Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment

Job description: Power converters are essential in numerous applications such as industry, photovoltaic systems, electric vehicles, and data centers. Their conventional maintenance is often based on fixed schedules, leading to premature replacement of components and significant electronic waste. This PhD project aims to develop a novel non-invasive and low-cost ultrasound-based monitoring approach to assess the state of health and remaining useful life (RUL) of power converters deployed across various industries. The research will focus on identifying and characterizing ultrasonic signatures emitted by aging electronic components, and on developing physics-informed neural networks (PINNs) to model their degradation mechanisms. The project will combine experimental studies with advanced signal processing and AI techniques (compressed sensing), aiming to detect early signs of failure and enable predictive maintenance strategies executed locally (edge deployment). The research will be carried out within a Marie Sklodowska-Curie Actions (MSCA) Doctoral Network, offering international training, interdisciplinary collaboration, and secondments at leading academic and industrial partners across Europe (Italy and Netherlands for this PhD offer).

Your profile: Master’s degree in signal processing, applied physics, electrical/electronic engineering, or data science with an interest in physical systems.