@article{NeuJanserKhatibietal.2017,
  author    = {Neu, Eugen and Janser, Frank and Khatibi, Akbar A. and Orifici, Adrian C.},
  title     = {Fully Automated Operational Modal Analysis using multi-stage clustering},
  series = {Mechanical Systems and Signal Processing},
  volume    = {Vol. 84, Part A},
  journal   = {Mechanical Systems and Signal Processing},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0888-3270},
  doi       = {10.1016/j.ymssp.2016.07.031},
  pages     = {308 -- 323},
  year      = {2017},
  language  = {en}
}
@article{NeuJanserKhatibietal.2016,
  author    = {Neu, Eugen and Janser, Frank and Khatibi, Akbar A. and Orifici, Adrian C.},
  title     = {Automated modal parameter-based anomaly detection under varying wind excitation},
  series = {Structural Health Monitoring},
  volume    = {15},
  journal   = {Structural Health Monitoring},
  number    = {6},
  publisher = {Sage},
  address   = {London},
  issn      = {1475-9217},
  doi       = {10.1177/1475921716665803},
  pages     = {1 -- 20},
  year      = {2016},
  abstract  = {Wind-induced operational variability is one of the major challenges for structural health monitoring of slender engineering structures like aircraft wings or wind turbine blades. Damage sensitive features often show an even bigger sensitivity to operational variability. In this study a composite cantilever was subjected to multiple mass configurations, velocities and angles of attack in a controlled wind tunnel environment. A small-scale impact damage was introduced to the specimen and the structural response measurements were repeated. The proposed damage detection methodology is based on automated operational modal analysis. A novel baseline preparation procedure is described that reduces the amount of user interaction to the provision of a single consistency threshold. The procedure starts with an indeterminate number of operational modal analysis identifications from a large number of datasets and returns a complete baseline matrix of natural frequencies and damping ratios that is suitable for subsequent anomaly detection. Mahalanobis distance-based anomaly detection is then applied to successfully detect the damage under varying severities of operational variability and with various degrees of knowledge about the present operational conditions. The damage detection capabilities of the proposed methodology were found to be excellent under varying velocities and angles of attack. Damage detection was less successful under joint mass and wind variability but could be significantly improved through the provision of the currently encountered operational conditions.},
  language  = {en}
}
@article{PetersonRoethUibel2017,
  author    = {Peterson, Leif Arne and R{\"o}th, Thilo and Uibel, Thomas},
  title     = {Einsatz von Holzwerkstoffen in Fahrzeugstrukturen},
  series = {Bauen mit Holz},
  journal   = {Bauen mit Holz},
  number    = {3},
  publisher = {Bruderverlag},
  address   = {K{\"o}ln},
  issn      = {0005-6545},
  pages     = {32 -- 38},
  year      = {2017},
  language  = {de}
}
@article{WeiheErnstRoethetal.2013,
  author    = {Weihe, Stefan and Ernst, Ansgar and R{\"o}th, Thilo and Proksch, Johannes},
  title     = {Aluminium-Stahl-Verbundguss im Nutzfahrzeugbau},
  series = {ATZ - Automobiltechnische Zeitschrift},
  volume    = {115},
  journal   = {ATZ - Automobiltechnische Zeitschrift},
  number    = {4},
  publisher = {Springer Fachmedien Wiesbaden},
  issn      = {2192-8800 (Online)},
  pages     = {312 -- 316},
  year      = {2013},
  abstract  = {In modernen Fahrzeugkarosserien der Großserie kommen zunehmend Materialmischbauweisen zur Anwendung. In Zusammenarbeit der Daimler AG, der Tower Automotive Holding GmbH, der Imperia GmbH sowie der Partnerunternehmen KSM Castings GmbH und Schaufler Tooling GmbH \& Co. KG wird das Leichtbaupotenzial von Aluminiumverbundguss-Stahlblech-Hybriden am Beispiel des vorderen Dachquertr{\"a}gers des Mercedes-Benz Viano/Vito ausf{\"u}hrlich untersucht.},
  language  = {de}
}
@article{RoethPielenWolffetal.2018,
  author    = {R{\"o}th, Thilo and Pielen, Michael and Wolff, Klaus and L{\"u}diger, Thomas},
  title     = {Urbane Fahrzeugkonzepte f{\"u}r die Shared Mobility},
  series = {Automobiltechnische Zeitschrift - ATZ},
  volume    = {120},
  journal   = {Automobiltechnische Zeitschrift - ATZ},
  number    = {1},
  publisher = {Springer Vieweg},
  address   = {Wiesbaden},
  issn      = {0001-2785},
  doi       = {10.1007/s35148-017-0176-8},
  pages     = {18 -- 23},
  year      = {2018},
  abstract  = {Urbane Mobilit{\"a}tskonzepte der Zukunft erfordern neue Unternehmensformen, idealerweise aus Old Economy und New Economy, sowie eine enge Anbindung an die gesellschaftsrelevante Zukunftsforschung. F{\"u}r neue Fahrzeugkonzepte des Carsharing bedeutet dies, dass alle kostenverursachenden Faktoren erfasst und analysiert werden m{\"u}ssen. Die FH Aachen, share2drive und FEV geben einen Ausblick auf die zuk{\"u}nftige Fahrzeugklasse der Personal Public Vehicles als „Rolling Device".},
  language  = {de}
}
@article{WeiheAnsgarRoethetal.2013,
