We are excited to announce the continuation of the successful collaboration between HPC.NRW and MathWorks for the third consecutive year, offering a comprehensive series of four workshops designed to empower researchers, engineers, and scientists. This unique series aims to enhance skills in high-performance computing (HPC), software development for research, and artificial intelligence (AI) with MATLAB.

Artificial intelligence (AI) is rapidly becoming a critical component of many engineering systems and disciplines today. In the field of predictive maintenance, AI is being used to design and develop smarter ways to perform anomaly detection, identify faults, and estimate remaining useful life of machines. In this forth part of the Parallel Computing with MATLAB series you will write and execute code examples in MATLAB Online – entirely in the browser – to learn and explore how to apply principles of AI to predictive maintenance: machine learning, deep learning, feature extraction, and domain-specific data processing.

Workshop Series

1. Parallel Computing with MATLAB
2. Parallel Computing with MATLAB on the CLAIX Cluster
3. Introduction to Research Software Development with MATLAB
4. AI for Predictive Maintenance (this course)

Organization

• There is no seminar fee.
• Presentations will be given in English. Slides will be available after the event.
• This is an online event and will be held in Zoom. Links are sent to registered attendees with the registration confirmation.
• You can/must register per topic, i.e., for each workshop separately.

Speaker

• Dr. Kathi Kugler (MathWorks)

Course level

• Beginner to Intermediate

Target audience

• HPC/AI users
• HPC/AI developers
• Users interested in predictive maintenance

Prerequisites

• Basic MATLAB familiarity (If you feel insecure, we recommend completing the self-pace online course MATLAB Onramp (ca. 2 hours) before the workshop)
• Your MathWorks-Account

Gained skills

• • Familiarizing yourself with MATLAB Online and AI tools with an introductory example that trains a machine learning model to classify faults.
  • Exploring how to extract features in the time and frequency domains, and rank them to obtain the most relevant features to train your AI model
  • Diving deep into an advanced, predictive maintenance workflow that covers anomaly detection and remaining useful life estimation.

Registration

Jetzt anmelden »

Course Material

• 4_-AI-for-Predictive-Maintenance-HPC-NRW-Handout.pdf
• Exercise material on Github

Cooperation Partners