Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor

Ruben Medina, René Vinicio Sánchez, Diego Cabrera, Mariela Cerrada, Edgar Estupiñan, Wengang Ao, Rafael E. Vásquez

    Producción científica: Contribución a una revistaArtículo en revista científica indexadarevisión exhaustiva

    Resumen

    Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

    Idioma originalInglés
    Número de artículo461
    PublicaciónSensors
    Volumen24
    N.º2
    DOI
    EstadoPublicada - ene. 2024

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    © 2024 by the authors.

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