Technical description 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.

Organizing Committee Guillaume MULLER, École des Mines de Saint-Étienne, France (guillaume dot muller at emse dot fr) Anaïs Lavorel, Université Claude Bernard Lyon 1, France (anais dot lavorel at univ-lyon1 dot fr) Kamal Singh, Université de Saint-Étienne, France (kamal dot singh at univ-st-etienne dot fr) Technical Program Committee: Kandaraj Piamrat, Université de Nantes, France Ons Aouedi, Université de Nantes, France Priyanka Rawat, Université de Saint-Étienne, France Xavier Olive, ONERA, France Nabil Benamar, Moulay Ismail University, Meknes, Morocco Houda Chihi, Sup’COM, Tunisia

Program The program of the Workshop will be added when the list of accepted papers will be finalized.