  author    = {Weihe, Stefan and Ansgar, Ernst and R{\"o}th, Thilo and Proksch, Johannes},
  title     = {Leichtmetall-Stahl-Verbundguss im Nutzfahrzeugbau},
  series = {Lightweight Design},
  volume    = {6},
  journal   = {Lightweight Design},
  number    = {2},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {2192-8738 (Online)},
  pages     = {38 -- 43},
  year      = {2013},
  abstract  = {In modernen Fahrzeugkarosserien der Großserie kommen zunehmend Materialmischbauweisen zur Anwendung. In Zusammenarbeit der Daimler AG, der Tower Automotive Holding GmbH, der Imperia GmbH sowie der Partnerunternehmen KSM Castings GmbH und Schaufler Tooling GmbH \& Co. KG wird das Leichtbaupotenzial von Stahlblech-AluminiumverbundgussHybriden am Beispiel des vorderen Dachquertr{\"a}gers des Mercedes-Benz Viano/Vito ausf{\"u}hrlich untersucht.},
  language  = {de}
}
@article{RuppSchulzeKuperjans2018,
  author    = {Rupp, Matthias and Schulze, Sven and Kuperjans, Isabel},
  title     = {Comparative life cycle analysis of conventional and hybrid heavy-duty trucks},
  series = {World electric vehicle journal},
  volume    = {9},
  journal   = {World electric vehicle journal},
  number    = {2},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2032-6653},
  doi       = {10.3390/wevj9020033},
  pages     = {Article No. 33},
  year      = {2018},
  abstract  = {Heavy-duty trucks are one of the main contributors to greenhouse gas emissions in German traffic. Drivetrain electrification is an option to reduce tailpipe emissions by increasing energy conversion efficiency. To evaluate the vehicle's environmental impacts, it is necessary to consider the entire life cycle. In addition to the daily use, it is also necessary to include the impact of production and disposal. This study presents the comparative life cycle analysis of a parallel hybrid and a conventional heavy-duty truck in long-haul operation. Assuming a uniform vehicle glider, only the differing parts of both drivetrains are taken into account to calculate the environmental burdens of the production. The use phase is modeled by a backward simulation in MATLAB/Simulink considering a characteristic driving cycle. A break-even analysis is conducted to show at what mileage the larger CO2eq emissions due to the production of the electric drivetrain are compensated. The effect of parameter variation on the break-even mileage is investigated by a sensitivity analysis. The results of this analysis show the difference in CO2eq/t km is negative, indicating that the hybrid vehicle releases 4.34 g CO2eq/t km over a lifetime fewer emissions compared to the diesel truck. The break-even analysis also emphasizes the advantages of the electrified drivetrain, compensating the larger emissions generated during production after already a distance of 15,800 km (approx. 1.5 months of operation time). The intersection coordinates, distance, and CO2eq, strongly depend on fuel, emissions for battery production and the driving profile, which lead to nearly all parameter variations showing an increase in break-even distance.},
  language  = {en}
}
@article{SerrorHackHenzeetal.2021,
  author    = {Serror, Martin and Hack, Sacha and Henze, Martin and Schuba, Marko and Wehrle, Klaus},
  title     = {Challenges and Opportunities in Securing the Industrial Internet of Things},
  series = {IEEE Transactions on Industrial Informatics},
  volume    = {17},
  journal   = {IEEE Transactions on Industrial Informatics},
  number    = {5},
  publisher = {IEEE},
  address   = {New York},
  issn      = {1941-0050},
  doi       = {10.1109/TII.2020.3023507},
  pages     = {2985 -- 2996},
  year      = {2021},
  language  = {en}
}
@article{PfaffGidaszewskiSchmidt2020,
  author    = {Pfaff, Raphael and Gidaszewski, Lars and Schmidt, Bernd},
  title     = {Ber{\"u}cksichtigung von No Fault Found im Diagnose- und Instandhaltungssystem von Schienenfahrzeugen},
  series = {ETR - Eisenbahntechnische Rundschau},
  journal   = {ETR - Eisenbahntechnische Rundschau},
  number    = {5},
  publisher = {DVV Media Group},
  address   = {Hamburg},
  issn      = {0013-2845},
  pages     = {56 -- 59},
  year      = {2020},
  abstract  = {Intermittierende und nicht reproduzierbare Fehler, auch als No Fault Found bezeichnet, treten in praktisch allen Bereichen auf und sorgen f{\"u}r hohe Kosten. Diese sind h{\"a}ufig auf unpr{\"a}zise Fehlerbeschreibungen zur{\"u}ckzuf{\"u}hren. Im vorliegenden Beitrag werden Anpassungen der Vorgehensweise bei der Entwicklung und Anpassungen des Diagnosesystems vorgeschlagen.},
  language  = {de}
}
@article{KhayyamJamaliBabHadiasharetal.2020,
  author    = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalili, Mahdi and Naebe, Minoo},
  title     = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0},
  series = {IEEE Access},
  volume    = {8},
  journal   = {IEEE Access},
  number    = {Art. 9108222},
  publisher = {IEEE},
  address   = {New York, NY},
  issn      = {2169-3536},
  doi       = {10.1109/ACCESS.2020.2999898},
  pages     = {111381 -- 111393},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}