Casestudie Breakdown prediction Contell PILOT - Transumo

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Another problem is caused by the ring memory. It was implemented to ignore old measurement values to avoid a high influence of these values. This allows recognition of significant changes of general behavior on the short-run but disables analysis on the long-run. The succeeding steps curve selection, calculation of <strong>prediction</strong>, recognition of changing trends and the determination of the <strong>prediction</strong>’s probability are faced with two big problems, which are caused by a missing ability to recognize external influences. First of all, every door opening would lead to an assumption of a change in general behavior, although it should be ignored, if a general change should be identified. Moreover, this approach is also not capable of predicting upcoming failures, because door openings cannot be distinguished from real malfunctions. Faced with these problems, the verification module is not able to offer more reliable information of a device’s current state, than the introduced method from section 2.3. Table 4-1 summarizes the just made review. Table 4-1: Compliance of the Generalized Approach According to the Requirements Analysis Approach is able to classify the current state of a monitored device Approach is able to recognize significant changes of behavior on the shortrun 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 The introduced generalized approach seems to be applicable within many monitoring settings that suit the author’s assumptions. But especially the ignorance of outliers and the very low probability of a real technical malfunction 40 lead to a nonapplicability of this approach to the setting of sensor based temperature monitoring in practice. By contrast, single ideas, like the usage of regression and the other above mentioned approaches will be described and tested on applicability in the following chapter. 40 See section 2.2.5 for details 54
5 Possible and Promising Ways of Data Analysis The first four chapters already introduced the existing methodological problems of sensor based temperature monitoring systems and the current state of research. The second chapter pointed out the existing problems of the temperature monitoring task and limitations of the current approach of just setting critical temperature limits. The biggest problem was the existing lack of information. 41 This disabled an analyst to identify real causes of temperature deviations. Moreover, a very low probability of real malfunctions leads to many false alarms. 42 The introduction of currently available temperature monitoring products in the third chapter pointed out that no solution seems to be available that bases on an other approach. Only some workarounds like time dependent limit settings are offered to solve the existing problems partially. 43 In fact, no introduced product did fully comply with the requirements from section 2.5. In addition to that, the fourth chapter pointed out that there seems to be no research activity within this particular setting of sensor based temperature monitoring of cooling devices within medical laboratories. That is the reason why this chapter tries to find ways to gain additional information of monitored devices by the use of statistical analysis and data mining. Due to missing specialized methods, the current research begins with an analysis of basic statistical and data mining methods. Aside from that, other specialized methods from the fourth chapter are introduced and tested on applicability. 5.1 The Six Possible Levels of Data Analysis To be able to categorize different approaches of data analysis, it is important to review its possible kinds. Data analysis can be divided into six different levels of detail. Depending on the demands of the underlying setting, data analysis ranges from highly abstract to very detailed. According to the chosen level, one of the following kinds of results is aimed: ([Berthold99], p. 171) 41 See section 2.4.1 for details 42 See section 2.2.5 for details 43 See section 3.3.2 for details 55
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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
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 and 26: 3 Current Monitoring Systems The la
Page 27 and 28: device’s condition. Especially th
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: • Critical values • Pre-warning
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
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7 Summary Cooling devices within me
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Bibliography Books and Articles: [B
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[Wittenberg98] Reinhard Wittenberg,
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Appendix 1 - Implementation of Inte
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%Name and location of the source fi
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Appendix 2 - Implementation of Stat
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dailydate = [dailydate; floor(date(
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%Count Dooropenings per Day dailydo
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xlswrite(strcat(path, 'Excel\', fil
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print('-dtiff', strcat(path, 'Graph
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ar([dailydate(1):dailydate(length(s
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Appendix 3 - Implementation of Data
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Duration = 0; maxtemp = 0; elseif (
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%Contains [Duration, Number of Occu
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disp(strcat('Dooropening
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