As part of the in-depth study weeks offered to second-year engineering students at the Arts et Métiers campus in Bordeaux-Talence, a week-long program on Artificial Intelligence (AI) was organized in collaboration with ESI Group.ESI Group.
Thanks to the company's feedback, students were able to better understand the challenges involved in deploying artificial intelligence techniques in today's industry, particularly the crucial issue of data collection and processing for efficient use.
For this week of in-depth study, engineering students had to meet a challenge: make a 3-minute video to present an aspect of AI in industry.
To conclude the week, a conference on "Data Analytics deployed in factories. Beyond theory: operational feedback" was also organized on campus. This conference gave students a better understanding of approaches such as predictive maintenance and resource consumption optimization through three concepts:
- What is industrial data?
- What are the use cases deployed?
- How should this type of project be approached?
Although they are key technologies in Industry 4.0 projects, Artificial Intelligence, Machine Learning, and Predictive Maintenance are all buzzwords that are often abstract and confusing to manufacturers, which slows down their deployment.
(Henri Pais, Data Analytics Product Manager - ESI Group)
The Mineset solution, developed by ESI Group and designed for manufacturers, uses visualization and analysis tools to intuitively explore data from industrial production lines in order to extract correlations and causal relationships. This approach is fully in line with the goal of improving production line performance (reducing waste, predicting machine downtime, optimizing energy consumption, etc.).
