Casestudie Breakdown prediction Contell PILOT - Transumo

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Hence, chapter 5 reviewed other promising approaches from chapter 4 on applicability. Especially the promising appliance of time series analysis and artificial neural networks failed, mainly because of the very low probability of a real malfunction and the missing of training data that contains malfunctions. By contrast, three methods were identified to improve the current monitoring situation. The first one is based on the statistical measures minimum, maximum, mean and standard deviation. The basic idea is to detect changes in general cooling behavior by comparison of these measures from succeeding time intervals. As soon as a change is significantly higher than average, the user should be notified of this change on the short-run. To avoid too many false notifications, only the uninfluenced nighttime data is used, which can be determined by the use of a door opening sensor. Moreover, linear regression could be used to determine a trend on the long-run. Although the temperature data is not linear, the achieved fit is sufficient to get reliable results. The last identified method is based on data mining. The general idea is, to compare current behavior to similar situations in the past and the succeeding developing. This enables a classification into different alarming levels. Chapter 6 introduced the implementation of the identified methods to Matlab and applies it to a selected sample dataset from the UMC St. Radboud (University hospital of Nijmegen, the Netherlands). As a result, the combination of statistical analysis and data mining is able to significantly improve the current monitoring situation. Changes in behavior on the short-run can be discovered, by comparing daily statistical measures. Moreover, regression can be used to determine changes in cooling behavior on the long-run. Using the suggested classification, leads to the gained additional decision support in case of a temperature exceeding. Only the aimed goal, to reliably predict upcoming failures can not be achieved because of unrecognizable external influences and the very low probability of a real technical malfunction. But the recognition of changes in cooling behavior might hint an upcoming malfunction automatically, so that also this aimed goal is at least partly reached. 106
Bibliography Books and Articles: [Beichelt97] Frank Beichelt, Stochastische Prozesse für Ingenieure, B.G. Teubner, Stuttgart, 1. Edition, 1997 [Benker01] Hans Benker, Statistik mit Mathcad und Matlab, Springer Verlag, Berlin, 1. Edition, 2001 [Berthold99] Michael Berthold & David J. Hand, Intelligent Data Analysis, Springer Verlag, Berlin, 1. Edition, 1999 [Blasig95] Reinhard Blasig, Neuronale Netze und die Induktion symbolischer Klassifikationsregeln, Dissertation, Universität Kaiserslautern, 1995 [Bohnekamp97] H. Bonekamp, Monitor to Guard Fridge Temperature, In: Elektor Electronics, Canterbury: Elektro Publ. Ltd, ISSN 0308-308X, 23, p. 58-61, 1997 [Bourier03] Günther Bourier, Beschreibende Statistik, Gabler Verlag, Wiesbaden, 5. Edition, 2003 [Chatfield04] Chris Chatfield, The Analysis of Time Series, Chapman & Hall/CRC, Boca Raton (Florida), Sixth Edition, 2004 [Daßler95] Frank Daßler, Tendenztriggerung – Meßdatenanalyse im on-line- Betrieb mit dem Ziel der frühzeitigen Erkennung und Vorhersage von Daten, Trends und Störungen, Dissertation, TU Chemnitz- Zwickau, 1995 [Eckey02] Hans-Friedrich Eckey & Reinhold Kosfeld & Christian Dreger, Statistik, Gabler Verlag, Wiesbaden, 3. Edition, 2002 [Gentle02] James E. Gentle, Elements of Computational Statistics, Springer Verlag, New York, 1. Edition, 2002 [Hagen97] Claudia Hagen, Neuronale Netze zur statistischen Datenanalyse, Dissertation, Technische Hochschule Darmstadt, 1997 [Hawibowo97] Singgih Hawibowo, Sicherheitstechnische Abschätzung des Betriebszustandes von Pumpen zur Schadensfrüherkennung, Dissertation, Technische Universität Berlin, 1997 [Heinzelmann99] Dipl.-Ing. Andreas Heinzelmann, Produktintegrierte Diagnose komplexer mobiler Systeme, VDI Verlag, Düsseldorf, VDI Reihe 12, Nr. 391, 1999 [Heuer97] Jürgen Heuer, Neuronale Netze in der Industrie, Gabler Verlag, Wiesbaden, 1. Edition, 1997 107
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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
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: 7 Summary Cooling devices within me
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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