Casestudie Breakdown prediction Contell PILOT - Transumo

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w ( t + 1) = w ( t) + α ⋅( d ( t) − y ( t) ⋅ x ( t) = w ( t) + α ⋅δ ( t) ⋅ x ( t) ij ij i i j ij i j with w α > 0 = Learning rate δ = Error x = Given input d i i i = Weighting of connection = Aimed result y = Calculated Output i ij i = 1, K, n from n j to n i Formula 5-22: The Delta Rule Unsupervised learning has to be used, if only the question of data analysis but not the result is available. The general idea is again to train the network with sample patterns. But this time, the network has to find and evaluate structures itself. A needed requirement is redundancy within the input vector. The more redundancy, the better the training results because it allows the identification of noise and disturbances. ([Heuer97], p. 18; [Hagen97], p. 19) Most unsupervised approaches use Hebb learning. This principle is adopted from the human brain because the weighting of the connection between two active neurons increases. Hebb defined that the weighting is proportional to the product of the two neurons’ outputs. Formula 5-23 summarizes this approach. ([Hagen97], p. 20) w ( t + 1) = w ( t) + α ⋅ y ( t) ⋅ y ( t) ij ij i j with w = Weighting of connection α > 0 = Learning rate y = Calculated Output i ij from n j to n i Formula 5-23: Hebb Learning Rule 5.9.3 Non-Applicability of Artificial Neural Networks to Current Datasets Although many different specific artificial neural networks do exist, they are always based on either supervised or unsupervised learning methods. An application of an unsupervised artificial neural network to currently obtained temperature data is not possible, because each input vector would only contain a time, a temperature and 78
sometimes data of a door opening sensor. These three variables always contain a different kind of information, so that each input vector is free of redundancy. But as mentioned in the last section, redundancy is necessary to identify structures or patterns in case of unsupervised learning. By contrast, supervised artificial neural networks are used within other settings to classify the condition of a monitored device. But always certain preconditions have to be kept. A neural network is able to predict upcoming failures of pumps for instance. This is possible because a pump shows a nearly constant behavior. Typical slight changes in behavior over time that indicate upcoming malfunction could be learned by an artificial neural network, because every device behaves nearly the same way. (e.g. [Hawibowo97], chapter 5) At the moment, a problem of appliance is that the provided datasets from the UMC St. Radboud only cover the time range of about a year. Moreover, not a single technical malfunction occurred during that year, 61 so that this data would be insufficient to train an artificial neural network on the recognition of technical malfunctions. But even, if the datasets would contain some errors, a general problem is again the very small quantity of real malfunctions. 62 This could lead to a learning behavior that ignores malfunctions because of very low weightings of the corresponding edges within the network. 5.10 Promising Analyzing Methods As neither the generalized approach from section 4.3.2 nor other approaches are directly applicable to the setting of sensor based temperature monitoring, this section will combine collected ideas to promising analyzing methods. Chapter 6 will apply these suggested approaches to data from the UMC St. Radboud and will review them according to the requirements analysis. 5.10.1 Promising Appliance of Basic Descriptive Statistics Section 5.3 introduced the most common descriptive statistical measures. As the basic ones are applicable to all kinds of stored numerical data, this section will 61 See section 2.2.5 for details 62 See section 2.2.5 for details 79
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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
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 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: The general idea of supervised lear
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
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
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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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Casestudie Breakdown prediction Contell PILOT - Transumo

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