Poincaré Images Extracted from Vibration Signals are Useful Features for Fault Classification in a Reciprocating Compressor

Medina, Rubén; Cerrada, Mariela; Cabrera, Diego; Sánchez, René–Vinicio

doi:10.1007/978-3-031-26361-3_11

Cited by 1 publication

(1 citation statement)

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“…This computational efficiency is a relevant advantage of the MFDFA method proposed in this document. Research reported in [63] investigated the usefulness of Poincaré images generated from vibration signals, combined with CNN models for fault classification in a reciprocating compressor. The research was validated using the multi-valve dataset with 17 valve faults from the reciprocating compressor.…”

Section: Discussionmentioning

confidence: 99%

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

Medina,

Sánchez,

Cabrera

et al. 2024

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%