Casestudie Breakdown prediction Contell PILOT - Transumo

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assumed to be asymptotic and only the stationary state is used for calculations. This leads to a linear system of equations that can be solved with less effort again. 56 Both, Markov chains and Markov processes have become important analyzing methods within many different settings. As mentioned above, they can be used, for instance, to predict the time of first occurrence of a critical system state. Looking back to the setting of machinery condition monitoring from section 4.2 would allow to use Markov chains, for example, to predict upcoming malfunctions due to friction. ([Waldmann04], p. 6-7) The Markov property seems to be promising also within the setting of sensor based temperature monitoring because a cooling device may malfunction at any time, no matter how long it worked fine before. But as already mentioned in section 2.2.5, a real technical malfunction has a very low unknown probability. Hence, starting distribution and transition matrix cannot be determined. 5.8 Inferential Statistics In contrast to descriptive statistics, inferential approaches do not describe available datasets but try to generalize gained knowledge from existing data. These methods are applied to problems where data cannot be obtained entirely. The general idea is to analyze a representative sample of the statistical universe. But only in case of a really representative sample, the gained results can be applied correctly to the whole statistical universe. ([Eckey02], p. 242) The generalization of gained information is always bound to probability calculation. Hence, the general approach of inferential statistics is to determine the distribution of a representative sample. Afterwards, this distribution can be used to perform interval estimations, hypothesis testing or other similar methods. 57 An application to sensor based temperature monitoring would require such a representative sample to determine the distribution. But in fact, such a representative sample does not exist, because of the randomness of external influences. This problem could partly be solved by applying monitoring data of a longer time period as representative sample to calculate the distribution. 56 See ([Waldmann04], Chapter 4) for details 57 See (e.g. [Scharnbacher04]) for details 72
But the greatest problem is again the probability calculation, because a calculated low probability of a short-term malfunction could lead again to the assumption that the corresponding cooling device will not break down. 58 5.9 Data Mining Data mining represents a special way of statistical data analysis. Its main purpose is to determine relationships between several items that were not recognized previously. Most often these relationships were not of primary interest at collection time. ([Gentle02], p. 123) To be able to apply data mining, many companies collect as much data as possible nowadays. In former times, check-outs in supermarkets, for instance, just summarized unit prices to calculate the final amount. Modern check-outs log every single product as well as other available data. Moreover, credit cards or discount cards allow a customer’s identification. ([Martin98], p. 249-250) These collected datasets can be analyzed by the use of data mining methods to gain additional knowledge. An aimed goal could be, for example, adapted sales promotion for different kinds of customers. Furthermore, the determination of the customer’s buying behavior could be of interest. A possible result could be that eighty percent of customers that buy beer do also buy potato chips. ([Lusti02], p. 262) 5.9.1 General Fields of Application The just quoted examples already introduced some very common approaches. In general, data mining is divided into five fields of application: ([Lusti02], p. 262) 1. Text mining 2. Association rule mining 3. Prediction 4. Clustering 5. Classification Text mining is the most basic field of application. Its purpose is to find patterns in text files for information retrieval. Therefore, special search algorithms have to be 58 See also section 5.6 73
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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
Page 27 and 28: device’s condition. Especially th
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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
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Page 41 and 42: Aside from these functions, the kin
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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
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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
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Page 85 and 86: sometimes data of a door opening se
Page 87 and 88: Beside the introduced notification
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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
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Page 117 and 118: Appendix 1 - Implementation of Inte
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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
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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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