An Effective EMD-Based Feature Extraction Method for Boring Chatter Recognition

Chatter often occurs during precision hole boring, it results in low quality of finished surface and even damages the cutting tool. In order to identify chatter rapidly and gain the precious time for chatter suppression, a chatter monitoring system was established and an effective feature extraction method for boring chatter recognition was presented. According to the characteristic of chatter signal, empirical mode decomposition (EMD) was introduced into chatter feature extraction, and its basic theories were investigated. The vibration signal was decomposed by EMD, then the intrinsic mode functions (IMF) was got. Finally, the feature of chatter symptom was extracted by analyzing the energy spectrum of each IMF. The results show that feature extracted from vibration of boring bar by EMD can indicate chatter outbreak symptom, and it can be used as feature vectors for rapidly recognizing chatter.

[1]  G. Pegram,et al.  Empirical Mode Decomposition in 2-D space and time: a tool for space-time rainfall analysis and nowcasting , 2005 .

[2]  Dejie Yu,et al.  Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings , 2005 .

[3]  Marcus Dätig,et al.  Performance and limitations of the Hilbert–Huang transformation (HHT) with an application to irregular water waves , 2004 .

[4]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[6]  Zichen Chen,et al.  Magnetorheological fluid-controlled boring bar for chatter suppression , 2009 .

[7]  M. S. Woolfson,et al.  Application of empirical mode decomposition to heart rate variability analysis , 2001, Medical and Biological Engineering and Computing.

[8]  Zichen Chen,et al.  On-line chatter detection and identification based on wavelet and support vector machine , 2010 .

[9]  J. Cusido,et al.  Fault detection by means of Hilbert Huang Transform of the stator current in a PMSM with demagnetization , 2010, 2007 IEEE International Symposium on Intelligent Signal Processing.

[10]  Brendan Walsh.  Seismic signal processing for single well imaging applications , 2007 .

[11]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[12]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.