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

Author:

Medina Ruben1ORCID,Sánchez René-Vinicio2ORCID,Cabrera Diego2ORCID,Cerrada Mariela2ORCID,Estupiñan Edgar3ORCID,Ao Wengang4,Vásquez Rafael E.5ORCID

Affiliation:

1. CIBYTEL-Engineering School, Universidad de Los Andes, Mérida 5101, Venezuela

2. GIDTEC, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador

3. Mechanical Engineering Department, Universidad de Tarapacá, Arica 1010069, Chile

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

5. School of Engineering, Universidad Pontificia Bolivariana, Circular 1 # 70-01, Medellín 050031, Colombia

Abstract

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.

Funder

MoST Science and Technology Partnership Program

National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University

Universidad Politécnica Salesiana

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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