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

Author

Listed:
  • Ruben Medina

    (CIBYTEL-Engineering School, University of Los Andes, Mérida 5101, Venezuela)

  • Mariela Cerrada

    (GIDTEC, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador)

  • Shuai Yang

    (National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, 19# Xuefu Avenue, Nan’an District, Chongqing 400067, China)

  • Diego Cabrera

    (GIDTEC, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador)

  • Edgar Estupiñan

    (Mechanical Engineering Department, University of Tarapaca, Arica 1130000, Chile)

  • René-Vinicio Sánchez

    (GIDTEC, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador)

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.

Suggested Citation

  • Ruben Medina & Mariela Cerrada & Shuai Yang & Diego Cabrera & Edgar Estupiñan & René-Vinicio Sánchez, 2022. "Fault Classification in a Reciprocating Compressor and a Centrifugal Pump Using Non-Linear Entropy Features," Mathematics, MDPI, vol. 10(17), pages 1-29, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3033-:d:895187
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    References listed on IDEAS

    as
    1. Leite, Gustavo de Novaes Pires & Araújo, Alex Maurício & Rosas, Pedro André Carvalho & Stosic, Tatijana & Stosic, Borko, 2019. "Entropy measures for early detection of bearing faults," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 458-472.
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