Nowadays, the new paradigm of Industry 4.0 is spreading at an increasing rate in the industrial world, entailing an equally growing adoption of technologies based on communication via Internet. With Industry 4.0, the process of technological innovation has begun to spread even in maintenance, leading to a shift towards the new concept of smart maintenance. There are many fields of research on the use of the enabling technologies of Industry 4.0 for maintenance. In this thesis, the interest concerned the use of cloud, fog and edge computing technologies for Condition-Based Maintenance (CBM). The CBM in fact, involving the extraction and analysis of information relating to the condition of the assets, is strictly dependent on the technology. At the same time, cloud computing, by harnessing the power of the Internet, allows access to data from anywhere in the world, enabling the remote monitoring of assets. Although the use of cloud computing technology in CBM applications is widespread, minor attention is being given to the use of fog and edge computing technologies in relation to CBM. This thesis, therefore, aims to address the issue of the integration of different computing technologies – i.e. cloud, fog and edge – in CBM applications in order to improve maintenance decision-making. Therefore, this thesis work conceptualizes a framework, based on the concept of operational capability, that can be used as a guiding tool in the configuration of smart maintenance systems, which integrate the different types of computing technologies. Concretely, the proposed framework identifies, develops and proposes criteria for the allocation and implementation of operational capabilities in a smart maintenance system, allowing to configure the smart maintenance system, so as to improve and integrate the decision-making process. Finally, the proposed framework is tested, showing at the same time its feasibility and operational use.
Al giorno d’oggi, il nuovo paradigma dell’Industria 4.0 si sta diffondendo ad un ritmo crescente nel mondo industriale, comportando una altrettanto crescente adozione di tecnologie basate sulla comunicazione via Internet. Con l’Industria 4.0, il processo di innovazione tecnologica ha iniziato a diffondersi anche nel campo della manutenzione, comportando uno spostamento verso il nuovo concetto di manutenzione intelligente. Esistono molti campi di ricerca sull’uso delle tecnologie abilitanti dell’Industria 4.0 per la manutenzione. In questa tesi, l’interesse è rivolto verso l’uso delle tecnologie di cloud, fog e edge computing per la manutenzione basata su condizione (CBM). Il CBM infatti, coinvolgendo l’estrazione e l’analisi delle informazioni relative alla condizione degli asset, è strettamente dipendente dalla tecnologia. Allo stesso tempo, il cloud computing, sfruttando la potenza di Internet, consente l’accesso ai dati da qualsiasi parte del mondo, abilitando così il monitoraggio remoto degli asset. Sebbene sia ampiamente diffuso l’uso della tecnologia di cloud computing nelle applicazioni per il CBM, minore è l’attenzione riservata all’uso delle diverse tecnologie di fog e edge computing in relazione al CBM. Pertanto, questa tesi si propone di risolvere il problema dell’integrazione di diverse tecnologie di computing – i.e. cloud, fog e edge – nelle applicazioni CBM, al fine di migliorare il processo decisionale in materia di manutenzione. Il presente lavoro di tesi concettualizza quindi un framework, basato sul concetto di operational capability, che può essere utilizzato come strumento guida nella configurazione di sistemi di manutenzione intelligenti, che integrano i diversi tipi di tecnologie di computing. Concretamente, il framework proposto identifica, sviluppa e propone criteri per l’allocazione e l’implementazione delle operational capabilities in un sistema di manutenzione intelligente, consentendo di configurare quest’ultimo, in modo da migliorare e integrare il processo decisionale. Infine, il framework proposto viene testato, mostrandone allo stesso tempo la fattibilità e l’uso operativo.
Formalization and test of a capability-based framework for smart and condition-based maintenance
BERARDI, FRANCESCA
2017/2018
Abstract
Nowadays, the new paradigm of Industry 4.0 is spreading at an increasing rate in the industrial world, entailing an equally growing adoption of technologies based on communication via Internet. With Industry 4.0, the process of technological innovation has begun to spread even in maintenance, leading to a shift towards the new concept of smart maintenance. There are many fields of research on the use of the enabling technologies of Industry 4.0 for maintenance. In this thesis, the interest concerned the use of cloud, fog and edge computing technologies for Condition-Based Maintenance (CBM). The CBM in fact, involving the extraction and analysis of information relating to the condition of the assets, is strictly dependent on the technology. At the same time, cloud computing, by harnessing the power of the Internet, allows access to data from anywhere in the world, enabling the remote monitoring of assets. Although the use of cloud computing technology in CBM applications is widespread, minor attention is being given to the use of fog and edge computing technologies in relation to CBM. This thesis, therefore, aims to address the issue of the integration of different computing technologies – i.e. cloud, fog and edge – in CBM applications in order to improve maintenance decision-making. Therefore, this thesis work conceptualizes a framework, based on the concept of operational capability, that can be used as a guiding tool in the configuration of smart maintenance systems, which integrate the different types of computing technologies. Concretely, the proposed framework identifies, develops and proposes criteria for the allocation and implementation of operational capabilities in a smart maintenance system, allowing to configure the smart maintenance system, so as to improve and integrate the decision-making process. Finally, the proposed framework is tested, showing at the same time its feasibility and operational use.| File | Dimensione | Formato | |
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2019_04_Berardi.pdf
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Descrizione: Testo della tesi
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https://hdl.handle.net/10589/145877