Casestudie Breakdown prediction Contell PILOT - Transumo

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that are not caused by user interaction. This improvement would extend the currently limited possibility to recognize general changes on the short-run. 73 In addition to that, the suggested analysis of door openings might offer the possibility to optimize the usage of cooling devices. Although this was not part of the requirements analysis, a relief of frequently opened devices could lead to a better cooling behavior and less alarms. Also the suggested graphical distribution cannot improve factors of the requirement analysis. But it could indicate the cooling device’s accuracy and behavior at a glance. This additional knowledge might support decisions in case of uncertainty of the cooling device’s general condition. Section 5.10.2 presented a promising way to discover a trend by the use of regression. Such a trend would notify a change in general behavior on the long-run. Hence, a combination of basic statistics and regression could lead to a limited ability to predict upcoming failures. In fact, a definite <strong>prediction</strong> is not possible due to the very low probability and the lack of information. 74 But a detected change may hint a person in charge to have a closer look at that corresponding cooling device. These estimated improvements can even be extended by using not only statistical analysis, but also data mining. The suggested classification from section 5.10.3 establishes additional system states besides “OK” and “Malfunctioning”. These states could allow a more accurate description of the current system state because an alarm is rated. Based on these ratings, a person in charge could react to a temperature exceeding in a better way. Moreover, external influences are partly recognized because the classification of alarms also depends on the occurrence of door openings in advance of a temperature exceeding. Hence, a combination of statistical analysis and data mining is promising to significantly improve the current monitoring situation. The estimated improvements are summarized again in Table 5-1. Blue dashed arrows represent estimated improvements by the use of statistical analysis and magenta dashed arrows represent estimated improvements by the use of data mining. 73 See section 6.3 for details 74 See sections 2.4.1, 5.4.2 and 5.9.3 for details 84
Table 5-1: Estimated Improvements Approach is able to classify the current state of a monitored device Approach is able to recognize significant changes of behavior on the short-run Approach is able to recognize significant changes of behavior on the long-run Approach is able to predict upcoming failures Approach is able to identify failures as soon as they are recognizable Approach is able to avoid an error of second kind in any case Approach is able to recognize external influences Approach is able to optimize the usage of cooling devices Approach offers a very quick overview of the cooling device’s accuracy The succeeding chapter 6 will present an appliance of the introduced methods to a selected sample dataset from the UMC St. Radboud to evaluate the real improvements in practice. 85
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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
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
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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: A classification could be achieved
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
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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