Diagnóstico de fallas por rotura de barras en motores de inducción trifásicos mediante el uso de modelos de deep learning
Three-phase induction motors are used in various industrial applications due to their efficiency and reliability. However, rotor failures such as a broken rotor bar can affect their performance, causing electromagnetic instability, vibrations, and loss of energy efficiency, leading to additional mai...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/153873 |
| Acceso en línea: | https://hdl.handle.net/10609/153873 |
| Access Level: | acceso abierto |
| Palabra clave: | aprendizaje automático aprendizaje profundo motores de inducción detección de fallas mantenimiento predictivo barras rotas machine learning deep learning induction motors fault detection predictive maintenance broken bars Deep learning -- TFM Aprenentatge profund -- TFM |
| Sumario: | Three-phase induction motors are used in various industrial applications due to their efficiency and reliability. However, rotor failures such as a broken rotor bar can affect their performance, causing electromagnetic instability, vibrations, and loss of energy efficiency, leading to additional maintenance costs and losses in industrial production. Early detection of these failures allows predictive maintenance strategies to be implemented to reduce costs and avoid unplanned interruptions in industrial systems. This work proposes the development of a deep learning-based classification model for the automatic detection of broken rotor bar faults in three-phase induction motors. The aim is to identify the number of broken bars from the analysis of current signals based on the angular position of the rotor, using a non-invasive method that combines current monitoring with automatic detection techniques. The methodology includes experimental data acquisition, current signal preprocessing, feature extraction, and training of deep neural network models. The model architecture is designed to improve its generalization capacity and performance in detecting faults in motors with different rotor bar configurations. Experimental data obtained in collaboration with the Universitat Politècnica de València will be used, which will allow the evaluation of the model and its capacity to recognize characteristic patterns of these faults. The system starts from signal acquisition → codification → classification → results visualization in the GUI. |
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