Location & approach

via car

The campus of TU Dortmund University is located near the freeway junction Dortmund West, where the Sauerland line A45 crosses the Ruhr expressway B1/A40. The Dortmund-Eichlinghofen exit on the A45 leads to the South Campus, the Dortmund-Dorstfeld exit on the A40 leads to the North Campus. The university is signposted at both exits. It is best to park in the large parking lot on Otto-Hahn-Straße.

via train and bus

The "Dortmund Universität" S-Bahn station is located directly on the North Campus. From there, the S-Bahn line S1 runs every 15 or 30 minutes to Dortmund main station and in the opposite direction to Düsseldorf main station via Bochum, Essen, and Duisburg. In addition, the university can be reached by bus lines 445, 447, and 462. Timetable information can be found on the homepage of the Verkehrsverbund Rhein-Ruhr, and DSW21 also offer an interactive route network map.

via H-Bahn

One of the landmarks of the TU Dortmund University is the H-Bahn. Line 1 runs every 10 minutes between Dortmund Eichlinghofen and the Technology Center via Campus South and Dortmund University S, while Line 2 commutes every 5 minutes between Campus North and Campus South. It covers this distance in two minutes.

via airplane

From Dortmund Airport, the AirportExpress takes just over 20 minutes to Dortmund Central Station and from there to the university by S-Bahn. A wider range of international flight connections is offered by Düsseldorf Airport, about 60 kilometers away, which can be reached directly by S-Bahn from the university's train station.

ServicePortal UniMail

Contrast Deutsch

Location & approach

via car

The campus of TU Dortmund University is located near the freeway junction Dortmund West, where the Sauerland line A45 crosses the Ruhr expressway B1/A40. The Dortmund-Eichlinghofen exit on the A45 leads to the South Campus, the Dortmund-Dorstfeld exit on the A40 leads to the North Campus. The university is signposted at both exits. It is best to park in the large parking lot on Otto-Hahn-Straße.

via train and bus

The "Dortmund Universität" S-Bahn station is located directly on the North Campus. From there, the S-Bahn line S1 runs every 15 or 30 minutes to Dortmund main station and in the opposite direction to Düsseldorf main station via Bochum, Essen, and Duisburg. In addition, the university can be reached by bus lines 445, 447, and 462. Timetable information can be found on the homepage of the Verkehrsverbund Rhein-Ruhr, and DSW21 also offer an interactive route network map.

via H-Bahn

One of the landmarks of the TU Dortmund University is the H-Bahn. Line 1 runs every 10 minutes between Dortmund Eichlinghofen and the Technology Center via Campus South and Dortmund University S, while Line 2 commutes every 5 minutes between Campus North and Campus South. It covers this distance in two minutes.

via airplane

From Dortmund Airport, the AirportExpress takes just over 20 minutes to Dortmund Central Station and from there to the university by S-Bahn. A wider range of international flight connections is offered by Düsseldorf Airport, about 60 kilometers away, which can be reached directly by S-Bahn from the university's train station.

ServicePortal UniMail

Enterprise Computing

Prediction on Industrial Processes through Explainable Artificial Intelligence (pipeAI)

Profile

Call: R&D program "Information and Communication Technology"
Project management: Prof. Dr. Christian Janiesch
Project participants: ROBUR Automation GmbH, SKZ - KFE gGmbH, Julius-Maximilians-Universität Würzburg
Project start : 01.09.2020
Project end: 31.08.2023
Funding volume: 772,000 Euro
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi)

Summary

Machine learning methods have been applied to industrial data for a long time, e.g. to predict machine failures. Their effectiveness depends on the reliability of the predictions and the acceptance by the user. To promote the widespread use of AI tools by AI-inexperienced employees, pipeAI addresses this problem. New AI approaches (e.g. GQNs) are tested in an industrial context, supported by improved methods for data cleaning and automated labeling. Furthermore, with Explainable AI and active participation of the user, e.g. in Transfer Learning, AI tools are made more comprehensible. As an example, the use case Predictive Maintenance is pursued. The resulting implementation is exemplary and generalizable.

© JMU (2020)

pipeAI

© Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (2023)

© VDI/VDE Innovation + Technik GmbH (2023)