Casestudie Breakdown prediction Contell PILOT - Transumo

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This idea would allow recognition of changes within very short time. Nevertheless, it is not promising because the single cooling cycles differ from each other due to technical and other reasons. 68 Hence, an appliance of this method would lead to a high quantity of additional false alarms. The second idea is based on the assumption that temperature sequences of cooling devices may not contain a trend on the long-run. To obtain a presumable trend, a linear regression function could be used. In case of a good fit (coefficient of correlation ≥ 0.9) 69 the gradient of the regression function determines the trend. As already mentioned, this method is only promising on the long-run, because linear regression functions on the short-run are highly influenced by outliers. 70 This would lead to a too small coefficient of determination. 5.10.3 Classification by Using Past Behavior As a real malfunction is very improbable and the current number of alarms leads to a loss of credibility, more system states have to be made up than just “OK” and “Malfunctioning”. 71 Moreover, most alarms are user made due to door openings [Nijmegen06]. An appliance of data mining methods may provide these additional system states. The main idea is, to compare current behavior to similar situations in the past and the succeeding developing. Therefore, classification of alarms into different levels (e.g. green, yellow, red) could be used to indicate how critical a current temperature exceeding is. To achieve such a classification, every alarming situation is compared to all other situations in the past. The general assumption is that an alarm is only classified as a red one, if it is significantly different to most previous ones. Furthermore, alarms that cannot be connected to a previous door opening should immediately be classified as a red alarm. To be able to classify the door made alarms, different criteria have to be found. A promising suggestion is the usage of the following criteria: • The duration of door openings • The maximum temperature during an alarm • The maximum duration of an alarm 68 See section 2.3 for details 69 See section 5.4.2 for details 70 See Figure 5-2 for details 71 See section 4.3.2 for details 82
A classification could be achieved by calculating the probability of the actual situation, based on historical data. The underlying assumption is that a situation that is more exceptional than any other in the past indicates a critical situation of a hundred percent probability. If, for instance, a current door opening already takes more than one minute, a comparison to past values could conclude that ninety percent of all door openings took less time. Hence, the probability that the current situation is critical is ninety percent. To put this value on a larger basis, the maximum probability of all three criteria should be taken. The probability of a critical situation could be used to define the above suggested alarm levels: • Green alarm: probability of a critical situation ≥ 50% • Yellow alarm: probability of a critical situation ≥ 75% • Red alarm: probability of a critical situation ≥ 95% This definition is only used exemplarily and can be adapted to other values. Especially the made assumption that probabilities < 50% shall not be classified as alarms, although the critical temperature level is exceeded, might be problematic in some settings. But this assumption saves a lot of alarms without increasing the risk too much. 72 Using classification like this offers the person in charge additional operational decision support, whether an occurring alarm has to be taken seriously or not. Section 6.2.3 will apply this method to a sample dataset to point out the possible improvements. 5.10.4 Review The last three sections introduced promising ideas to improve the current monitoring situation. This section will now review the expected improvements. Whether these methods really lead to the expected gain of information will be reviewed in chapter 6 by applying them to a sample dataset. Section 5.10.1 pointed out, that the appliance of descriptive statistics to monitoring data might be used to recognize significant changes of general cooling behavior on daily basis. Especially the analysis of daily nighttime values could recognize changes 72 See section 6.2.3 for details 83
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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
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: Beside the introduced notification
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
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
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disp(strcat('Dooropening
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