Enhancing micro factory efficiency through digital twin-based predictive maintenance
Collesi, Giacomo (A.A. 2023/2024) Enhancing micro factory efficiency through digital twin-based predictive maintenance. Tesi di Laurea in International operations and supply chain, Luiss Guido Carli, relatore Federica Morandi, pp. 74. [Master's Degree Thesis]
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Abstract/Index
Industry 4.0. Unplanned downtimes. Digital twins. Research gap. Background. Existing predictive models. DT real-world mirror. Integration of sensors data. Different PdM strategies. PdM for micro factories. PdM algorithms. Methods. Resources and tools. Sensors’ data and measurement system. Overview of the dataset. Data cleaning. Data exploration. Principal component analysis (PCA). Statistical models. Applications in a micro factory business model. DT/ M-F/ PdM limitations.
References
Bibliografia: pp. 70-73.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | International operations and supply chain |
| Thesis Supervisor: | Morandi, Federica |
| Thesis Co-Supervisor: | Bontadini, Filippo |
| Academic Year: | 2023/2024 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 09 Jan 2025 08:26 |
| Last Modified: | 09 Jan 2025 08:26 |
| URI: | https://tesi.luiss.it/id/eprint/40828 |
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