Casestudie Breakdown prediction Contell PILOT - Transumo

More documents

Recommendations

Info

Figure 5-4: Sales of an Industrial Heater [Chatfield04] A decomposition of a time series y = t , s , o , i ) like that allows a nearly complete t ( t t t t description. Deviations are very small and have to be classified as other irregular fluctuations. A prediction, based on such a time series, leads to much better results than regression because the regular variations t, s, and o are taken into account. To be able to identify these components it is first of all necessary to define the interaction of the single components. In general, two models do exist. Formula 5-8 pictures the additive one and Formula 5-9 pictures the multiplicative one. ([Bourier03], p. 158-159) y = t + s + o + i t t Formula 5-8: The Additive Component Model t t t y t = t ⋅ s ⋅o ⋅i t t t t Formula 5-9: The Multiplicative Component Model The first model is normally used, if cyclic variations with constant amplitude are assumed. By contrast, the multiplicative model is used, if cyclic components are on the increase over time. ([Eckey02], p. 188) The first step of a time series analysis is the identification of a possible trend. A very common approach to identify a trend is the usage of the least squares method, as 66
explained in section 5.4. If such a trend is available, it can be removed from the existing data, so that the residual components can be determined. A time series without a trend is called stationary ([Chatfield04], p. 13). In fact, most methods require stationary time series data. After obtaining the trend, seasonal and other cyclic variations can be determined. This is done, for instance, by the use of the periodogram method. The general idea is to determine the distances to the trend function and to discover regular patterns. 51 But as already mentioned in section 5.4.2, a prediction of an upcoming malfunction is not possible because every significant rise in temperature would lead to such a prediction. Moreover, the very low probability of a real malfunction 52 in combination with randomly occurring external influences have to be classified as irregular variations. Faced with these significant irregular variations, time series analysis is not able to offer additional improvements, compared to regression. 5.6 Failure- and Availability Ratios Common within the settings of quality assurance and condition monitoring are operating ratios that specify the availability of systems and their tendency to run into failure. Common ratios to define this behavior are the “mean time to failure” (abbr. MTTF), the “mean time between failures” (abbr. MTBF) and the “mean time to repair” (abbr. MTTR). The first two measures characterize the average time a unit is working correctly before breaking down. The only difference between MTTF and MTBF is that the first one is used for parts that cannot or should not be repaired, but replaced. The second one is used for giving the average time between two necessary repairs of high value parts. The MTTR characterizes the average time the repairing takes. ([Masing88], p. 113) These ratios can be used to specify the availability of systems by using the Formula 5-10. This availability allows probability calculations, whether a system can be used during a specified time. 51 See (e.g. [Bourier03], p. 180-189) for details 52 See section 2.2.5 for details 67
Page 1 and 2:
Technische Universität Braunschwei
Page 3 and 4:
4.4 Review of Current State of Rese
Page 5 and 6:
Index of Tables Table 2-1: Error of
Page 7 and 8:
1 Introduction 1.1 Initial Position
Page 9 and 10:
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 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: The next section will introduce tim
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
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
show all

Casestudie Breakdown prediction Contell PILOT - Transumo

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?