Events

Tutorial:
Machine Learning for Industrial Condition Monitoring – how to?

Condition monitoring (CM) of components and processes using machine learning (ML) is one of the central promises of Industry 4.0. Many successful examples have been demonstrated under laboratory conditions. However, the transfer to actual industrial application is proving difficult. The main challenge remaining is the data quality required for developing a meaningful and robust ML model: in industrial applications, most data represent the “good” condition, while samples for different fault scenarios are typically scarce. Furthermore, comprehensive training data are required covering all relevant circumstances to allow successful CM under changing environmental conditions and other causes of domain shift. Even if extensive data are available, most effort is spent on their organization to delete outliers, ensure correct labeling etc.

The tutorial will address these issues with two main approaches. The first is a checklist to guide users through the complete process of an ML project, starting with project, measurement, and data planning proceeding to data acquisition, checking and pre-processing up to finally building and validating the ML model. This checklist specifically supports users with little experience in ML to be successful. The second approach is classical process optimization based on insights gained using explainable machine learning methods.

Presenters:

Tizian Schneider, Centre for Mechatronics and Automation Technology

Andreas Schütze, Lab for Measurement Technology, Saarland University

Prof. Andreas Schütze has been invited to give a keynote presentation at

The International Symposium on Olfaction and Electronic Nose (ISOEN) is the world’s premiere technical conference in artificial chemoreception, olfaction and taste.
ISOEN 2024 provides a forum for scientists, engineers and practitioners to share their latest findings, innovations and products in the area of artificial chemoreception.

ISOEN 2024 will be held in Grapevine, Texas, a town right next to the Dallas Forth-Worth (DFW) International Airport, Texas, USA.

The Call for Papers is open until January 15, 2024.
Topics of interest:

• Odor sampling: headspace analysis, dynamic sampling, pre-concentration and storage
• New detection principles and materials for sensors for gases, odors and liquids
• Sensors with multiple responses (known as multivariable or multiparameter sensors, virtual sensor arrays)
• Odor and taste analysis devices, including Electronic Noses, Electronic Tongues
• New data analytics (machine learning) for immunity to interferences, improved stability of baseline and sensitivity
• Applications: medical, industrial, environmental, air quality, and food safety
• IoT and robotic systems with chemical and biological sensors
• Odor and gustatory perception and olfactory display
• Bioengineering: cell-based olfactory sensors, receptor-based sensors, bioinspired algorithms

Die Universitätsgesellschaft des Saarlandes zeichnet am Donnerstag, dem 19. Oktober, ab 18 Uhr in der Aula (A3 3) auf dem Campus Saarbrücken vierzehn herausragende Doktorarbeiten des vergangenen Studienjahres mit Dr.-Eduard-Martin-Preisen aus. Ihre Forschung stellen die Preisträgerinnen und Preisträger in kurzen Vorträgen vor. Interessierte können die Preisverleihung vor Ort oder online mitverfolgen. 

Der LMT stellt mit Dr.-Ing. Tobias Baur (links) zum zweiten Mal nacheinander (2022: Dr.-Ing. Caroline Schultealbert, rechts) und insgesamt bereits zum vierten Mal einen Dr.-Eduard-Martin-Preisträger - herzlichen Glückwunsch! (Foto: Oliver Dietze)

Pressemitteilung der UdS mit Kurzportraits dreier Preisträger:innen, u.a. von Tobias Baur..

Um Anmeldung wird gebeten an: gradus(at)uni-saarland.de
Wer online teilnehmen möchte, findet am 19. Oktober ab 18 Uhr den Livestream unter diesem Link (MS Teams).