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An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction
Joint Authors
Chen, Dongju
Zhou, Shuai
Dong, Lihua
Fan, Jinwei
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-06
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This paper presents a new identification method to identify the main errors of the machine tool in time-frequency domain.
The low- and high-frequency signals of the workpiece surface are decomposed based on the Daubechies wavelet transform.
With power spectral density analysis, the main features of the high-frequency signal corresponding to the imbalance of the spindle system are extracted from the surface topography of the workpiece in the frequency domain.
With the cross-correlation analysis method, the relationship between the guideway error of the machine tool and the low-frequency signal of the surface topography is calculated in the time domain.
American Psychological Association (APA)
Chen, Dongju& Zhou, Shuai& Dong, Lihua& Fan, Jinwei. 2016. An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction. Shock and Vibration،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1118761
Modern Language Association (MLA)
Chen, Dongju…[et al.]. An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction. Shock and Vibration No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1118761
American Medical Association (AMA)
Chen, Dongju& Zhou, Shuai& Dong, Lihua& Fan, Jinwei. An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1118761
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118761