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Condition-based Maintenance with Reinforcement Learning for Refrigeration Systems with Selected Monitored Features by de Lima Munguba, Caio Filipe (author);de Novaes Pires Leite, Gustavo (author);Ochoa, Alvaro Antonio Villa (author);Lopez Droguett, Enrique (author) is a Computer Science article available to read on EtoBox.

What is Condition-based Maintenance with Reinforcement Learning for Refrigeration Systems with Selected Monitored Features about?

Worldwide, buildings are responsible for almost 30% of energy consumption, and those buildings that intensively use refrigeration systems, such as supermarkets and grocery stores, are also among the most energy-intensive consumers. Refrigeration devices, either commercial or residential, are responsible for a significant part of net emissions. Based on careful measurements, it is possible to reduce energy consumption in these devices by up to 15% only by improving the fault detection and diagnosis techniques. Thus, improving maintenance programs has become a crucial area in energy management in recent years. Nowadays, the market has experienced a hike after smart systems and new network interfaces applied to smart buildings that have allowed previously isolated devices to become smart devices, interacting with control algorithms smartly and, to some extent, autonomously. Here, we propose a reinforcement learning framework to develop a maintenance policy for mechanical compression refrigeration devices. Firstly, a test bench is built in which each component is assigned to be individually repairable and individually degradable in parallel and interconnected processes. Then, the degra

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Author

de Lima Munguba, Caio Filipe (author);de Novaes Pires Leite, Gustavo (author);Ochoa, Alvaro Antonio Villa (author);Lopez Droguett, Enrique (author)

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)