After a 1.5 year experience at McKinsey and Co. as a business analyst consultant, I have obtained a PhD in Mechanical Engineering at Politecnico di Milano (Italy, 2013) with a thesis on “Tools for the automated condition monitoring of rotating machines”. During my PhD and in a brief post-doctoral activity at Politecnico di Milano I have worked on a series of industrial projects related to the health monitoring and system identification for a wide range of mechanical systems, including bridges, large rotating machines, road and rail vehicles and industrial coolers.
His research activity has continued at QUT since from December 2013 until the end of 2017. In particular, I have conducted research in collaboration with Australian companies (e.g. Delta Electricity, Macquarie Generation, Powerlink and Wilmar Sugar) and government agencies (e.g. Australian Department of Education and Training and ARENA) for the optimisation of maintenance strategies based on the estimation and prediction of critical asset condition.
I am an Editorial Board Member for Mechanical Systems and Signal Processing (MSSP) and a Member of the International Society for Engineering Asset Management (ISEAM).
My main research area at UNSW is in the fields of Health Monitoring and Health Management, with a particular interest in stochastic signal processing, time-frequency analysis, cyclostationary analysis, rotor-dynamics and vibration mitigation, acquisition systems and vibration/acoustic sensors, machine learning for prognostics, reliability analysis, and maintenance optimisation.
I am an Editorial Board Member for Mechanical Systems and Signal Processing (MSSP), a Member of the International Society for Engineering Asset Management (ISEAM) and CI in three main research projects:
- an ARC Discovery on gear diagnostics and prognostics, aimed at obtaining a model-based residual-life estimation of gears based on vibration and acoustic emission sensors,
- an ARC Linkage aimed at the optimisation of maintenance for sugar mills, and
- an ASTRI project (funded by ARENA) aimed at the condition-based optimisation of maintenance in concentrated solar power plants.