Physically-driven design of health indicators for diagnosis and prognosis

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

Offer Description
MSCA Doctoral Networks will implement doctoral programmes, by partnerships of universities, research institutions and research infrastructures, businesses including SMEs, and other socio-economic actors from different countries across Europe and beyond. MSCA Doctoral Networks are indeed open to the participation of organisations from third countries, in view of fostering strategic international partnerships for the training and exchange of researchers. These doctoral programmes will respond to well-identified needs in various R&I areas, expose the researchers to the academic and non-academic sectors, and offer training in research-related, as well as transferable skills and competences relevant for innovation and long-term employability (e.g. entrepreneurship, commercialisation of results, Intellectual Property Rights, communication). Proposals for doctoral networks can reflect existing or planned research partnerships among the participating organisations.

The PhD project is about the design of novel Health Indicators (HIs), considering explicitly the physics of degradation. HIs are fundamental quantities at the basis of current diagnosis and prognosis methodologies of machines (e.g. windturbines, turbomachines, etc.). Despite remarkable progresses in health monitoring boosted by new technologies (IoT, new sensors) and AI, most approaches still rely on the use of rudimentary HIs defined more than half a century ago, when the main motivation was to provide metrics that could be easily calculated with the low computational resources of that time. Many popular HIs have traded simplicity against physical relevance and, as a consequence, it turns out difficult to tailor them to monitor specific degradation processes. Rather paradoxically, HIs with limited informational content are used as the inputs of extremely sophisticated machine learning algorithms (such as regressors and classifiers), yet constituting the weakest link of the chain. The project will address the construction of a mathematical mapping from physical multidimensional quantities such as surface topology and local mechanical properties to a scalar metric that can be calculated from the measurement of the dynamic behavior of the structure. Models of tribology will be used to correlate the dynamic response of a structure to local properties of damaged surfaces of contact (gears, rolling element bearings). Fracture dynamics and fatigue models will be considered to construct metrics of damage. The methodologies will be tested, evaluated and validated experimentally using a testbench for fatigue analysis of rolling element bearings and tribometers.