TY  - JOUR
A1  - Khayyam, Hamid
A1  - Jamali, Ali
A1  - Bab-Hadiashar, Alireza
A1  - Esch, Thomas
A1  - Ramakrishna, Seeram
A1  - Jalili, Mahdi
A1  - Naebe, Minoo
T1  - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0
JF  - IEEE Access
N2  - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
Y1  - 2020
U6  - http://dx.doi.org/10.1109/ACCESS.2020.2999898
SN  - 2169-3536
VL  - 8
IS  - Art. 9108222
SP  - 111381
EP  - 111393
PB  - IEEE
CY  - New York, NY
ER  - 
TY  - JOUR
A1  - Pfaff, Raphael
A1  - Gidaszewski, Lars
A1  - Schmidt, Bernd
T1  - Berücksichtigung von No Fault Found im Diagnose- und Instandhaltungssystem von Schienenfahrzeugen
JF  - ETR - Eisenbahntechnische Rundschau
N2  - Intermittierende und nicht reproduzierbare Fehler, auch als No Fault Found bezeichnet, treten in praktisch allen Bereichen auf und sorgen für hohe Kosten. Diese sind häufig auf unpräzise Fehlerbeschreibungen zurückzuführen. Im vorliegenden Beitrag werden Anpassungen der Vorgehensweise bei der Entwicklung und Anpassungen des Diagnosesystems vorgeschlagen.
Y1  - 2020
SN  - 0013-2845
IS  - 5
SP  - 56
EP  - 59
PB  - DVV Media Group
CY  - Hamburg
ER  - 
TY  - JOUR
A1  - Khayyam, Hamid
A1  - Jamali, Ali
A1  - Bab-Hadiashar, Alireza
A1  - Esch, Thomas
A1  - Ramakrishna, Seeram
A1  - Jalil, Mahdi
A1  - Naebe, Minoo
T1  - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0
JF  - IEEE Access
N2  - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
Y1  - 2020
SN  - 2169-3536
U6  - http://dx.doi.org/10.1109/ACCESS.2020.2999898
SP  - 1
EP  - 12
PB  - IEEE
CY  - New York, NY
ER  - 
TY  - CHAP
A1  - Dinghofer, Kai
A1  - Hartung, Frank
T1  - Analysis of Criteria for the Selection of Machine Learning Frameworks
T2  - 2020 International Conference on Computing, Networking and Communications (ICNC)
Y1  - 2020
U6  - http://dx.doi.org/10.1109/ICNC47757.2020.9049650
SP  - 373
EP  - 377
ER  - 
TY  - JOUR
A1  - Wilbring, Daniela
A1  - Enning, Manfred
A1  - Pfaff, Raphael
A1  - Schmidt, Bernd
T1  - Neue Perspektiven für die Bahn in der Produktions- und Distributionslogistik durch Prozessautomation
JF  - ETR - Eisenbahntechnische Rundschau
Y1  - 2020
SN  - 0013-2845
VL  - 69
IS  - 3
SP  - 15
EP  - 19
ER  - 
TY  - CHAP
A1  - Finger, Felix
A1  - de Vries, Reynard
A1  - Vos, Roelof
A1  - Braun, Carsten
A1  - Bil, Cees
T1  - A comparison of hybrid-electric aircraft sizing methods
T2  - AIAA Scitech 2020 Forum
Y1  - 2020
U6  - http://dx.doi.org/10.2514/6.2020-1006
N1  - AIAA Scitech 2020 Forum, Driving aerospace solutions for global challenges, Orlando, 06. - 10. January 2020
ER  - 
TY  - CHAP
A1  - Kreyer, Jörg
A1  - Müller, Marvin
A1  - Esch, Thomas
T1  - A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude
T2  - Deutscher Luft- und Raumfahrtkongress 2019, „Luft- und Raumfahrt –  technologische Brücke in die Zukunft“,  Darmstadt, 30. September bis 2. Oktober 2019
Y1  - 2020
U6  - http://dx.doi.org/10.25967/490162
PB  - Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V
CY  - Bonn
ER  -