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Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features

In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extracti...

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Autores principales: Toma, Rafia Nishat, Gao, Yangde, Piltan, Farzin, Im, Kichang, Shon, Dongkoo, Yoon, Tae Hyun, Yoo, Dae-Seung, Kim, Jong-Myon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696953/
https://www.ncbi.nlm.nih.gov/pubmed/36433553
http://dx.doi.org/10.3390/s22228958
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author Toma, Rafia Nishat
Gao, Yangde
Piltan, Farzin
Im, Kichang
Shon, Dongkoo
Yoon, Tae Hyun
Yoo, Dae-Seung
Kim, Jong-Myon
author_facet Toma, Rafia Nishat
Gao, Yangde
Piltan, Farzin
Im, Kichang
Shon, Dongkoo
Yoon, Tae Hyun
Yoo, Dae-Seung
Kim, Jong-Myon
author_sort Toma, Rafia Nishat
collection PubMed
description In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extraction techniques are sensitive to the noisy component of the signal and need more time for training. To deal with these issues, a comparatively new feature extraction technique, referred to as a wavelet scattering transform (WST) is utilized, and incorporated with ML classifiers to design a framework for bearing fault classification in this paper. The WST is a knowledge-based technique, and the structure is similar to the convolution neural network. This technique provides low-variance features of real-valued signals, which are usually necessary for classification tasks. These signals are resistant to signal deformation and preserve information at high frequencies. The current signal data from a publicly available dataset for three different bearing conditions are considered. By combining the scattering path coefficients, the decomposition coefficients from the 0th and 1st layers are considered as features. The experimental results demonstrate that WST-based features, when used with ensemble ML algorithms, could achieve more than 99% classification accuracy. The performance of ANN models with these features is similar. This work exhibits that utilizing WST coefficients for the motor current signal as features can improve the bearing fault classification accuracy when compared to other feature extraction approaches such as empirical wavelet transform (EWT), information fusion (IF), and wavelet packet decomposition (WPD). Thus, our proposed approach can be considered as an effective classification method for the fault diagnosis of rotating machinery.
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spelling pubmed-96969532022-11-26 Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features Toma, Rafia Nishat Gao, Yangde Piltan, Farzin Im, Kichang Shon, Dongkoo Yoon, Tae Hyun Yoo, Dae-Seung Kim, Jong-Myon Sensors (Basel) Article In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extraction techniques are sensitive to the noisy component of the signal and need more time for training. To deal with these issues, a comparatively new feature extraction technique, referred to as a wavelet scattering transform (WST) is utilized, and incorporated with ML classifiers to design a framework for bearing fault classification in this paper. The WST is a knowledge-based technique, and the structure is similar to the convolution neural network. This technique provides low-variance features of real-valued signals, which are usually necessary for classification tasks. These signals are resistant to signal deformation and preserve information at high frequencies. The current signal data from a publicly available dataset for three different bearing conditions are considered. By combining the scattering path coefficients, the decomposition coefficients from the 0th and 1st layers are considered as features. The experimental results demonstrate that WST-based features, when used with ensemble ML algorithms, could achieve more than 99% classification accuracy. The performance of ANN models with these features is similar. This work exhibits that utilizing WST coefficients for the motor current signal as features can improve the bearing fault classification accuracy when compared to other feature extraction approaches such as empirical wavelet transform (EWT), information fusion (IF), and wavelet packet decomposition (WPD). Thus, our proposed approach can be considered as an effective classification method for the fault diagnosis of rotating machinery. MDPI 2022-11-19 /pmc/articles/PMC9696953/ /pubmed/36433553 http://dx.doi.org/10.3390/s22228958 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Toma, Rafia Nishat
Gao, Yangde
Piltan, Farzin
Im, Kichang
Shon, Dongkoo
Yoon, Tae Hyun
Yoo, Dae-Seung
Kim, Jong-Myon
Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features
title Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features
title_full Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features
title_fullStr Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features
title_full_unstemmed Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features
title_short Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features
title_sort classification framework of the bearing faults of an induction motor using wavelet scattering transform-based features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696953/
https://www.ncbi.nlm.nih.gov/pubmed/36433553
http://dx.doi.org/10.3390/s22228958
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