Category Archives: Industrie

New Article: Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data #ComputationalIntelligence #MachineLearning

The article “Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data” (Alexis Sardá-Espinosaa, Subanatarajan Subbiah, Thomas Bartz-Beielstein), which will be published in the journal “Engineering Applications of Artificial Intelligence“, can be freely downloaded until May 20, 2017 from
https://authors.elsevier.com/a/1Uojb3OWJ8l3Gq
Anyone who clicks on the link until May 20, 2017, will be taken to the final version of your article on ScienceDirect for free. No sign up or registration is needed – just click and read!

Abstract: Computational tools for the analysis of data gathered by monitoring systems are necessary because the amount of data steadily increases. Machine learning algorithms can be used in both regression and classification problems, providing useful insights while avoiding the bias and proneness to errors of humans. In this paper, a specific kind of decision tree algorithm, called conditional inference tree, is used to extract relevant knowledge from data that pertains to electrical motors. The model is chosen due to its flexibility, strong statistical foundation, as well as great capabilities to generalize and cope with problems in the data. The obtained knowledge is organized in a structured way and then analyzed in the context of health condition monitoring. The final results illustrate how the approach can be used to gain insight into the system and present the results in an understandable, user-friendly manner.

Keywords: Decision tree; Conditional inference tree; Health condition monitoring; Machine learning; Knowledge extraction

Authors: Alexis Sardá-Espinosa (ABB AG German Research Center, Technische Hochschule Köln),  Subanatarajan Subbiah (ABB AG German Research Center), Thomas Bartz-Beielstein (Technische Hochschule Köln)

Here is the DOI: 10.1016/j.engappai.2017.03.008

 

 

CfP: Multidisciplinary Design #Optimisation @EUROGEN2017


Across all fields of Engineering Sciences, many design problems are multidisciplinary in nature. An optimal design can be achieved if all the disciplines are concurrently considered in an integrated approach. In MDO the whole is more than the sum of the parts, therefore the optimum of the integrated problem is superior to the design found by optimizing each discipline independently. However, including all disciplines simultaneously significantly increases the complexity of the problem. The optimal design of each discipline can be in itself a hard and computationally intensive optimization problem. In addition, the definition of the level of fidelity of the model for each discipline, the interexchange of variables of different nature (the output of one discipline can become the input to another) and the increased dimensionality, contribute to make the problem considerably harder. The largest number of applications is in the field of aerospace engineering, such as aircraft and spacecraft design in which aerodynamics, structural analysis, propulsion, control theory, and economics are integrated in a single optimization process. But many techniques have been developed and applied in a number of different fields, including automotive design, naval architecture, electronics, computers, and electricity distribution. More: http://eurogen2017.etsiae.upm.es/minisymposia/

@UTOPIAE_network @TH_Koeln: Uncertainty Treatment and OPtimisation In Aerospace Engineering

Mit “UTOPIAE – Uncertainty Treatment and OPtimisation In Aerospace Engineering” beteiligt sich die TH Köln zum vierten Mal an einem von der EU geförderten Marie-Curie-Innovations-Ausbildungsprogramm. Mit rund 3.9 Millionen Euro Fördersumme forschen europaweit 15 Doktorandinnen und Doktoranden interdisziplinär an der Optimierung der computergenerierten Konstruktion von Luft- und Raumfahrtzeugen.
Koordiniert von der Strathclyde University in Schottland arbeiten insgesamt 15 Hochschulen, Forschungseinrichtungen und Firmen in Großbritannien, Italien, Belgien, Frankreich und den USA zusammen. Darunter die Stanford University, Airbus Operations GmbH und die Deutsche Luft- und Raumfahrt. Die TH Köln übernimmt dabei Aufgabenbereiche aus der Mathematik und Informatik unter der Leitung der Professoren Dr. Thomas Bartz-Beielstein und Dr. Boris Naujoks von der Fakultät für Informatik und Ingenieurwissenschaften.

Den vollständigen Text der Pressemitteilung finden Sie unter https://www.th-koeln.de/hochschule/utopiae–uncertainty-treatment-and-optimisation-in-aerospace-engineering_41808.php