Predictive Maintenance with C-MAPSS

As Artificial Intelligence (AI) becomes increasingly woven into our daily lives, the need to understand how and why these systems make decisions has never been more important.

This intensive half-day course provides a practical, hands-on introduction to Predictive Maintenance using multivariate time-series data. Utilizing the industry-standard NASA C-MAPSS dataset, participants will learn the end-to-end process of identifying equipment degradation, predicting Remaining Useful Life (RUL), and explaining machine learning decisions to domain experts.

Key Learning Objectives

• Degradation Detection: Understand run-to-failure trajectories, analyze sensor signals across various operating conditions, and identify anomalous behavior in complex machinery.
  
• Remaining Useful Life (RUL) Prediction: Learn how to construct an RUL target from time-series data, apply feature- and window-based modeling techniques, and evaluate model performance and error rates.
  
• Explainable AI (XAI) in Maintenance: Use SHAP and feature-attribution techniques to interpret model outputs, determining exactly which sensor characteristics drive specific RUL predictions across healthy, degrading, and near-failure states.
  

Agenda & Content

For a detailed timetable and additional information, please see Agenda & Content in the left menu.

Target audience, eligibility & prices

AI practitioners, data scientists, engineers, researchers, and students interested in predictive maintenance, time-series analysis, and trustworthy machine learning. The course is especially relevant for participants working with industrial data or looking to apply AI to real-world maintenance problems.

This course is open and free of charge for all participants from academia, industry, and public administration from EU and/or EuroHPC JU member countries.

Entry level & prerequisites

Basic knowledge of machine learning and Python is recommended. Familiarity with supervised learning concepts is helpful, but no prior experience with predictive maintenance or explainable AI is required.

Course format

This course will be delivered as a LIVE ONLINE COURSE (Zoom).

Hands-on labs

All participants will get a temporary user account on one of the ASC systems to do the hands-on labs.

You will use your own laptop or workstation to connect conveniently from your browser to the ASC Jupyterhub and do the hands-on exercises on a suitable CPU or GPU partition of the ASC.

Accepted participants will be contacted a few days before the course and asked to do a short pre-assignment that has to be completed before the course starts.

Lecturer

Anahid Wachsenegger (AIT Austrian Institute of Technology GmbH)                    
Anahid Wachsenegger is a data scientist at the AIT Austrian Institute of Technology, specializing in artificial intelligence, explainable machine learning, data-driven modeling, and time-series analysis across domains such as forestry and mobility data science. She holds a Master’s degree in Computational Intelligence from TU Wien and also served as an associate lecturer in Media and Digital Technologies at the University of Applied Sciences St. Pölten. She is passionate about developing trustworthy, transparent AI systems, applying data science to real-world challenges, and supporting students and practitioners in understanding and responsibly using modern AI technologies.

Language

English

Date, time, and location

11.11.2026, 13:00 – 16:30 (CET), LIVE ONLINE COURSE (Zoom)

Registration

Registration is required, the registration form can be found on top of this page in the menu on the left.

Please register with your official institutional email address to prove your affiliation.

You will get an automatic confirmation by email (subject starting with "[Indico] Registration"), please check your Spam/Junk folders.

Following your successful registration, you will receive further information a few days before the course.

Please do not hesitate to contact us at training@ai-at.eu if you have any questions.

Waitinglist

After the number of registrations has reached its maximum or the registration form has been closed, you may want to send us an email (training@ai-at.eu) stating that you are interested to be put on the waiting list (vacancies may occur due to cancelations, etc.).

To be able to do the hands-on labs on the ASC systems please provide your full international mobile-phone number for the two-factor authentication required to login to the ASC systems.

Modification, withdrawal & no-show policy

Your registration is binding. Please only register for the course if you are really going to attend.

You can update your registration data or withdraw your registration anytime before the registration form has been closed via the link "Manage my registration" which you can find at the bottom of your automatic email confirmation (subject starting with "[Indico] Registration").

Alternatively, or after the registration form has been closed, please inform us about your cancelation or any change in your registration data (especially your mobile-phone number) via email (training@ai-at.eu).

No-show policy: If you do not cancel and do not show up at the course you will be blacklisted and excluded from future training events.

Organizers

This course is jointly organized by AI Factory Austria AI:AT and Austrian Scientific Computing ASC (aka ASC Research Center, TU Wien).

Acknowledgements

AI Factory Austria AI:AT has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101253078. The JU receives support from the Horizon Europe Programm of the European Union and Austria (BMIMI / FFG).