| KEYWORD |
Machine learning techniques to evaluate the performance of drilling machines and supporting predictive maintenance through IoT
Tesi esterna in azienda
Parole chiave INDUSTRY 4.0, MACHINE LEARNING, AI, IOT, MACHINE LEARNING, MAINTENANCE PROCEDURES
Riferimenti ALESSANDRO RIZZO
Riferimenti esterni Dr. Elia Abdo, Drillmec S.p.A.
Tipo tesi APPLIED, EXPERIMENTAL, INDUSTRIAL, INTERNSHIP
Descrizione 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.
Scadenza validita proposta 04/07/2021
PROPONI LA TUA CANDIDATURA