Análise comparativa de acelerômetros MEMS de baixo custo para detecção de falhas em rolamentos usando redes neurais convolucionais
Resumo
A manutenção preditiva é essencial para a confiabilidade de máquinas rotativas, nas quais falhas em rolamentos são frequentes. Este trabalho avalia o uso de acelerômetros MEMS de baixo custo na detecção de falhas por meio de Redes Neurais Convolucionais (CNNs). Os sinais de vibração foram coletados a 200 RPM em três condições: normal, falha no elemento rolante e falha na pista interna. Após pré-processamento, os dados foram convertidos em matrizes 8×8 para entrada na CNN. O modelo alcançou aproximadamente 94,76 de acurácia, precisão e recall. Os resultados demonstram a viabilidade da abordagem para manutenção preditiva de baixo custo.Referências
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Gawde, S., Patil, S., Kumar, S., and Kotecha, K. (2023). A scoping review on multi-fault diagnosis of industrial rotating machines using multi-sensor data fusion. Artificial Intelligence Review, 56:4711–4764.
Hakim, M., Omran, A. A. B., Ahmed, A. N., Al-Waily, M., and Abdellatif, A. (2023). A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations. Ain Shams Engineering Journal, 14:101945.
Lu, C., Wang, Z., and Zhou, B. (2017). Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification. Advanced Engineering Informatics, 32:139–151.
Ompusunggu, A. P., Eryılmaz, K., and Janssen, K. (2021). Condition monitoring of critical industrial assets using high performing low-cost mems accelerometers. In Procedia CIRP, volume 104, pages 1389–1394.
Romanssini, M., Aguirre, P. C. C. d., Compassi-Severo, L., and Girardi, A. G. (2023). A review on vibration monitoring techniques for predictive maintenance of rotating machinery. Sensors.
Sharma, G., Kaur, T., Mangal, S. K., and Kohli, A. (2024). Investigating bearing and gear vibrations with a micro-electro-mechanical systems (mems) and machine learning approach. Results in Engineering.
Sinkittiyanont, S. and Ratanasumawong, C. (2024). Fault diagnosis of rolling element bearing from vibration signal using one dimensional convolutional neural networks. In 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE), pages 390–397.
Tan, A. and Wannaboon, C. (2026). Lightweight cnn-based bearing fault diagnosis on resource-constrained devices. International Journal of Innovative Computing, Information and Control, 22(2):565–578.
Zhang, S., Zhang, S., Wang, B., and Habetler, T. G. (2020). Deep learning algorithms for bearing fault diagnostics—a comprehensive review. IEEE Access.
Zhao, J., Wang, W., Huang, J., and Ma, X. (2025). A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges. AIP Advances, 15(2):020702.
Zhu, Z., Lei, Y., Qi, G., Chai, Y., Mazur, N., An, Y., and Huang, X. (2023). A review of the application of deep learning in intelligent fault diagnosis of rotating machinery. Measurement, 206:112346.
Publicado
19/07/2026
Como Citar
ALMEIDA, Diogo S.; PELEGRINO, João V.; PESSOA, Cristiano A.; DANTAS, Rogério D.; S. JUNIOR, Wilson C..
Análise comparativa de acelerômetros MEMS de baixo custo para detecção de falhas em rolamentos usando redes neurais convolucionais. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 53. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 84-95.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2026.23994.
