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Machine monitoring for predictive maintenance

keywords IMAGE PROCESSING, INDUSTRY 4.0, MACHINE LEARNING, AI, PREDICTIVE MAINTENANCE, SENSOR FUSION, SIGNAL PROCESSING

Reference persons GIULIA BRUNO, FRANCO LOMBARDI

External reference persons emiliano.traini@polito.it

Research Groups Gestione della conoscenza nello sviluppo prodotto/processo

Thesis type SPERIMENTALE

Description In the Industry 4.0 era, Internet of Things (IoT) and Artificial Intelligence (AI) are transforming the manufacturing paradigm. By means of IoT, manufacturing systems are more and more able to monitor the physical processes through the real-time communication among sensors and interface devices. By means of AI and Machine Learning, manufacturing systems can develop new abilities to dig into the knowledge encompassed within the huge amount of data available from the IoT and make smart decisions in real time. Example of affordable scopes are to reduce equipment downtimes, detect or prevent production defects, minimize work-in-process stocks, improve the cooperation between machines and humans, and improve the efficiency of the whole supply chain. 

The thesis work aims to develop a system monitoring tool to combine Industrial IoT technologies with Machine Learning technologies to forecast the exact time in which each manufacturing equipment will need maintenance.  Thus, recovering plans and decisions can be formulated on time, so that unexpected events will be avoided.

Required skills Programming; Basics on machine learning.


Deadline 01/10/2020      PROPONI LA TUA CANDIDATURA




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