Acoustic Emission Intelligent Monitoring of Diamond Grinding Wheel Wear Based on Empirical Mode Decomposition
In view of the existing problem in the wavelet analysis of acoustic emission signals in wear state of diamond grinding wheel， because engineering ceramics partially stabilized zirconia grinding acoustic emission signals have nonlinear and nonstationary characteristics， using empirical mode decomposition method the acoustic emission signals were decomposed into several stationary intrinsic mode functions and then the root mean squares， variances and energy coefficients were extracted. When the wear state of diamond grinding wheel changes from mild wear to severe wear， the root mean squares（IMFrms） and variances（IMFvar） of the intrinsic mode function increase， and the energy coefficients（IMFpe） change significantly. As the input parameter of the least squares support vector machine， the wear state of diamond grinding wheel was successfully monitored.
Keywords: partially stabilized zirconia grinding, diamond grinding wheel wear state monitoring, acoustic emission, empirical mode decomposition, least squares support vector machine
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