We are facing the 4th Industrial Revolution revolving around IoT, Edge Device and Machine Learning applications. While IoT is now part of our daily environment, these paradigms, combined together, open the door to a handful of new possibilities for predictive maintenance. They make this possible by enabling the Edge to “talk” and send real-time data.

Since predictive maintenance is aimed at finding the right balance between scheduled maintenance and curative maintenance, it requires the use of machine learning (ML) based solutions to explore and exploit the data generated. Innovative solutions are required which go beyond the current predictive maintenance systems by exploiting Artificial Intelligence techniques. This need to go beyond can be seen in the case of supervision systems where every new failure risk may not be predictable beforehand but with the use of machine learning, the decision process can be made more reactive to failures and more robust against attacks.

As this research area is still new, many scientific barriers need to be overcome and different challenges need to be addressed ranging from the data acquisition to the type of machine learning solution applied. Therefore, this workshop aims to bring together researchers, practitioners, and industry experts to discuss and explore the latest developments, methodologies, and applications of Artificial Intelligence techniques in predictive maintenance and IIoT. The primary goals of the workshop are to foster collaboration, exchange ideas, and promote advancements in this rapidly evolving field.


Topics include but not limited to:

- Data: acquisition & preprocessing, sensor fusion & data integration, benchmarks & datasets, simulations & digital twins
- Features: extraction, selection
- Targets: anomaly detection, fault diagnosis, fault prediction, root cause analysis, recovery protocols design, data privacy protection, knowledge capitalization
- Methods: deep learning, generative methods, explainability & interpretability, transfer learning, domain adaptation, real time algorithms, optimization, evolutionary algorithms, open-world machine learning, continual learning, symbolic AI, graph-based architectures (knowledge graphs, Graph neural networks, ...)
- Edge computing: tiny ML, distributed architectures (federated learning, distributed learning, multi-agent system

These topics provide a comprehensive coverage of the technical challenges and advancements in machine learning for predictive maintenance and IIoT. They offer opportunities for researchers and practitioners to discuss their work, share insights, and collaborate on solving real-world maintenance problems.

Submission procedure

Please see this page for the submission instructions.
https://aiccsa-wsai4pmi1.gitlab.io/website/

Organizing Committee :

- Guillaume MULLER, École des Mines de Saint-Étienne, France
- Anaïs Lavorel, Université Claude Bernard Lyon 1, France
- Kamal Singh, Université de Saint-Étienne, France