Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning Models
Compressors and pumps are machines frequently used in petroleum and chemical industries for fluid transportation through flow systems to keep industrial processes running permanently. As their failure can produce costly disruption, developing fault detection and diagnosis tools is essential for accu...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-02-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/14/5/1710 |
_version_ | 1797264840558903296 |
---|---|
author | Diego Cabrera Ruben Medina Mariela Cerrada René-Vinicio Sánchez Edgar Estupiñan Chuan Li |
author_facet | Diego Cabrera Ruben Medina Mariela Cerrada René-Vinicio Sánchez Edgar Estupiñan Chuan Li |
author_sort | Diego Cabrera |
collection | DOAJ |
description | Compressors and pumps are machines frequently used in petroleum and chemical industries for fluid transportation through flow systems to keep industrial processes running permanently. As their failure can produce costly disruption, developing fault detection and diagnosis tools is essential for accurately detecting and diagnosing faults. This research proposes a bi-dimensional representation of the vibration signal corresponding to the Mel Frequency Cepstral Coefficients (MFCC) and their first two derivatives as features. The pseudo-periodic nature of the fault signature in rotating machines is exploited to put forward an efficient and accurate patch-wise fault classification method. This approach enables the classification of 13 combined types of faults in a multi-stage centrifugal pump and 17 faults in a reciprocating compressor. Classification is performed using the Long Short-Term Memory (LSTM) network, the bidirectional Long Short-Term Memory (BiLSTM) neural network, and the Convolutional Neural Network (CNN). Accurate classification over 99% is attained, showing that the proposed feature extraction procedure correctly classifies a large set of faults simultaneously appearing in such rotating machines. |
first_indexed | 2024-04-25T00:35:18Z |
format | Article |
id | doaj.art-465820a184b944e1b83ee41259cf38b5 |
institution | Directory Open Access Journal |
issn | 2076-3417 |
language | English |
last_indexed | 2024-04-25T00:35:18Z |
publishDate | 2024-02-01 |
publisher | MDPI AG |
record_format | Article |
series | Applied Sciences |
spelling | doaj.art-465820a184b944e1b83ee41259cf38b52024-03-12T16:38:35ZengMDPI AGApplied Sciences2076-34172024-02-01145171010.3390/app14051710Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning ModelsDiego Cabrera0Ruben Medina1Mariela Cerrada2René-Vinicio Sánchez3Edgar Estupiñan4Chuan Li5GIDTEC, Universidad Politécnica Salesiana, Cuenca 010105, EcuadorCIBYTEL—Engineering School, Universidad de Los Andes, Mérida 5101, VenezuelaGIDTEC, Universidad Politécnica Salesiana, Cuenca 010105, EcuadorGIDTEC, Universidad Politécnica Salesiana, Cuenca 010105, EcuadorMechanical Engineering Department, Universidad de Tarapacá, Arica 1010069, ChileSchool of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, ChinaCompressors and pumps are machines frequently used in petroleum and chemical industries for fluid transportation through flow systems to keep industrial processes running permanently. As their failure can produce costly disruption, developing fault detection and diagnosis tools is essential for accurately detecting and diagnosing faults. This research proposes a bi-dimensional representation of the vibration signal corresponding to the Mel Frequency Cepstral Coefficients (MFCC) and their first two derivatives as features. The pseudo-periodic nature of the fault signature in rotating machines is exploited to put forward an efficient and accurate patch-wise fault classification method. This approach enables the classification of 13 combined types of faults in a multi-stage centrifugal pump and 17 faults in a reciprocating compressor. Classification is performed using the Long Short-Term Memory (LSTM) network, the bidirectional Long Short-Term Memory (BiLSTM) neural network, and the Convolutional Neural Network (CNN). Accurate classification over 99% is attained, showing that the proposed feature extraction procedure correctly classifies a large set of faults simultaneously appearing in such rotating machines.https://www.mdpi.com/2076-3417/14/5/1710fault diagnosismel frequency cepstral coefficientsconvolutional neural networkslong short term memoryreciprocating compressorsmulti-stage centrifugal pumps |
spellingShingle | Diego Cabrera Ruben Medina Mariela Cerrada René-Vinicio Sánchez Edgar Estupiñan Chuan Li Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning Models Applied Sciences fault diagnosis mel frequency cepstral coefficients convolutional neural networks long short term memory reciprocating compressors multi-stage centrifugal pumps |
title | Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning Models |
title_full | Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning Models |
title_fullStr | Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning Models |
title_full_unstemmed | Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning Models |
title_short | Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning Models |
title_sort | improved mel frequency cepstral coefficients for compressors and pumps fault diagnosis with deep learning models |
topic | fault diagnosis mel frequency cepstral coefficients convolutional neural networks long short term memory reciprocating compressors multi-stage centrifugal pumps |
url | https://www.mdpi.com/2076-3417/14/5/1710 |
work_keys_str_mv | AT diegocabrera improvedmelfrequencycepstralcoefficientsforcompressorsandpumpsfaultdiagnosiswithdeeplearningmodels AT rubenmedina improvedmelfrequencycepstralcoefficientsforcompressorsandpumpsfaultdiagnosiswithdeeplearningmodels AT marielacerrada improvedmelfrequencycepstralcoefficientsforcompressorsandpumpsfaultdiagnosiswithdeeplearningmodels AT reneviniciosanchez improvedmelfrequencycepstralcoefficientsforcompressorsandpumpsfaultdiagnosiswithdeeplearningmodels AT edgarestupinan improvedmelfrequencycepstralcoefficientsforcompressorsandpumpsfaultdiagnosiswithdeeplearningmodels AT chuanli improvedmelfrequencycepstralcoefficientsforcompressorsandpumpsfaultdiagnosiswithdeeplearningmodels |