Machine learning techniques to evaluate the performance of drilling machines and supporting predictive maintenance through IoT
Thesis in external company
keywords INDUSTRY 4.0, MACHINE LEARNING, AI, IOT, MACHINE LEARNING, MAINTENANCE PROCEDURES
Reference persons ALESSANDRO RIZZO
External reference persons Dr. Elia Abdo, Drillmec S.p.A.
Thesis type APPLIED, EXPERIMENTAL, INDUSTRIAL, INTERNSHIP
Description The thesis will explore machine learning techniques to evaluate the performance of drilling machines and supporting predictive maintenance through IoT. The student is expected to move at the industry sit in Gariga di Podenzano (Piacenza). A monthly reimbursement will be allocated. The selection will be performed by the company.
Deadline 04/07/2021 PROPONI LA TUA CANDIDATURA