Calibration of a Hub Dynamic Balancing Machine Based on the Least Squares Method and Systematic Error Analysis

To resolve issues such as excessive residual vibrations and unsatisfactory balance effects in the balancing process, the particle swarm optimization (PSO)algorithm is combined with the least squares influence coefficient method of rotor dynamic balance to perform dynamic balance calibration based on...

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Autores principales:	Yuan Cao, Fang Li, Jianguo Cao, Tao Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Rotor balancing influence coefficient method least squares method particle swarm optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2ea3dddd339d448489f1d1f7944acd3d
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id	oai:doaj.org-article:2ea3dddd339d448489f1d1f7944acd3d
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spelling	oai:doaj.org-article:2ea3dddd339d448489f1d1f7944acd3d2021-11-19T00:06:43ZCalibration of a Hub Dynamic Balancing Machine Based on the Least Squares Method and Systematic Error Analysis2169-353610.1109/ACCESS.2020.3024850https://doaj.org/article/2ea3dddd339d448489f1d1f7944acd3d2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9200484/https://doaj.org/toc/2169-3536To resolve issues such as excessive residual vibrations and unsatisfactory balance effects in the balancing process, the particle swarm optimization (PSO)algorithm is combined with the least squares influence coefficient method of rotor dynamic balance to perform dynamic balance calibration based on the research of the least squares influence coefficient method of wheel dynamic balance. The influence coefficient generally has a large error due to the influence of the vibration measured value, thereby lowering the accuracy of the calibrated influence coefficient. Therefore, the maximum likelihood estimate (MLE) method is employed to address the influence coefficient error, and the result is compared with the calibration value of the influence coefficient (IC) method. The analysis results indicate that the residual value generated by the calibration of the influence coefficient through the maximum likelihood estimate (MLE) is 1.036 while the residual value obtained through the influence coefficient (IC) method is 1.513, suggesting that the former exhibits a smaller systematic error and is closer to the true value.Yuan CaoFang LiJianguo CaoTao WangIEEEarticleRotor balancinginfluence coefficient methodleast squares methodparticle swarm optimizationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 178746-178754 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Rotor balancing influence coefficient method least squares method particle swarm optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Rotor balancing influence coefficient method least squares method particle swarm optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yuan Cao Fang Li Jianguo Cao Tao Wang Calibration of a Hub Dynamic Balancing Machine Based on the Least Squares Method and Systematic Error Analysis
description	To resolve issues such as excessive residual vibrations and unsatisfactory balance effects in the balancing process, the particle swarm optimization (PSO)algorithm is combined with the least squares influence coefficient method of rotor dynamic balance to perform dynamic balance calibration based on the research of the least squares influence coefficient method of wheel dynamic balance. The influence coefficient generally has a large error due to the influence of the vibration measured value, thereby lowering the accuracy of the calibrated influence coefficient. Therefore, the maximum likelihood estimate (MLE) method is employed to address the influence coefficient error, and the result is compared with the calibration value of the influence coefficient (IC) method. The analysis results indicate that the residual value generated by the calibration of the influence coefficient through the maximum likelihood estimate (MLE) is 1.036 while the residual value obtained through the influence coefficient (IC) method is 1.513, suggesting that the former exhibits a smaller systematic error and is closer to the true value.
format	article
author	Yuan Cao Fang Li Jianguo Cao Tao Wang
author_facet	Yuan Cao Fang Li Jianguo Cao Tao Wang
author_sort	Yuan Cao
title	Calibration of a Hub Dynamic Balancing Machine Based on the Least Squares Method and Systematic Error Analysis
title_short	Calibration of a Hub Dynamic Balancing Machine Based on the Least Squares Method and Systematic Error Analysis
title_full	Calibration of a Hub Dynamic Balancing Machine Based on the Least Squares Method and Systematic Error Analysis
title_fullStr	Calibration of a Hub Dynamic Balancing Machine Based on the Least Squares Method and Systematic Error Analysis
title_full_unstemmed	Calibration of a Hub Dynamic Balancing Machine Based on the Least Squares Method and Systematic Error Analysis
title_sort	calibration of a hub dynamic balancing machine based on the least squares method and systematic error analysis
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/2ea3dddd339d448489f1d1f7944acd3d
work_keys_str_mv	AT yuancao calibrationofahubdynamicbalancingmachinebasedontheleastsquaresmethodandsystematicerroranalysis AT fangli calibrationofahubdynamicbalancingmachinebasedontheleastsquaresmethodandsystematicerroranalysis AT jianguocao calibrationofahubdynamicbalancingmachinebasedontheleastsquaresmethodandsystematicerroranalysis AT taowang calibrationofahubdynamicbalancingmachinebasedontheleastsquaresmethodandsystematicerroranalysis
_version_	1718420600287920128

Calibration of a Hub Dynamic Balancing Machine Based on the Least Squares Method and Systematic Error Analysis

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