| KEYWORD |
Gearbox failure modes, damage detection and classification: statistical models and machine learning standpoint
Thesis in external company
keywords DAMAGE, FAILURE PROBABILITY, FATIGUE, MACHINE LEARNING, ROBUST DESIGN, SYSTEM RELIABILITY, VIBRATION DIAGNOSTICS
Reference persons MAURIZIO GALETTO, GIANFRANCO GENTA
External reference persons Fabio Petrarulo (Dana Graziano Srl).
Research Groups Ingegneria della qualità
Thesis type EXPEIRMENTAL, IN COMPANY, SIMULATION
Description Proofs of system reliability for complex mechanical systems like gearboxes are in increasing demand in various sectors of the power transmission industry. The objective of the thesis is the study and implementation of statistical models to determine the reliability of the overall system based on the failure probability of the individual components, leveraging data analysis to "calibrate" the virtual models for predictive purposes, considering also Machine Learning techniques.
See also https://www.dana.com/
Required skills Fundamentals of mechanical design. Basics of vibration analysis. Basics of statistics.
Notes The thesis work will be carried out mainly at the company Dana Graziano Srl based in Rivoli (TO).
An expense reimbursement is expected.
Deadline 10/10/2024
PROPONI LA TUA CANDIDATURA