A new modal analysis method applied to changing machine tool using clustering

The states of the machine tool, such as the components’ position and the spindle speed, play leading roles in the change of dynamic parameters. However, the traditional modal analysis method that modal parameters manually identified from vibration signal is greatly interfered by harmonics, and the p...

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Main Authors: Xuchu Jiang, Xinyong Mao, Yingjie Chen, Caihua Hao
Format: Article
Language:English
Published: SAGE Publishing 2020-10-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1177/1687814020968323
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spelling doaj-d8dc13ff649141269f5fc2559e58dd5b2020-11-25T04:06:37ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402020-10-011210.1177/1687814020968323A new modal analysis method applied to changing machine tool using clusteringXuchu Jiang0Xinyong Mao1Yingjie Chen2Caihua Hao3School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, ChinaSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, ChinaWuhan Intelligent Equipment Industrial Technology Institute Company Limited, Huazhong University of Science and Technology, Wuhan, ChinaThe states of the machine tool, such as the components’ position and the spindle speed, play leading roles in the change of dynamic parameters. However, the traditional modal analysis method that modal parameters manually identified from vibration signal is greatly interfered by harmonics, and the process of eliminating interference is very inefficient and subjective. At present, there is a lack of a standard and efficient method to characterize modal parameter changes in different states of machine tools. This paper proposes a new machine tool modal classification analysis method based on clustering. The characteristics related to the modal parameters are extracted from the response signal in different states, and the clustering results are used to reflect the changes of machine tool modal parameters. After the amplitude of the frequency response function is normalized, the characteristics related to the natural frequency are acquired, and the clustering results further reflect the difference of the natural frequency of the signal. The new method based on clustering can be a standard and efficient method to characterize modal parameter changes in different states of machine tools.https://doi.org/10.1177/1687814020968323
collection DOAJ
language English
format Article
sources DOAJ
author Xuchu Jiang
Xinyong Mao
Yingjie Chen
Caihua Hao
spellingShingle Xuchu Jiang
Xinyong Mao
Yingjie Chen
Caihua Hao
A new modal analysis method applied to changing machine tool using clustering
Advances in Mechanical Engineering
author_facet Xuchu Jiang
Xinyong Mao
Yingjie Chen
Caihua Hao
author_sort Xuchu Jiang
title A new modal analysis method applied to changing machine tool using clustering
title_short A new modal analysis method applied to changing machine tool using clustering
title_full A new modal analysis method applied to changing machine tool using clustering
title_fullStr A new modal analysis method applied to changing machine tool using clustering
title_full_unstemmed A new modal analysis method applied to changing machine tool using clustering
title_sort new modal analysis method applied to changing machine tool using clustering
publisher SAGE Publishing
series Advances in Mechanical Engineering
issn 1687-8140
publishDate 2020-10-01
description The states of the machine tool, such as the components’ position and the spindle speed, play leading roles in the change of dynamic parameters. However, the traditional modal analysis method that modal parameters manually identified from vibration signal is greatly interfered by harmonics, and the process of eliminating interference is very inefficient and subjective. At present, there is a lack of a standard and efficient method to characterize modal parameter changes in different states of machine tools. This paper proposes a new machine tool modal classification analysis method based on clustering. The characteristics related to the modal parameters are extracted from the response signal in different states, and the clustering results are used to reflect the changes of machine tool modal parameters. After the amplitude of the frequency response function is normalized, the characteristics related to the natural frequency are acquired, and the clustering results further reflect the difference of the natural frequency of the signal. The new method based on clustering can be a standard and efficient method to characterize modal parameter changes in different states of machine tools.
url https://doi.org/10.1177/1687814020968323
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