Casestudie Breakdown prediction Contell PILOT - Transumo

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6 Implementation and Case Study This chapter will apply the promising analyzing methods from section 5.10 to a selected dataset from the UMC St. Radboud. Therefore, section 6.1 will introduce the major problems and the actually found solutions to perform the data analysis task of the exported XiltriX data. Afterwards, the calculated results will be presented. A review of the information gain according to the just defined estimated improvements will conclude this chapter. 6.1 Implementation of Promising Analyzing Methods Section 3.2.1 already introduced the stored data of XiltriX. The export functionality allows a storage of this data to disk as a comma separated value file (CSV). An excerpt of such a file is pictured in Figure 6-1. Figure 6-1: Exported XiltriX Data (An Excerpt) CSV is a standard file format and should be read by a large number of programs. In fact, the import of this data to other programs is problematic due to the following two reasons: • Programs failed to import the string based date and time correctly • Programs failed to manage the occurring large data sets Table 6-1 contains a listing of tested programs and the existing problems: Table 6-1: Import Problems of Tested Software Products Origin Euler Rt-Plot FreeMat MS Excel 7.5 2.4 2.7 2.0 2002 Product is able to manage the occurring large datasets Product is able to import date and time correctly 86
As all these software products fail to offer a satisfying solution, Matlab is used in the following to implement the suggested methods. In fact, also Matlab has some problems to import the original datasets, but these problems can easily be solved by changing some delimiters in the CSV file. 75 Moreover, Matlab is capable of importing date and time correctly and is able to process very large datasets. In the following, the general ideas of the made implementation will be introduced. The technical realization and annotations to occurred problems, due to Matlab’s limited programming possibilities, can be found in the appendix. Caused by the storage behavior of XiltriX 76 the stored values contain different time intervals. An example of this behavior is pictured in Figure 6-1. But the suggested analyzing methods from section 5.10 assume constant time ranges. Hence, the first step of data analysis is an interpolation of the stored datasets. The basic idea is, to create new datasets of measurement values that contain regular time intervals. Door openings are stored to the beginning of the minute, in which they occur. A combination of the original and the interpolated datasets can be used to calculate the desired values, as described in the following, without making adaptations to the described methods. Certainly, in case of an implementation to XiltriX, its storage behavior should be adapted, so that interpolation is not necessary any more. After this interpolation, the desired statistical measures can be calculated. Therefore, the original and the interpolated datasets are divided into single days and these days again into daytime and nighttime. As described in section 5.10.1, daytime limits can be obtained by analyzing the door openings. Based on these made classes, the promising measures maximum, minimum, mean, standard deviation and the number of door openings can be calculated on the aimed basis of daytime, nighttime and whole day. To obtain correct results, the calculation of minimum and maximum values has to be based on the original data to avoid smoothing. By contrast, mean as well as standard deviation has to be based on the interpolated data to achieve a correct weighting in time. The determination of door openings can be based on both datasets, because the number of door openings remains unchanged after interpolation. The also aimed goal, to plot a temperature distribution, can be implemented easily by just counting 75 See appendix for details 76 See section 3.2.1 for details 87
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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,
Page 41 and 42: Aside from these functions, the kin
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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: Table 5-1: Estimated Improvements A
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 and 138: %Contains [Duration, Number of Occu
Page 139 and 140: disp(strcat('Dooropening
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