$Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra$
Código QR

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Iwona Komorska, Andrzej Puchalski
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wavelet leaders multifractal spectrum rotating machines fault diagnostics Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/7291f6f0677f45d2bf8d0a2043943e04
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7291f6f0677f45d2bf8d0a2043943e04
record_format	dspace
spelling	oai:doaj.org-article:7291f6f0677f45d2bf8d0a2043943e042021-11-25T18:58:24ZRotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra10.3390/s212276771424-8220https://doaj.org/article/7291f6f0677f45d2bf8d0a2043943e042021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7677https://doaj.org/toc/1424-8220Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode decomposition algorithm with wavelet leader multifractal formalism applied to diagnosing damages of rotating machines in non-stationary conditions. The development of damage causes an increase in the level of multifractality of the signal. The multifractal spectrum obtained as a result of the algorithm changes its shape. Diagnosis is based on the classification of the features of this spectrum. The method is effective in relation to faults causing impulse responses in the dynamic signal registered by the sensors. The method has been illustrated with examples of vibration signals of rotating machines recorded on a laboratory stand, as well as on real objects.Iwona KomorskaAndrzej PuchalskiMDPI AGarticlewavelet leadersmultifractal spectrumrotating machinesfault diagnosticsChemical technologyTP1-1185ENSensors, Vol 21, Iss 7677, p 7677 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wavelet leaders multifractal spectrum rotating machines fault diagnostics Chemical technology TP1-1185
spellingShingle	wavelet leaders multifractal spectrum rotating machines fault diagnostics Chemical technology TP1-1185 Iwona Komorska Andrzej Puchalski Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra
description	Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode decomposition algorithm with wavelet leader multifractal formalism applied to diagnosing damages of rotating machines in non-stationary conditions. The development of damage causes an increase in the level of multifractality of the signal. The multifractal spectrum obtained as a result of the algorithm changes its shape. Diagnosis is based on the classification of the features of this spectrum. The method is effective in relation to faults causing impulse responses in the dynamic signal registered by the sensors. The method has been illustrated with examples of vibration signals of rotating machines recorded on a laboratory stand, as well as on real objects.
format	article
author	Iwona Komorska Andrzej Puchalski
author_facet	Iwona Komorska Andrzej Puchalski
author_sort	Iwona Komorska
title	Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra
title_short	Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra
title_full	Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra
title_fullStr	Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra
title_full_unstemmed	Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra
title_sort	rotating machinery diagnosing in non-stationary conditions with empirical mode decomposition-based wavelet leaders multifractal spectra
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7291f6f0677f45d2bf8d0a2043943e04
work_keys_str_mv	AT iwonakomorska rotatingmachinerydiagnosinginnonstationaryconditionswithempiricalmodedecompositionbasedwaveletleadersmultifractalspectra AT andrzejpuchalski rotatingmachinerydiagnosinginnonstationaryconditionswithempiricalmodedecompositionbasedwaveletleadersmultifractalspectra
_version_	1718410468642521088

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

Ejemplares similares