This thesis presents the activities performed during an internship at DAB Pumps, where a predictive maintenance system was developed and structured for an automated production line. The project explores the integration of Industry 4.0 technologies to monitor machinery in real-time, preventing equipment failures and optimizing production. Through data analysis and sensor monitoring, the system aims at enhancing operational efficiency and reducing product defects. The thesis details the steps taken to map the production process involved in the case study in order to design and implement a solid system, demonstrating the potential of predictive maintenance in modern manufacturing environments.

This thesis presents the activities performed during an internship at DAB Pumps, where a predictive maintenance system was developed and structured for an automated production line. The project explores the integration of Industry 4.0 technologies to monitor machinery in real-time, preventing equipment failures and optimizing production. Through data analysis and sensor monitoring, the system aims at enhancing operational efficiency and reducing product defects. The thesis details the steps taken to map the production process involved in the case study in order to design and implement a solid system, demonstrating the potential of predictive maintenance in modern manufacturing environments.

Predictive maintenance in smart manufacturing: a Data-Driven approach to sensor implementation and database analysis at DAB Pumps

GARDANO, SILVIA
2023/2024

Abstract

This thesis presents the activities performed during an internship at DAB Pumps, where a predictive maintenance system was developed and structured for an automated production line. The project explores the integration of Industry 4.0 technologies to monitor machinery in real-time, preventing equipment failures and optimizing production. Through data analysis and sensor monitoring, the system aims at enhancing operational efficiency and reducing product defects. The thesis details the steps taken to map the production process involved in the case study in order to design and implement a solid system, demonstrating the potential of predictive maintenance in modern manufacturing environments.
2023
Predictive maintenance in smart manufacturing: a Data-Driven approach to sensor implementation and database analysis at DAB Pumps
This thesis presents the activities performed during an internship at DAB Pumps, where a predictive maintenance system was developed and structured for an automated production line. The project explores the integration of Industry 4.0 technologies to monitor machinery in real-time, preventing equipment failures and optimizing production. Through data analysis and sensor monitoring, the system aims at enhancing operational efficiency and reducing product defects. The thesis details the steps taken to map the production process involved in the case study in order to design and implement a solid system, demonstrating the potential of predictive maintenance in modern manufacturing environments.
machine learning
data-driven
smart manufacturing
predictive
industry 4.0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/74660