Published

2024-12-01

Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches

Detección de daños basada en impedancia electromecánica mediante métodos de aprendizaje automático

DOI:

https://doi.org/10.15446/ing.investig.111646

Keywords:

Electromechanical Impedance Method, K-Means algorithm, Decision Tree, Random Forest, Structural Health Monitoring (en)
Método de impedancia electromecánica, Algoritmo K-Medias, Árbol de Decisión, Bosque Aleatorio, Control del Estado Estructural (es)

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Electromechanical impedance-based structural health monitoring has been the subject of extensive research in recent decades. The method’s low cost and ability to detect minor structural damages make it an appealing alternative to other non-destructive techniques. Ongoing research on damage detection approaches continues to be a topic of interest in relation to the electromechanical impedance method. This work proposes the use of the K-Means, Decision Tree, and Random Forest algorithms to distinguish between four structural conditions in an aluminum beam. These techniques were applied to raw impedance data and a dataset reduced via principal components analysis. The findings revealed that the compressed dataset improved the accuracy of all models, except for the Random Forest approach, whose accuracy decreased by 2.9%. The K-Means algorithm was most affected by the reduction in dimensionality, with a 105.9% increase in accuracy. The Decision Tree and Random Forest methods yielded outstanding outcomes, comparable or superior to other state-of-the-art approaches. This makes them a compelling choice for detecting damage using electromechanical impedance data, even when using raw data as the input information.

El monitoreo de la salud estructural basado en la impedancia electromagnética ha sido objeto de investigación exhaustiva en las últimas décadas. El bajo coste del método y su capacidad para detectar daños estructurales menores lo convierten en una alternativa atractiva a otras técnicas no destructivas. La investigación actual sobre enfoques de detección de daños sigue siendo un tema de interés en lo que concierne al método de impedancia electromecánica. En este trabajo se propone utilizar los algoritmos K-Means, Decision Tree y Random Forest para diferenciar entre cuatro condiciones estructurales en una viga de aluminio. Estas técnicas se aplicaron a datos de impedancia en bruto y a un conjunto de datos reducido mediante análisis de componentes principales. Los resultados revelaron que el conjunto de datos comprimido mejoro la precisión de todos los modelos, excepto en el caso del método Random Forest, cuya precisión disminuyo en un 2,9 %. El algoritmo K-Means fue el más afectado por la reducción de la dimensionalidad, con un aumento del 105,9 % en la precisión. Los métodos Decision Tree y Random Forest produjeron resultados sobresalientes, comparables o superiores a otros enfoques de vanguardia. Esto los convierte en una opción convincente para detectar daños a traves de datos de impedancia electromecánica, incluso cuando se utilizan datos en bruto como informacion de entrada.

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Pereira, P. E. C., de Rezende, S. W. F., Barella, B. P., de Moura Junior, J. dos R. V. and Finzi Neto, R. M. (2024). Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches. Ingeniería e Investigación, 44(3), e111646. https://doi.org/10.15446/ing.investig.111646

ACM

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Pereira, P.E.C., de Rezende, S.W.F., Barella, B.P., de Moura Junior, J. dos R.V. and Finzi Neto, R.M. 2024. Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches. Ingeniería e Investigación. 44, 3 (Dec. 2024), e111646. DOI:https://doi.org/10.15446/ing.investig.111646.

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Pereira, P. E. C.; de Rezende, S. W. F.; Barella, B. P.; de Moura Junior, J. dos R. V.; Finzi Neto, R. M. Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches. Ing. Inv. 2024, 44, e111646.

ABNT

PEREIRA, P. E. C.; DE REZENDE, S. W. F.; BARELLA, B. P.; DE MOURA JUNIOR, J. dos R. V.; FINZI NETO, R. M. Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches. Ingeniería e Investigación, [S. l.], v. 44, n. 3, p. e111646, 2024. DOI: 10.15446/ing.investig.111646. Disponível em: https://revistas.unal.edu.co/index.php/ingeinv/article/view/111646. Acesso em: 6 mar. 2025.

Chicago

Pereira, Paulo Elias Carneiro, Stanley Washington Ferreira de Rezende, Bruno Pereira Barella, José dos Reis Vieira de Moura Junior, and Roberto Mendes Finzi Neto. 2024. “Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches”. Ingeniería E Investigación 44 (3):e111646. https://doi.org/10.15446/ing.investig.111646.

Harvard

Pereira, P. E. C., de Rezende, S. W. F., Barella, B. P., de Moura Junior, J. dos R. V. and Finzi Neto, R. M. (2024) “Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches”, Ingeniería e Investigación, 44(3), p. e111646. doi: 10.15446/ing.investig.111646.

IEEE

[1]
P. E. C. Pereira, S. W. F. de Rezende, B. P. Barella, J. dos R. V. de Moura Junior, and R. M. Finzi Neto, “Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches”, Ing. Inv., vol. 44, no. 3, p. e111646, Dec. 2024.

MLA

Pereira, P. E. C., S. W. F. de Rezende, B. P. Barella, J. dos R. V. de Moura Junior, and R. M. Finzi Neto. “Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches”. Ingeniería e Investigación, vol. 44, no. 3, Dec. 2024, p. e111646, doi:10.15446/ing.investig.111646.

Turabian

Pereira, Paulo Elias Carneiro, Stanley Washington Ferreira de Rezende, Bruno Pereira Barella, José dos Reis Vieira de Moura Junior, and Roberto Mendes Finzi Neto. “Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches”. Ingeniería e Investigación 44, no. 3 (December 1, 2024): e111646. Accessed March 6, 2025. https://revistas.unal.edu.co/index.php/ingeinv/article/view/111646.

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Pereira PEC, de Rezende SWF, Barella BP, de Moura Junior J dos RV, Finzi Neto RM. Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches. Ing. Inv. [Internet]. 2024 Dec. 1 [cited 2025 Mar. 6];44(3):e111646. Available from: https://revistas.unal.edu.co/index.php/ingeinv/article/view/111646

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