Casestudie Breakdown prediction Contell PILOT - Transumo

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Table 6-4: Classification of Alarms Selected Offset time (Minutes) Number of Alarms Caused by Door Openings 1 139/158 2 155/158 3 156/158 4 157/158 10 158/158 To classify these user made alarms, the suggested classes from section 5.10.3 are taken. Moreover the suggested criteria are used to determine the current condition. Table 6-5 contains the corresponding results of data analysis of the selected sample dataset. Due to the historical behavior, a green alarm will currently be raised in case of a door opening that takes at least 19 seconds because 50 percent took less time. Moreover, a temperature of 6.9°C or higher and a temperature exceeding of 7 minutes or more would have the same effect. But as this data is calculated dynamically, these results only mirror a snapshot. Table 6-5: Results of Classification According to Single Criterions Duration of Door Openings Maximum Temperature During an Alarm Maximum Duration of an Alarm Green Alarm ≥ 19 Seconds ≥ 6,9°C ≥ 7 Minutes Yellow Alarm ≥ 38 Seconds ≥ 7,8 °C ≥ 12 Minutes Red Alarm ≥ 84 Seconds ≥ 9,8 °C ≥ 27 Minutes Occurred Red Alarms 39 8 42 9 Beside current limits for the single alarm classes, the last row of Table 6-5 is of special interest. It contains the number of red alarms that would have been raised during the whole monitoring time. This quantity of 42 red alarms is significantly different to 158, so that more than 60% of all occurred alarms could be classified as not that critical. But if this classification method is applied to very critical devices, additional conditions are needed. If, for instance, the temperature level may not exceed for more than 15 minutes, the red alarm should go off earlier. 100
Remarkable is a comparison to the actually applied method of setting a higher maximum temperature limit, which was used to reduce the quantity of false alarms. 84 Data analysis discovered that this set temperature limit was exceeded 152 times. In case of an unchanged temperature limit of 6°C, this number would have increased to 158. Hence, this method saved 6 alarms but increased the notification delay in case of a real malfunction. 6.3 Review Section 5.10.4 already pointed out the estimated improvements that might be achieved by using the suggested statistical and data mining methods. This section will review whether these estimated improvements really occurred. First of all, descriptive statistics led to the estimation that the currently limited ability to detect changes on the short-run may be improved. An appliance to the selected sample dataset showed that major changes in general cooling behavior were actually detected. Moreover, an adjustment to different security levels can be achieved by selecting bigger or smaller deltas. Only the appliance to daytime data does not provide reliable notifications, so that changes can only be recognized from morning to morning. But as this method recognized previously unknown irregularities, it definitely improves the recognition on the short-run. In addition to that, the appliance of regression provided very good results. The determined function had a very good fit ( R 2 = 0. 97492 ) and contained a gradient that described the really occurred temperature increase well. Hence, as long as the monitoring data is not faced with too many influences that lead to a fit less than 0.9, this method is able to reliably detect changes in behavior on the long-run. Section 5.10.4 pointed out that the combination of basic statistics and regression could lead to a limited ability to predict upcoming failures. In fact, both methods detected changes in behavior, but the cooling device kept on functioning. Hence, the gained results could be an indication for an upcoming malfunction but do not have to be. Moreover, the optimization of the cooling device’s usage by analyzing door openings cannot be assured within this diploma thesis but has to be tested in practice. The appliance of data mining confirmed the estimated improvements. The gain of additional system states improved the previously limited possibility to classify the 84 See introduction of chapter 6 for details 101
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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
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 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: Important for the determination of
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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