Casestudie Breakdown prediction Contell PILOT - Transumo

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Figure 3-1: Flowchart of the Temperature Monitoring Task Section 2.4.1 introduced the lack of information problem that causes many false alarms. Figure 3-1 illustrates that even parts of the known data remain unused for classification purposes. This is indicated by the green and red arrows. Although the whole available data is collected and stored, only the current temperature and the predefined critical temperature limits are used by XiltriX to determine the cooling 20
device’s condition. Especially the stored historical temperature data is not used. Only the user has the possibility to analyze the collected data manually. The next sections will introduce the possibilities XiltriX is currently offering. This description is divided into three parts: 1. XiltriX’s components (this section) 2. XiltriX’s basic functionality (section 3.2) 3. XiltriX’s additional features (section 3.3) 3.1.1 Basic Components of a XiltriX Installation A basic XiltriX installation consists at least of a web server, one or more power supplies, one or more substations (called OS-4’s) and several temperature sensors (called PT100’s). Figure 3-2 pictures a schematic drawing of the connections between the single units of XiltriX. Although all parts are mandatory for a working XiltriX system, the web server is the most important one, because it contains the XiltriX software and stores the measurement data. The software is provided as a java applet. That means that no local installations are necessary. Every client just needs a web browser like the Microsoft Internet Explorer 6 and a connection to the local area network. In case of a web server’s breakdown, the whole XiltriX system will discontinue working. Also important are the OS-4’s. They are installed near the device that should be monitored. Every single of these substations offers the possibility to attach up to four sensors and up to four digital devices like switches, sirens or flashlights. 14 Furthermore, it is possible to connect up to ten substations in a row. The connection between web server and substations is made by the use of the system’s power supplies. Each power supply is capable to energize five rows of substations with a maximum number of ten devices per row. Furthermore, the same cable is used to relay the measurement data from the connected OS-4’s to the web server, so that no additional cable is needed. 14 See section 3.3.1 for further details 21
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: 3 Current Monitoring Systems The la
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 and 120:
%Name and location of the source fi
Page 121 and 122:
Appendix 2 - Implementation of Stat
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dailydate = [dailydate; floor(date(
Page 125 and 126:
%Count Dooropenings per Day dailydo
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xlswrite(strcat(path, 'Excel\', fil
Page 129 and 130:
print('-dtiff', strcat(path, 'Graph
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ar([dailydate(1):dailydate(length(s
Page 133 and 134:
Appendix 3 - Implementation of Data
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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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Casestudie Breakdown prediction Contell PILOT - Transumo

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