Fault Classification in a Reciprocating Compressor and a Centrifugal Pump Using Non-Linear Entropy Features

Ruben Medina; Mariela Cerrada; Shuai Yang; Diego Cabrera; Edgar Estupiñan; René Vinicio Sánchez

doi:10.3390/math10173033

Fault Classification in a Reciprocating Compressor and a Centrifugal Pump Using Non-Linear Entropy Features

Ruben Medina
, Mariela Cerrada
, Shuai Yang
, Diego Cabrera
, Edgar Estupiñan
, René Vinicio Sánchez

Grupo de Investigación y Desarrollo en Tecnologías Industriales (GIDTEC)

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

10 Citas (Scopus)

Resumen

This paper describes a comparison of three types of feature sets. The feature sets were intended to classify 13 faults in a centrifugal pump (CP) and 17 valve faults in a reciprocating compressor (RC). The first set comprised 14 non-linear entropy-based features, the second comprised 15 information-based entropy features, and the third comprised 12 statistical features. The classification was performed using random forest (RF) models and support vector machines (SVM). The experimental work showed that the combination of information-based features with non-linear entropy-based features provides a statistically significant accuracy higher than the accuracy provided by the Statistical Features set. Results for classifying the 13 conditions in the CP using non-linear entropy features showed accuracies of up to 99.50%. The same feature set provided a classification accuracy of 97.50% for the classification of the 17 conditions in the RC.

Idioma original	Inglés
Número de artículo	3033
Publicación	Mathematics
Volumen	10
N.º	17
DOI	https://doi.org/10.3390/math10173033
Estado	Publicada - sept 2022

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Publisher Copyright:
© 2022 by the authors.

Areas de Conocimiento del CACES

827A Mantenimiento industrial

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10.3390/math10173033

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Monitoreo Inteligente de la Condición de Maquinaria Rotativa Mediante la Fusión de Señales de Audio, Emisión Acústica, Vibración y Corriente
Llerena Pizarro, O. R. (Investigador Secundario), Sanchez Loja, R. V. (Investigador principal), Cabrera Mendieta, D. R. (Investigador Secundario), Lucero Otorongo, P. M. (Investigador Externo), Macancela Poveda, J. C. (Investigador Externo), Perez Rivera, I. A. (Investigador Externo), Pacheco Cordova, E. E. (Investigador Externo), Vacacela Costa, A. S. (Estudiante Investigador), Pacheco Montilla, F. K. (Investigador Externo), Villacis Marin, M. L. (Investigador Secundario), Guaman Buestan, A. D. P. (Investigador Secundario), Torres Diaz, C. P. (Investigador Externo), Valente De Oliveira, J. L. (Investigador Externo), Vásquez, R. (Investigador Externo), Lojano Armijos, F. J. (Estudiante Investigador), Chingal Imaicela, D. E. (Investigador Externo), Siguencia Urgiles, J. F. (Investigador Externo), Cajas Muñoz, F. D. (Investigador Externo), Montalvan Pulla, F. I. (Estudiante Investigador), Quinteros Espinoza, M. E. (Investigador Externo), Ortega Lucero, L. R. (Estudiante Investigador), Llivicura Orellana, H. F. (Estudiante Investigador), Calle Lazo, A. K. (Estudiante Investigador) & Li, C. (Investigador Externo)
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title = "Fault Classification in a Reciprocating Compressor and a Centrifugal Pump Using Non-Linear Entropy Features",

abstract = "This paper describes a comparison of three types of feature sets. The feature sets were intended to classify 13 faults in a centrifugal pump (CP) and 17 valve faults in a reciprocating compressor (RC). The first set comprised 14 non-linear entropy-based features, the second comprised 15 information-based entropy features, and the third comprised 12 statistical features. The classification was performed using random forest (RF) models and support vector machines (SVM). The experimental work showed that the combination of information-based features with non-linear entropy-based features provides a statistically significant accuracy higher than the accuracy provided by the Statistical Features set. Results for classifying the 13 conditions in the CP using non-linear entropy features showed accuracies of up to 99.50\%. The same feature set provided a classification accuracy of 97.50\% for the classification of the 17 conditions in the RC.",

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author = "Ruben Medina and Mariela Cerrada and Shuai Yang and Diego Cabrera and Edgar Estupi{\~n}an and S{\'a}nchez, \{Ren{\'e} Vinicio\}",

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doi = "10.3390/math10173033",

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AU - Medina, Ruben

AU - Cerrada, Mariela

AU - Yang, Shuai

AU - Cabrera, Diego

AU - Estupiñan, Edgar

AU - Sánchez, René Vinicio

PY - 2022/9

Y1 - 2022/9

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AB - This paper describes a comparison of three types of feature sets. The feature sets were intended to classify 13 faults in a centrifugal pump (CP) and 17 valve faults in a reciprocating compressor (RC). The first set comprised 14 non-linear entropy-based features, the second comprised 15 information-based entropy features, and the third comprised 12 statistical features. The classification was performed using random forest (RF) models and support vector machines (SVM). The experimental work showed that the combination of information-based features with non-linear entropy-based features provides a statistically significant accuracy higher than the accuracy provided by the Statistical Features set. Results for classifying the 13 conditions in the CP using non-linear entropy features showed accuracies of up to 99.50%. The same feature set provided a classification accuracy of 97.50% for the classification of the 17 conditions in the RC.

KW - approximate entropy

KW - fault classification

KW - non-linear systems

KW - phase space reconstruction

KW - random forest

KW - support vector machines

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Fault Classification in a Reciprocating Compressor and a Centrifugal Pump Using Non-Linear Entropy Features

Resumen

Nota bibliográfica

Areas de Conocimiento del CACES

Acceder al documento

Otros archivos y enlaces

Proyectos

Monitoreo Inteligente de la Condición de Maquinaria Rotativa Mediante la Fusión de Señales de Audio, Emisión Acústica, Vibración y Corriente

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