Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal

Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diesel engine surface vibration signal were detected, p...

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Autores principales:	Yan Shen, Ping Wang, Xuesong Wang, Ke Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	surface vibration signal improved empirical mode decomposition extreme learning machine Technology T
Acceso en línea:	https://doaj.org/article/ce89132455864a6f81ef9d82aae3668d
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id	oai:doaj.org-article:ce89132455864a6f81ef9d82aae3668d
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spelling	oai:doaj.org-article:ce89132455864a6f81ef9d82aae3668d2021-11-25T17:26:14ZApplication of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal10.3390/en142275191996-1073https://doaj.org/article/ce89132455864a6f81ef9d82aae3668d2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7519https://doaj.org/toc/1996-1073Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diesel engine surface vibration signal were detected, predicted, and analyzed. First, the surface vibration signal was decomposed into a series of signal components by an improved empirical mode decomposition algorithm. Then, the extreme learning machine algorithm was applied to each signal component to obtain the predicted value of the corresponding signal component and determine the characteristics of the ground vibration signal. Compared with the empirical mode decomposition–extremum learning machine algorithm and the extremum learning machine algorithm, the results show that the improved empirical mode decomposition–extremum learning machine algorithm is feasible and effective.Yan ShenPing WangXuesong WangKe SunMDPI AGarticlesurface vibration signalimproved empirical mode decompositionextreme learning machineTechnologyTENEnergies, Vol 14, Iss 7519, p 7519 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	surface vibration signal improved empirical mode decomposition extreme learning machine Technology T
spellingShingle	surface vibration signal improved empirical mode decomposition extreme learning machine Technology T Yan Shen Ping Wang Xuesong Wang Ke Sun Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal
description	Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diesel engine surface vibration signal were detected, predicted, and analyzed. First, the surface vibration signal was decomposed into a series of signal components by an improved empirical mode decomposition algorithm. Then, the extreme learning machine algorithm was applied to each signal component to obtain the predicted value of the corresponding signal component and determine the characteristics of the ground vibration signal. Compared with the empirical mode decomposition–extremum learning machine algorithm and the extremum learning machine algorithm, the results show that the improved empirical mode decomposition–extremum learning machine algorithm is feasible and effective.
format	article
author	Yan Shen Ping Wang Xuesong Wang Ke Sun
author_facet	Yan Shen Ping Wang Xuesong Wang Ke Sun
author_sort	Yan Shen
title	Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal
title_short	Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal
title_full	Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal
title_fullStr	Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal
title_full_unstemmed	Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal
title_sort	application of empirical mode decomposition and extreme learning machine algorithms on prediction of the surface vibration signal
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ce89132455864a6f81ef9d82aae3668d
work_keys_str_mv	AT yanshen applicationofempiricalmodedecompositionandextremelearningmachinealgorithmsonpredictionofthesurfacevibrationsignal AT pingwang applicationofempiricalmodedecompositionandextremelearningmachinealgorithmsonpredictionofthesurfacevibrationsignal AT xuesongwang applicationofempiricalmodedecompositionandextremelearningmachinealgorithmsonpredictionofthesurfacevibrationsignal AT kesun applicationofempiricalmodedecompositionandextremelearningmachinealgorithmsonpredictionofthesurfacevibrationsignal
_version_	1718412376442667008

Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal

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