Projecte llegit
Títol: Utilización de drones para el mantenimiento predictivo en instalaciones de frío industrial según la norma F-gas
Director/a: CASTAN PONZ, JOSÉ ANTONIO
Departament: DEGD
Títol: Utilización de drones para el mantenimiento predictivo en instalaciones de frío industrial según la norma F-gas
Data inici oferta: 05-06-2020 Data finalització oferta: 05-02-2021
Estudis d'assignació del projecte:
GR ENG SIS TELECOMUN- GR ENG SIST AEROESP
- GR ENG TELEMÀTICA
| Tipus: Individual | |
| Lloc de realització: EETAC | |
| Paraules clau: | |
| Dron, Inspección, Mantenimiento, Fuga, Gas, Fluorado, Predictivo | |
| Descripció del contingut i pla d'activitats: | |
| Overview (resum en anglès): | |
| Controlling environmental characteristics (temperature, humidity¿) is essential for an industrial refrigeration unit to keep the interior of its chamber in optimal conditions. This is achieved through the refrigeration cycle. A failure in one part of the cooling system can have serious financial consequences and a negative impact on safety and the environment.
This work aims to improve the probability of fault detection in an industrial chiller through predictive maintenance with the help of a drone. In order to do this, a Modal Failure and Effects Analysis (FMEA) has been carried out. The results obtained have made it possible to identify the most appropriate techniques for monitoring the chamber parameters. Based on these techniques and parameters, a selection of sensors has been made that should be incorporated into the drone. The use of a drone does have limitations regarding the sensor model. First, the sensors must be mountable on the drone. In addition, the payload that the drone supports must be taken into account, since it plays an important role in flight autonomy. Nevertheless, the use of drones to carry out inspections in an industrial refrigeration unit does have a number of advantages from which other industrial sectors that have implemented drone inspections already benefit. Benefits include reduced exposure to unnecessary risks and significantly improved data acquisition and analysis. Additionally, implementing predictive maintenance can significantly reduce refrigeration system downtime, with all that that implies. |
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