Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS

Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of be...

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Autores principales:	Mostafa Rostaghi, Mohammad Mahdi Khatibi, Mohammad Reza Ashory, Hamed Azami
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	dispersion entropy fault diagnosis refined composite generalized multiscale dispersion entropy (RCGMDispEn) bearing multiclass FCM-ANFIS Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/042da005a2784e02956f9125a26c2db8
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id	oai:doaj.org-article:042da005a2784e02956f9125a26c2db8
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spelling	oai:doaj.org-article:042da005a2784e02956f9125a26c2db82021-11-25T17:30:23ZBearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS10.3390/e231115101099-4300https://doaj.org/article/042da005a2784e02956f9125a26c2db82021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1510https://doaj.org/toc/1099-4300Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults. Numerous studies have investigated multiscale algorithms; nevertheless, multiscale algorithms using the first moment lose important complexity data. Accordingly, generalized multiscale algorithms have been recently introduced. The present research examined the use of refined composite generalized multiscale dispersion entropy (RCGMDispEn) based on the second moment (variance) and third moment (skewness) along with refined composite multiscale dispersion entropy (RCMDispEn) in bearing fault diagnosis. Moreover, multiclass FCM-ANFIS, which is a combination of adaptive network-based fuzzy inference systems (ANFIS), was developed to improve the efficiency of rotating machinery fault classification. According to the results, it is recommended that generalized multiscale algorithms based on variance and skewness be examined for diagnosis, along with multiscale algorithms, and be used to achieve an improvement in the results. The simultaneous usage of the multiscale algorithm and generalized multiscale algorithms improved the results in all three real datasets used in this study.Mostafa RostaghiMohammad Mahdi KhatibiMohammad Reza AshoryHamed AzamiMDPI AGarticledispersion entropyfault diagnosisrefined composite generalized multiscale dispersion entropy (RCGMDispEn)bearingmulticlass FCM-ANFISScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1510, p 1510 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dispersion entropy fault diagnosis refined composite generalized multiscale dispersion entropy (RCGMDispEn) bearing multiclass FCM-ANFIS Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	dispersion entropy fault diagnosis refined composite generalized multiscale dispersion entropy (RCGMDispEn) bearing multiclass FCM-ANFIS Science Q Astrophysics QB460-466 Physics QC1-999 Mostafa Rostaghi Mohammad Mahdi Khatibi Mohammad Reza Ashory Hamed Azami Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS
description	Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults. Numerous studies have investigated multiscale algorithms; nevertheless, multiscale algorithms using the first moment lose important complexity data. Accordingly, generalized multiscale algorithms have been recently introduced. The present research examined the use of refined composite generalized multiscale dispersion entropy (RCGMDispEn) based on the second moment (variance) and third moment (skewness) along with refined composite multiscale dispersion entropy (RCMDispEn) in bearing fault diagnosis. Moreover, multiclass FCM-ANFIS, which is a combination of adaptive network-based fuzzy inference systems (ANFIS), was developed to improve the efficiency of rotating machinery fault classification. According to the results, it is recommended that generalized multiscale algorithms based on variance and skewness be examined for diagnosis, along with multiscale algorithms, and be used to achieve an improvement in the results. The simultaneous usage of the multiscale algorithm and generalized multiscale algorithms improved the results in all three real datasets used in this study.
format	article
author	Mostafa Rostaghi Mohammad Mahdi Khatibi Mohammad Reza Ashory Hamed Azami
author_facet	Mostafa Rostaghi Mohammad Mahdi Khatibi Mohammad Reza Ashory Hamed Azami
author_sort	Mostafa Rostaghi
title	Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS
title_short	Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS
title_full	Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS
title_fullStr	Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS
title_full_unstemmed	Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS
title_sort	bearing fault diagnosis using refined composite generalized multiscale dispersion entropy-based skewness and variance and multiclass fcm-anfis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/042da005a2784e02956f9125a26c2db8
work_keys_str_mv	AT mostafarostaghi bearingfaultdiagnosisusingrefinedcompositegeneralizedmultiscaledispersionentropybasedskewnessandvarianceandmulticlassfcmanfis AT mohammadmahdikhatibi bearingfaultdiagnosisusingrefinedcompositegeneralizedmultiscaledispersionentropybasedskewnessandvarianceandmulticlassfcmanfis AT mohammadrezaashory bearingfaultdiagnosisusingrefinedcompositegeneralizedmultiscaledispersionentropybasedskewnessandvarianceandmulticlassfcmanfis AT hamedazami bearingfaultdiagnosisusingrefinedcompositegeneralizedmultiscaledispersionentropybasedskewnessandvarianceandmulticlassfcmanfis
_version_	1718412265427828736

Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS

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