Casestudie Breakdown prediction Contell PILOT - Transumo

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1. Descriptive models 2. Numerical models 3. Graphical models 4. Statistical models 5. Functional models 6. Analytic models Descriptive models represent the most abstract level of data analysis. They describe circumstances just by the use of verbal phrasing. ([Berthold99], p. 171) The sentence “The warmer the room ambient temperature, the higher the electric power consumption of a freezer”, for instance, already composes a small descriptive model. Although this kind of model does not give precise information of magnitude, it offers enough knowledge within many situations. By contrast, a descriptive model is not capable of solving the existing problems within the setting of sensor based temperature monitoring, because it is too abstract. A statement like “a cooling device is malfunctioning in case of reaching an uncommon temperature without being influenced externally” describes the problem very accurately. But it does not mention methods how to recognize these influences. Numerical models offer a more detailed description in form of a table. ([Berthold99], p. 171) Relating to the exemplified descriptive room ambient temperature model, an associated numerical model lists concrete room ambient temperatures versus the corresponding electric power consumption. The third level of data analysis is just the graphical representation of a numerical model. This is especially useful to get an overview of large datasets within very short time. 44 The current methodological approach of predefining critical limits to classify the current state of a monitored system is a numerical model, because every temperature value is assigned to either “cooling device is OK” or “cooling device is malfunctioning” The graphical abilities of the introduced products, suffice to comply also with the third level of data analysis. The last three levels of data analysis are not based on verbal phrases or sample values but on mathematical “descriptions” for all values to get a higher detail. Statistical models use measures like, for instance, the mean temperature or the standard deviation to illustrate coherences ([Berthold99], p. 172). A sample model 44 See section 3.2.1 for details 56
could be: “The mean increase of a freezer’s electric power consumption is about 5% per degree room ambient temperature”. Functional models use functions to describe the existing behavior. ([Berthold99], p. 172) Finding a functional description can be very difficult and is not always possible. A functional model of the freezer’s electric power consumption would allow a calculation for every given room temperature. Furthermore, it could help predicting malfunctions of a cooling device by just comparing current behavior to the describing function. Most powerful and detailed are analytic models. They describe coherences by the use of algebraic or differential equations. This allows a very detailed description of outputs for all kinds of imaginable inputs. ([Berthold99], p. 172) As already pointed out in section 2.4.1, many in- and outputs and their coherences are unknown due to very few sensors. That is why it seems to be impossible to find analytic models with the currently available datasets. Hence, this diploma thesis will first of all focus on possibilities to create statistical and functional models to gain more detailed information of monitored cooling devices. Only in case of reaching a degree of total information with this kind of models, an attempt to determine an analytic model would be useful. 5.2 Different Kinds of Statistical Analysis The general purpose of statistical analysis is to provide information to advance important decisions. The main idea is to improve the quality of the decision making process by reducing uncertainties as good as possible. In general, statistics is divided into two branches: ([Holland01], p. 3) 1. Descriptive statistics 2. Inferential statistics Descriptive statistical methods describe large available datasets. Their main purpose is to summarize and to evaluate them. Another important task is the filtering of most important facts to get an overview of the underlying dataset. Typical results of descriptive statistical methods are statistical measures like the mean or the standard deviation for instance. The results are presented in form of a table or a graph to offer a quick overview. ([Holland01], p. 3) 57
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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
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 and 60: • Critical values • Pre-warning
Page 61: 5 Possible and Promising Ways of Da
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
Page 111 and 112: 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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