MONTAGNES, M. Baptiste (2024) Surveillance de la santé des machines tournantes PRE - Research Project, ENSTA.

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Abstract

Machine health monitoring refers to techniques for assessing the condition of rotating machinery. The goal is to extend the lifespan of components and reduce maintenance costs by implementing predictive maintenance. Deep Learning emerges as an effective method for extracting useful representations from the vibrational signals of bearings. These representations are reused in classification tasks to detect defaults and to estimate the remaining useful life using statistical models. The objective of this internship is to explore selfsupervised learning methods and determine if they can balance out for the lack of labeled data by outperforming supervised models. Moreover, this internship aims to establish a viable method for predicting the remaining useful life of a bearing.

Item Type:Thesis (PRE - Research Project)
Uncontrolled Keywords:rotating machinery, predictive maintenance, vibrational signals, self-supervised learning, transfer learning, remaining useful life
Subjects:Information and Communication Sciences and Technologies
ID Code:10085
Deposited By:Baptiste MONTAGNES
Deposited On:03 sept. 2024 10:07
Dernière modification:03 sept. 2024 10:07

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