Industry 4.0 sensing for machine condition monitoring

Position is funded by – COFUND, Marie Skłodowska-Curie Actions (MSCA), Horizon Europe, European Union.

Offer Description
The Australia France Network of Doctoral Excellence (AUFRANDE) is a highly ambitious interdisciplinary doctoral program linking France and Australia, with a strong support from the industry. AUFRANDE seeks to recruit excellent doctoral researchers of any nationality, gender, and background from around the world. AUFRANDE offers the recruited researchers an outstanding experience with excellent working conditions, including full-time employment contract in France with attractive salaries including social security benefits, a unique international research environment, and an innovative research training program in which they will deepen core scientific skills and develop new ones in complementary disciplines and sectors. International mobility is a core feature of the program with a residential year in Australia and participation in regular events where researchers share common experiences and build a sustainable community, laying a strong foundation for long-term impact on future collaborations and careers.

PhD description
Machine condition monitoring (MCM) is still largely based on the installation of a few expensive accelerometers on few critical machines, acquired by means of expensive and centralised electronics. In a world moving towards fleets of assets (e.g., wind farms, drones for delivery) it is paramount that this approach is replaced by more affordable, scalable and self -sufficient senor technologies. This thesis aims at exploring self-powered, inexpensive sensor network technologies for MCM. Alternatives to traditional piezoelectric accelerometers (e.g., MEMS) will be investigated, both in terms of diagnostic capabilities but also in their suitability for integration with non-invasive, easy- to-install and self-powered data-acquisition systems, able to communicate a sufficient amount of diagnostic information wirelessly in a network of monitored machines. This thesis aims at revolutionising the way diagnostic data is collected, thus enabling the collection of big-data necessary for popular data-driven approaches (artificial intelligence) and the Industry 4.0 transformation.