Vibration Status Monitoring of Machine Center Based on EMD and LTSA

As the abnormal conditions of manufacturing process seriously affect the machining performance of machine tool, the vibration signal of spindle is selected to monitor the manufacturing process of machine center. The vibration signals are decomposed by empirical mode decomposition (EMD) method, and the first five intrinsic module functions components are picked out to calculate the power spectrums. Then, the local tangential space arrangement (LTSA) method is developed for dimension reduction, and the one-dimensional feature vector indicating the vibration state is obtained. A support vector machine model is used to classify vibration states based on one-dimensional critical features of three different manufacturing processes. The classification result indicates that the EMD-LTSA method is an efficient feature extraction method for vibration status monitoring of machine tools.

[1]  I. Tsiafis,et al.  Experimental Analysis of the Effect of Vibration Phenomena on Workpiece Topomorphy Due to Cutter Runout in End-Milling Process † , 2018 .

[2]  Kilian Wasmer,et al.  Deep Learning for In Situ and Real-Time Quality Monitoring in Additive Manufacturing Using Acoustic Emission , 2019, IEEE Transactions on Industrial Informatics.

[3]  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.

[4]  Robert X. Gao,et al.  PCA-based feature selection scheme for machine defect classification , 2004, IEEE Transactions on Instrumentation and Measurement.

[5]  Baoping Tang,et al.  Rotating machine fault diagnosis using dimension reduction with linear local tangent space alignment , 2013 .

[6]  Xinghua Li,et al.  Study on Vibration Characteristics of Natural Gas Pipeline Explosion Based on Improved MP-WVD Algorithm , 2018, Shock and Vibration.

[7]  Mahmoud Omid,et al.  Feature-level fusion based on wavelet transform and artificial neural network for fault diagnosis of planetary gearbox using acoustic and vibration signals , 2013 .

[8]  Michela Simoncini,et al.  Frictional behaviour of AA7075-O aluminium alloy in high speed tests , 2017 .

[9]  Hongrui Cao,et al.  Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators , 2015 .

[10]  Min Wan,et al.  Dynamic damping of machining vibration: a review , 2017 .

[11]  Yue Wu,et al.  Model predictive control to mitigate chatters in milling processes with input constraints , 2015 .

[12]  E. García Plaza,et al.  Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations , 2018 .

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

[14]  Gang Li,et al.  Modified power prediction model based on infinitesimal cutting force during face milling process , 2018 .

[15]  Joshua H. Gordis,et al.  Efficient Transient Analysis for Large Locally Nonlinear Structures , 1999 .

[16]  Yan Yang,et al.  A STEP-based machining data model for autonomous process generation of intelligent CNC controller , 2018 .