Casestudie Breakdown prediction Contell PILOT - Transumo

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Other Sources: [DEMO06] [UMC06] Exported data and screenshots <strong>Contell</strong>/IKS demo system Date of export: June – November, 2006 (according to requirements) Exported operating data UMC St. Radboud (University Hospital of Nijmegen, the Netherlands) Date of Export: June 1st, 2001 110
Appendix 1 – Implementation of Interpolation As already explained in section 6.1 the collected datasets have to be interpolated to obtain constant time intervals between single measuring values. This interpolation is done by the following algorithm. The basic steps of this algorithm are: 1. Import of the monitoring data 2. Conversion of date and time to the right format 3. Interpolation of a measurement value for every single minute (Number of door openings is stored to the beginning of a minute) 4. Storage of the calculated values to disk 5. Reimport of calculated values from disk for validation purposes To be able to import the CSV files from XiltriX, they have to be adapted, as already mentioned in section 6.1. The reason for that is a different usage of delimiters. XiltriX exports the data with a point as thousands separator and a comma as decimal separator. Matlab interprets the point as decimal separator and the comma as separator. To solve this problem, two simple replacements have to be done with a text editor in the following order: 1. Replace “.” with “” 2. Replace “,” with “.” An experienced programmer may recognize that the following codes are all coded in iterative manner and not object-oriented. The reason for that is the limited possibility Matlab offers. Indeed, it is possible to encapsulate at least procedures to so called M- files. 85 But they have a significant negative influence on the runtime. This phenomenon has to be traced back on the internal data exchange behavior. Hence, a simple iterative structure is used. Another problem of Matlab is the nonexistence of well scaling data types like linked lists. Hence, the following algorithm slows down very fast. The first thousand values, for instance, are calculated in about 45 seconds. That is nearly 10 times faster, than the second thousand values. The next thousand values take even more calculation time. 86 As the collected datasets contain about 37000 values, calculation would take 85 See (e.g.[Benker01], p. 48-55) 86 Tests were made with a Pentium 3 mobile, 1GHz, 256MB Ram A-111
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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
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: [Wittenberg98] Reinhard Wittenberg,
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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