Casestudie Breakdown prediction Contell PILOT - Transumo

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identify the probable information gain by using descriptive statistics to better comply with the determined requirements. 63 The basic idea is to detect changes in general behavior by comparison of basic measures from different succeeding time intervals. This time interval can generally be chosen freely. As this diploma thesis assumes data of at least several months, a time interval of one day per value leads to meaningful results. 64 The smaller a time interval is chosen, the higher is the influence of new measurement values. On the other hand, too small time intervals, like hours for instance, could lead to significantly deviating results, even if the behavior of the monitored device did not change. This problem is mostly caused by the random behavior of employees. Even a chosen time interval of one day per value can lead to deviating results, if the user behavior differs significantly. To exclude random user behavior, a division of the analyzing task into daytime- and nighttime data analysis is promising. The door sensor data can be used to define the daily daytime and nighttime intervals. Daytime starts with the first door opening and ends a defined time range after the last door opening. This allows, for instance, a comparison of nighttime or daytime mean values of different days. As the nighttime values are not influenced by random user behavior, they should be very similar. Variances in daytime values could indicate employee deviance. Based on this idea, the aimed goal to gain additional knowledge can be added to XiltriX (or of course as well to any other monitoring system) by implementing an automated notification service. This service should calculate and compare daily daytime and nighttime values of minimum, maximum, mean and standard deviation. Median and Mode values are not promising due to their ignorance of outliers. 65 In case of a significant change of one of these values from one day to another, the person in charge should be notified. A classification as significant change should be done, as soon as the daily change is higher than delta times the regular changing behavior. To be able to define this behavior, historical data is needed. In general, data of a few days may suffice, but data of a longer time span probably assures the gained results. 63 See section 2.5 for details 64 See section 6.2.1 for details 65 See section 5.3 for details 80
Beside the introduced notification service, also a comparison of the door openings’ quantity to other devices and a graphical distribution of the stored temperature sequence should be added. The analysis of door openings could be used to optimize the usage of cooling devices. If, for example, two freezers of the same type do exist, they should have about the same quantity of door openings in the mean. Otherwise, some often accessed contents should be stored to the device with less door openings to improve the cooling behavior of both freezers. The graphical distribution could be used to get brief information of the cooling device’s accuracy. A very small distribution with a high peak indicates a very accurate behavior with little deviation. By contrast, a broad deviation or several smaller peaks may indicate a very inaccurate behavior. 66 This suggested distribution should offer highly aggregate information to evaluate the general behavior of the corresponding cooling device at a glance. Section 5.10.4 will review probable improvements by applying basic statistics and consecutively introduced ideas to the setting of sensor based temperature monitoring. The case study in chapter 6 applies these ideas to a sample dataset to validate these estimated improvements. 5.10.2 Detection of Changes in Behavior by the Use of Regression The last section introduced a promising possibility to detect changes in behavior on daily basis by the use of basic statistical measures. Also promising is the use of regression. Section 5.4.2 pointed out that regression can be used to describe a temperature sequence by determining a regression function. This possibility leads to two general ideas: 1. The comparison of single cooling cycles to each other 2. The determination of a trend in general behavior on the long run To compare single cooling cycles to each other, a polynomial regression function could be determined for a single representative cooling cycle. Afterwards, the coefficient of determination could be used to calculate the fit of other cycles to this regression function. 67 In case of a significant change in fit, a general change in behavior could be discovered. 66 See section 6.2.1 for details 67 See section 5.4.2 for details 81
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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
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 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: sometimes data of a door opening se
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 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 (
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%Contains [Duration, Number of Occu
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disp(strcat('Dooropening
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