Digital-twins and artificial intelligence for robust machine condition monitoring
Position is funded by – COFUND, Marie Skłodowska-Curie Actions (MSCA), Horizon Europe, European Union.
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.
Artificial intelligence (AI) has attracted immense interest in machine condition monitoring (MCM). The enthusiasm of researchers in this relatively new approach stems from its proven value in other image and signal processing applications, like computer-vision and speech recognition. Recent works have however highlighted the key difference of MCM from these more traditional AI applications: the scarcity of fault data. In MCM-intensive engineering applications, failure is often very expensive and prevented by strict maintenance procedures, resulting in just a handful of failure observations. Another complementary criticism that has been made to AI approaches to MCM is that they neglect decades of accumulated knowledge in degradation dynamics and machine reliability. A solution to these problems is offered by the combination of AI methods with digital twins.
The latter can produce large amount of data in any condition and codify knowledge about the machine behaviour. Yet, the portability of AI solutions developed in simulated environments to the real-world is still to be proven. A clear picture of the digital-twin characteristics which ensure this approach is successful is yet to be studied, and methods to combine scarce, yet valuable, experimental data with simulations have to be developed for MCM.