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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2020-10-01
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814020968323 |
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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 |
work_keys_str_mv |
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