Tutor: CAPONE ANTONIO Abstract:
Coherent Synoptic Fiber-Optic Sensor
Predictive diagnostics is a key technology in the manufacturing and process industry. Existing commercial sensing systems are mainly based on conventional electrical sensor devices such as accelerometers, vibrometers, strain-gauges and thermocouples, each of which can provide local information for the evaluation of a single specific parameter: vibration, strain or temperature. The photonic technology can instead establish an innovative and alternative methodology of sensing, enabling an all-comprehensive integral sensing device able to detect and provide multi-parameter information of the entire monitored machine/structure.
This PhD research activity will be focused on the proposal of a novel sensing approach, which exploits the well known sensing capabilities of optical fibres to mechanical, thermal and even magnetic perturbations, combined with advanced processing techniques borrowed from high-bit rate optical communications. The key idea is to develop a ¿coherent synoptic sensor¿, where the entire length of a standard optical fibre is used as the sensing element of the whole machine/structure, providing a distributed sensing functionality. The synoptic sensor can simultaneously collect multi-domain variables including vibrations of mechanical components or caused by pipeline leakages (up to hundreds of kHz), structural deformations, temperatures, magnetic fields and even concentration of chemical species. The exploitation of a coherent phase and polarization-diversity detection and advanced high-speed digital signal processing allows all the information collected along the entire fibre length to be properly separated and decoded by the synoptic sensor.
The main goal of this PhD thesis will be the analysis and implementation of proper fiber layout deployments and/or embedding in the machine tools to be monitored, combined to the development of suitable DSP algorithms in order to exploit the great potential of the synoptic sensor for predictive and diagnostic monitoring purposes in various industrial fields such as mechanical, chemical and food manufacturing.
Advisor: Mario Martinelli