Casestudie Breakdown prediction Contell PILOT - Transumo

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To achieve these and other aimed goals, several approaches of different conception levels do exist. In general, a decision between four different levels is made ([Kolerus95], p. 4): 1. Surveillance 2. Early recognition of failures 3. Failure diagnosis 4. Trend analysis Surveillance is the most basic goal. Its only task is to recognize a just occurred malfunction and to react in a predefined way (e.g. raise an alarm, shut down the machine, etc.). The early recognition of failures should not only detect already occurred malfunctions but also slightly occurring misbehavior to allow a reaction in advance of a total breakdown. The last two levels shall predict upcoming malfunctions before they actually occur. The failure diagnosis is based on analysis of sensor data. The trend analysis extends this diagnosis by predicting the actual time the malfunction will happen. Looking at the aimed goals and the different conception levels of machinery condition monitoring indicates the similarity to sensor based temperature monitoring. Especially the last two conception levels are comparable to the aimed requirements from section 2.5. Hence, approaches of machinery condition monitoring have to be tested on applicability. Probably the most common way of machinery condition monitoring in practice is the usage of vibration analysis. Its basic idea is to obtain information of the current condition of a machine by measuring the vibration level from important moving parts. The vibration changes over time due to friction. This measured vibration is compared to a rated value to classify the current condition of the monitored parts. Depending on the kind of machine, different VDI guidelines do exist that describe critical vibration values (e.g. VDI 2056, VDI 2059, etc.). ([Kolerus95], p. 8-19) This kind of condition monitoring is quite easy to implement by just attaching vibration sensors to important parts. But due to a single sensor usage, this approach is also faced with a lack of information and not able to recognize external influences. 29 To reduce the probability of externally influenced results, a filter can be applied to the measuring data to cut off untypical frequency ranges (e.g. [Kolerus95], p. 22-29). But 29 See section 2.4 for details 44
even this improvement is still faced with a lot of problems that are very similar to the described ones in section 2.4 (see e.g. [Pitter01], p. 63-68). Hence an aimed goal of current research is to sensor additional measures to improve this type of machinery condition monitoring. Therefore, the research and development of sensors is mainly based on two ideas: • To create multi sensors • To create “intelligent” sensors Multi sensors allow a monitoring of different measures at the same time at the same place ([Krallmann05], p. 50). Main advantages are a saving of costs, higher reliability and a fusion of different kinds of measurements at one place ([Pitter01], p. 76). 30 Intelligent sensors are based on mechatronics. The main idea of this research activity is to combine the fields of electronics, mechatronics and information processing ([Piiter01], p. 27). This combination allows an interaction between mechanical and electronic parts in form of a control cycle. The approach offers new possibilities of monitoring and data analysis. 31 But as this diploma thesis shall be based on currently existing data there will be no focus on this activity. Beside an improvement of sensors, current research focuses also on knowledge driven approaches. The main idea is to combine measured data with additional knowledge of the underlying process (e.g. [Tröltzsch06], p. 10). As this additional knowledge is specific for certain settings, only general ideas of research activities from one setting could be applied to another one. In general two knowledge driven approaches can be figured out: • Knowledge models • Artificial neural networks A knowledge model is the most specific approach. It contains information of the underlying process. Hence, current measurement values can be classified in a better way. Often, this kind of model is combined with vibration analysis to determine friction for instance. In this case, a knowledge model could contain additional 30 See e.g. [Krallmann05], [Pitter01] for details 31 See e.g. [Pitter01] 45
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: 4 Current State of Research As alre
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 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
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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
Page 133 and 134:
Appendix 3 - Implementation of Data
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Duration = 0; maxtemp = 0; elseif (
Page 137 and 138:
%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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