Casestudie Breakdown prediction Contell PILOT - Transumo

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[Holland01] Heinrich Holland & Kurt Scharnbacher, Grundlagen der Statistik, Gabler Verlag, Wiesbaden, 5. Edition, 2001 [Jondral02] Friedrich Jondral & Anne Wiesler, Wahrscheinlichkeitsrechnung und stochastische Prozesse, B.G. Teubner, Stuttgart, 2. Edition, 2002 [Kolerus95] Josef Kolerus, Zustandsüberwachung von Maschinen, Expert Verlag, Renningen-Malmsheim, 2. Edition, 1995 [Krallmann05] Jens Krallmann, Einsatz eines Multisensors für ein Condition Monitoring von mobilen Arbeitsmaschinen, Dissertation, TU Braunschweig, 2005 [Krems94] Josef F. Krems, Wissensbasierte Urteilsbildung, Hans Huber Verlag, Göttingen, 1. Edition, 1994 [Lusti02] Markus Lusti, Data Warehousing und Data Mining, Springer Verlag, Berlin, 2. Edition, 2002 [Martin98] Wolfgang Martin, Data Warehousing – Data Mining – OLAP, Thomson Publishing International, Bonn, 1. Edition, 1998 [Masing88] Dr. Walter Masing, Handbuch der Qualitätssicherung, Carl Hanser Verlag, München, 2. Edition, 1988 [Multhaupt00] Marko Multhaupt, Data Mining und Text Mining im strategischen Controlling, Shaker Verlag, Aachen, 1. Edition, 2000 [Nauth05] Peter Nauth, Embedded Intelligent Systems, Oldenbourg Verlag, München, 1. Edition, 2005 [Pitter01] Frank Pitter, Verfügbarkeitssteigerung von Werkzeugmaschinen durch Einsatz mechatronischer Sensorlösungen, Meisenbach Verlag, Bamberg, 1. Edition, 2001 [Sick00] Dipl.Inform. Bernhard Sick, Signalinterpretation mit Neuronalen Netzen unter Nutzung von modellbasierten Nebenwissen am Beispiel der Verschleißüberwachung von Werkzeugen in CNC- Drehmaschinen, VDI Verlag, Düsseldorf, VDI Reihe 10, Nr. 629, 2000 [Scharnbacher04] Heinrich Holland & Kurt Scharnbacher, Grundlagen statistischer Wahrscheinlichkeiten, Gabler Verlag, Wiesbaden, 1. Edition, 2004 [Turunen99] Esko Turunen, Mathematics behind Fuzzy Logic, Physica Verlag, Heidelberg, 1. Edition, 1999 [Waldmann04] Karl-Heinz Waldmann & Ulrike M. Stocker, Stochastische Modelle, Springer Verlag, Berlin, 1. Edition, 2004 108
[Wittenberg98] Reinhard Wittenberg, Grundlagen computerunterstützter Datenanalyse – Band 1, Lucius & Lucius, Stuttgart, 2. Edition, 1998 Interviewee: [Nijmegen06] Several Employees at UMC St. Radboud (University Hospital of Nijmegen, the Netherlands) Date: June 2 nd , 2006 [Weerdesteyn06] Han Weerdesteyn Product Manager of <strong>Contell</strong>/IKS WebPages: [2DI2006] [3M2006] [AES06] [DeltaTRAK06] [Rees06] [Triple06] [UniMunich06] Two Dimensional Instruments, LLC. (http://www.e2di.com/thermaviewer.html) Last visit: November 29 th , 2006 3M Worldwide (http://solutions.3m.com/wps/portal/3M/en_US/Microbiology/FoodS afety/products/time-temperature-indicators/) Last visit: November 28 th , 2006 AES Chemunex (http://www.aes-labguard.com) Last visit: November 29 th , 2006 DeltaTRAK (http://www.deltatrak.com/thermo_cdx.shtml) Last visit: November 29 th , 2006 Rees Scientific (http://www.reesscientific.com/Centron.htm) Last visit: November 29 th , 2006 Triple Red – Laboratory Technology (http://www.triplered.com/Products/alarms.htm) Last visit: November 29 th , 2006 University of Munich (http://leifi.physik.uni-muenchen.de/web_ph09/umwelt_technik /07kuehlschrank/kuehlschrank.htm) Last visit: November 8 th , 2006 109
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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
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: Bibliography Books and Articles: [B
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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Casestudie Breakdown prediction Contell PILOT - Transumo

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