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Appl. Sci. 2017, 7(2), 145; doi:10.3390/app7020145

The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection

1
Zhejiang Province’s Key Laboratory of Reliability Technology for Mechanical and Electrical Product, Hangzhou 310018, China
2
Hangzhou Hangji Machine Tool Co., Ltd., Hangzhou 311305, China
*
Author to whom correspondence should be addressed.
Academic Editor: Gangbing Song
Received: 2 November 2016 / Revised: 23 December 2016 / Accepted: 25 January 2017 / Published: 8 February 2017
View Full-Text   |   Download PDF [4384 KB, uploaded 8 February 2017]   |  

Abstract

Grinding chatter reduces the long-term reliability of grinding machines. Detecting the negative effects of chatter requires improved chatter detection techniques. The vibration signals collected from grinders are mainly nonstationary, nonlinear and multidimensional. Hence, bivariate empirical mode decomposition (BEMD) has been investigated as a multiple signal processing method. In this paper, a feature vector extraction method based on BEMD and Hilbert transform was applied to the problem of grinding chatter. The effectiveness of this method was tested and validated with a simulated chatter signal produced by a vibration signal generator. The extraction criterion of true intrinsic mode functions (IMFs) was also investigated, as well as a method for selecting the most ideal number of projection directions using the BEMD algorithm. Moreover, real-time variance and instantaneous energy were employed as chatter feature vectors for improving the prediction of chatter. Furthermore, the combination of BEMD and Hilbert transform was validated by experimental data collected from a computer numerical control (CNC) guideway grinder. The results reveal the good behavior of BEMD in terms of processing nonstationary and nonlinear signals, and indicating the synchronous characteristics of multiple signals. Extracted chatter feature vectors were demonstrated to be reliable predictors of early grinding chatter. View Full-Text
Keywords: bivariate empirical mode decomposition (BEMD); Hilbert transform; multiple signals; synchronous characteristic; real-time variance; instantaneous energy bivariate empirical mode decomposition (BEMD); Hilbert transform; multiple signals; synchronous characteristic; real-time variance; instantaneous energy
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Chen, H.; Shen, J.; Chen, W.; Wu, C.; Huang, C.; Yi, Y.; Qian, J. The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection. Appl. Sci. 2017, 7, 145.

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