Two themes in collaboration with Pietro Fiorentini SpA

1. Measuring systems are becoming more and more sophisticated to tackle the challenges of modern industrial problems. In particular, may complex metrology systems combine different sensors and data fusion techniques to estimate quantities that are difficult to be measured. While complexity is increasing, demands for providing confidence levels in the provided measure are becoming more and more popular in many sectors. Multiphase Flow Meters (MPFM) are important metering tools in the oil and gas industry. A MPFM provides real-time measurements of gas, oil and water flows of a well without the need to separate the phases. Despite the harsh environment in which the MPFM is placed, the reliability requirements are similar to satellites and airplanes.In this thesis we propose to develop and implement Machine Learning tools for the detection of anomalies and for the fault isolation. Both supervised and unsupervised techniques can be employed, depending on the student interests. 
2. Smart meters are revolutionizing the gas industry allowing access to valuable data on consumption and on distributed devices. In this thesis, we will investigate how to extract value from such information by exploiting Machine Learning approaches.

Contacts: Gian Antonio Susto gianantonio.susto@unipd.it

(Edited by Friso Simone - original submission Sunday, 17 May 2020, 9:52 PM)