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Shock and Vibration
Volume 2016, Article ID 1040942, 10 pages
Research Article

An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction

College of Mechanical Engineering & Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China

Received 19 June 2015; Revised 30 October 2015; Accepted 15 November 2015

Academic Editor: Toshiaki Natsuki

Copyright © 2016 Dongju Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.