基于EMD的金刚石砂轮磨损状态声发射监测

Acoustic Emission Intelligent Monitoring of Diamond Grinding Wheel Wear Based on Empirical Mode Decomposition

针对磨削金刚石砂轮磨损状态声发射信号小波分析中存在的问题,根据工程陶瓷部分稳定氧化锆磨削过程中声发射信号非线性非平稳性的特点,采用经验模态分解方法将磨削声发射信号分解为多个平稳的固有模态函数之和,并提取其有效值、方差和能量系数等特征值.在磨削金刚石砂轮从轻度磨损状态转变为严重磨损状态时,固有模态函数的有效值(IMFrms)和方差(IMFvar)增大,而能量系数(IMFpe)发生明显的变化;将其做为最小二乘支持向量机的输入参数,对金刚石砂轮的轻度磨损状态和严重磨损状态成功地进行了智能监测.

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.