implementation of the suggested data mining methods. As described in section 6.1. Basic steps of this implementation are: 1. Import of monitoring data and interpolated data 2. Determination of alarms (original limits, self-defined limits) 3. Determination of alarms (with door opening recognition) 4. Determination of alarm durations and the corresponding classification limits 5. Determination maximum alarm temperatures and the corresponding classification limits 6. Determination of the duration of door openings and the corresponding classification limits The actual implementation is printed on the following pages. A-127
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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
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 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: ar([dailydate(1):dailydate(length(s
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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