Casestudie Breakdown prediction Contell PILOT - Transumo

More documents

Recommendations

Info

if ~isnan(import.data(i,6)); dooropenings = dooropenings + import.data(i,6); else disp(strcat('NaN found at_: ', datestr(date(position)))); end end for i = starttime:minute:date(position+jumplength); ID = [ID; [i, round(10 * interp1([date(position), date(position+jumplength)],[import.data(position,2), import.data(position+jumplength,2)],i,'linear'))/10,dooropenings,import.dat a(position,4),import.data(position,5)]]; dooropenings = 0; %To make sure, that number of dooropenings is only added once starttime = starttime + minute; %Correct calculation mistakes if mod(starttime,minute) >= second; starttime = (starttime - mod(starttime,minute)) + minute; end; end position = position + jumplength; %store to disk, if next 250 positions are reached (performance reasons) if position >= saveposition; dlmwrite(strcat(path, filename, '- Interpolated.txt'),ID, 'delimiter', ';', 'newline', 'pc', 'precision', '%.12f', '-append'); ID = []; saveposition = saveposition + 250; end end %Save the rest dlmwrite(strcat(path, filename, '- Interpolated.txt'),ID, 'delimiter', ';', 'newline', 'pc', 'precision', '%.12f', '-append'); disp(strcat('End of Computation:_', datestr(now))); %Import file back from disk interpolation = importdata(strcat(path, filename, '- Interpolated.txt')); %Show Summary of the imported data %Count Dooropenings in Original File dooropenings = 0; for i = 1:length(import.data); if ~isnan(import.data(i,6)); dooropenings = dooropenings + import.data(i,6); end end disp(strcat('Dooropenings (Original File):_', int2str(dooropenings))); disp(strcat('Dooropenings (Interpolated File):_', int2str(sum(interpolation(:,3))))); disp(strcat('Dataset Starting Time:_', datestr(interpolation(1,1)))); disp(strcat('Dataset Ending Time:_', datestr(interpolation(length(interpolation),1)))); A-114
Appendix 2 – Implementation of Statistical Methods This section will introduce the implementation of the suggested statistical data analysis. As described in section 6.1, the promising statistical measures are calculated on daily basis (whole day, daytime and nighttime). All results are exported to Microsoft Excel files to allow additional data analysis. Moreover, the graphs from chapter 6 are also plotted and saved to disk. Basic steps of this implementation are: 1. Import of monitoring data and interpolated data 2. Calculation of daily minimum and maximum (whole day, daytime, nighttime) (based on non-interpolated data) 3. Calculation of daily mean, mode, median, standard deviation (whole day, daytime, nighttime) (based on interpolated data) 4. Calculation of daily door openings (whole day, daytime, nighttime) 5. Calculation of temperature distribution 6. Creation of graphs 7. Storage of calculated values and graphs to disk The actual implementation is printed on the following pages. A-115
Page 1 and 2:
Technische Universität Braunschwei
Page 3 and 4:
4.4 Review of Current State of Rese
Page 5 and 6:
Index of Tables Table 2-1: Error of
Page 7 and 8:
1 Introduction 1.1 Initial Position
Page 9 and 10:
2 Sensor Based Temperature Monitori
Page 11 and 12:
compressor. They are served by a ce
Page 13 and 14:
the specified value, it works very
Page 15 and 16:
of this, over a hundred irregular p
Page 17 and 18:
a centralized one. An isolated appl
Page 19 and 20:
Figure 2-8: Lack of Information Pro
Page 21 and 22:
Moreover, the fridge’s filling le
Page 23 and 24:
store this kind of data but only of
Page 25 and 26:
3 Current Monitoring Systems The la
Page 27 and 28:
device’s condition. Especially th
Page 29 and 30:
a converter that is also available
Page 31 and 32:
To indicate important events within
Page 33 and 34:
1. Display stored data in table for
Page 35 and 36:
This way of visualizing data is the
Page 37 and 38:
free text. Moreover it is possible
Page 39 and 40:
dependent limit and delay settings,
Page 41 and 42:
Aside from these functions, the kin
Page 43 and 44:
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 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: %Name and location of the source fi
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
show all

Casestudie Breakdown prediction Contell PILOT - Transumo

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?