Ariel Priarone
Unsupervised Machine Learning Algorithms for Edge Novelty Detection.
Rel. Marcello Chiaberge, Umberto Albertin, Gianluca Dara. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2024
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (77MB) | Preview |
Abstract
Predictive Maintenance and Novelty Detection are important topics in modern industrial engineering, aimed at proactively identifying equipment failures before they affect system functionality. Embracing these practices is crucial for reducing equipment downtime and optimizing maintenance efforts. Predictive Maintenance aims to quantify and forecast the state of degradation of a system. A quite novel frontier is the direct implementation of Predictive Maintenance within the maintained device, using the principles of Edge Computing. The fourth industrial revolution is characterized by the integration of Artificial Intelligence and the Internet of Things paradigm into factories. Nowadays, more than a decade since the beginning of this industrial revolution, the maintenance approach remained unchanged in most industrial applications.
The primary factor impeding the advancement of the maintenance approach is the significant expense associated with implementing Condition-Based or Predictive maintenance strategies, coupled with a lack of knowledge about the modelling or behaviour of a failing system
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modifica (riservato agli operatori) |
