Rolling bearing damage characterization - A machine learning approach
Milad Rahmani Tootkaboni
Rolling bearing damage characterization - A machine learning approach.
Rel. Alessandro Fasana. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Meccanica (Mechanical Engineering), 2021
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Abstract
Rolling bearing damage characterization - A machine learning approach This thesis is done based on the article published on Mechanical Systems and Signal Processing journal . Article name: The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data. Article authors: Alessandro Paolo Daga, Alessandro Fasana, Stefano Marchesiello, Luigi Garibaldi . The research has been taken place and the test has been conducted in the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The main article by PoliTo works on two main different tests, Variable Speed and Load test and Endurance test.
However, on this thesis only the data of Variable Speed and Load test is used
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