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- Title
A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography.
- Authors
Trejo-Chavez, Omar; Cruz-Albarran, Irving A.; Resendiz-Ochoa, Emmanuel; Salinas-Aguilar, Alejandro; Morales-Hernandez, Luis A.; Basurto-Hurtado, Jesus A.; Perez-Ramirez, Carlos A.
- Abstract
Infrared thermography (IRT) has become an interesting alternative for performing condition assessments of different types of induction motor (IM)-based equipment when it operates under harsh conditions. The reported results from state-of-the-art articles that have analyzed thermal images do not consider (1): the presence of more than one fault, and (2) the inevitable noise-corruption the images suffer. Bearing in mind these reasons, this paper presents a convolutional neural network (CNN)-based methodology that is specifically designed to deal with noise-corrupted images for detecting the failures that have the highest incidence rate: bearing and broken bar failures; moreover, rotor misalignment failure is also considered, as it can cause a further increase in electricity consumption. The presented results show that the proposal is effective in detecting healthy and failure states, as well as identifying the failure nature, as a 95% accuracy is achieved. These results allow considering the proposal as an interesting alternative for using IRT images obtained in hostile environments.
- Subjects
INDUCTION motors; CONVOLUTIONAL neural networks; THERMOGRAPHY; ELECTRIC power consumption; FAILED states
- Publication
Machines, 2023, Vol 11, Issue 7, p752
- ISSN
2075-1702
- Publication type
Article
- DOI
10.3390/machines11070752