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Publikationen von Professorinnen und Professoren der HAW Hamburg

Jahr:  
Alle : 1984, ... , 2016, 2017, 2018, 2019
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Alle : A, À, B, C, D, E, F, G, H, I, J, K, L, M, N, O, Ö, P, Q, R, S, T, U, V, W, Y, Z 
Alle : Gabriel, ... , Golas, Golby, Goldapp, ... , Görne 
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Zeitschriftenbeiträge:

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Günther Gravel und Thies Kahnenbley
Welligkeiten auf Zahnflanken und ihre Ursachen
Antriebstechnik,
20 September 2017

Marker: TI-MP

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Vera Schorbach, Peter Dalhoff und Peter Gust
Teeter design for lowest extreme loads during end impacts
Wind Energy,
19 September 2017
ISSN: 1099-1824

Marker: TI-MP

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Mike Gerdes, Diego Galar und Dieter Scholz
Genetic Algorithms and Decision Trees for Condition Monitoring and Prognosis of A320 Aircraft Air Conditioning
Insight - Non-Destructive Testing and Condition Monitoring, 59(8):424 - 433
August 2017
ISSN: 1354-2575

Marker: TI-FF

Zusammenfassung: Unscheduled maintenance is a large cost driver for airlines, but condition monitoring and prognosis can reduce the number of unscheduled maintenance actions. The paper shows condition monitoring can be introduced into most system by adopting a data-driven approach and using existing data sources. The goal is to forecast the remaining useful life (RUL) of a system based on various sensor inputs. We use decision trees to learn the characteristics of a system. The data for the decision tree training and classification are processed by a generic parametric signal analysis. To obtain the best classification results for the decision tree, the parameters are optimized by a genetic algorithm. A forest of three different decision trees with different signal analysis parameters is used as classifier. The proposed method is validated with data from an A320 aircraft from ETIHAD Airways. Validation shows condition monitoring can classify the sample data into ten predetermined categories, representing the total useful life (TUL) in 10 percent steps. This is used to predict the RUL. There are 350 false classifications out of 850 samples. Noise reduction reduces the outliers to nearly zero, making it possible to correctly predict condition. It is also possible to use the classification output to detect a maintenance action in the validation data.

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Felix Langfeldt, Hannes Kemsies, Wolfgang Gleine und Otto von Estorff
Perforated membrane-type acoustic metamaterials
Physics Letters A, 381(16):1457-1462
25 April 2017

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Rasmus Rettig, R. Günther, T. Wenzel und M. Wegner
Big Data Driven Dynamic Driving Cycle Development for Busses in Urban Public Transportation
Transportation Research Part D: Transport and Environment, Volume 51, :276-289
März 2017

Marker: TI-IE

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Mike Gerdes, Diego Galar und Dieter Scholz
Decision Trees and the Effects of Feature Extraction Parameters for Robust Sensor Network Design
Eksploatacja i Niezawodnosc – Maintenance and Reliability, 19(1):31-42
2017
ISSN: 1507-2711

Schlüsselwörter: decision trees, feature extraction, sensor optimization, sensor fusion, sensor selection

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Zusammenfassung: Reliable sensors and information are required for reliable condition monitoring. Complex systems are commonly monitored by many sensors for health assessment and operation purposes. When one of the sensors fails, the current state of the system cannot be calculated in same reliable way or the information about the current state will not be complete. Condition monitoring can still be used with an incomplete state, but the results may not represent the true condition of the system. This is especially true if the failed sensor monitors an important system parameter. There are two possibilities to handle sensor failure. One is to make the monitoring more complex by enabling it to work better with incomplete data; the other is to introduce hard or software redundancy. Sensor reliability is a critical part of a system. Not all sensors can be made redundant because of space, cost or environmental constraints. Sensors delivering significant information about the system state need to be redundant, but an error of less important sensors is acceptable. This paper shows how to calculate the significance of the information that a sensor gives about a system by using signal processing and decision trees. It also shows how signal processing parameters influence the classification rate of a decision tree and, thus, the information. Decision trees are used to calculate and order the features based on the information gain of each feature. During the method validation, they are used for failure classification to show the influence of different features on the classification performance. The paper concludes by analysing the results of experiments showing how the method can classify different errors with a 75% probability and how different feature extraction options influence the information gain.

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Uta Gaidys, G. Gius, M. Jarchow, G. Koch und H. Zinsmeister
hermA: Automated modelling of hermeneutic processes
Hamburger Journal für Kulturanthropologie , No 7,
2017

Marker: WS-PM

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Hardy Gundlach
Internetspezifische Ziele der Netzregulierung: Internet Governance und Netzneutralität.
MedienWirtschaft - Zeitschrift für Medienmanagement und Medienökonomie, H. 2, :18-25
2017

Marker: DMI-I

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Gunter Groen
Kognitive Verhaltenstherapie bei depressiven Kindern und Jugendlichen: Praxis und Konzepte.
Schweizerische Zeitschrift für Heipädagogik, 23(2):6-14.
2017

Marker: W&S

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Hardy Gundlach
Machbarkeit und Public Value des interaktiven Hörfunks.
MedienWirtschaft - Zeitschrift für Medienmanagement und Medienökonomie -, Praxisforum, 2017, Heft 1, S. 2 - 15,
2017

Marker: DMI-I

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Letzte Änderung: 23.01.15

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