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Grinding is known as the most complicated material removal process and the method for monitoring the grinding wheel wear has
its own characteristics comparing with the approaches for detecting the wear on regular cutting tools. Research efforts were
made to develop the wheel wear monitoring system due to its significance in grinding process. This paper presents a novel
method for identification of grinding wheel wear signature by combination of wavelet packet decomposition (WPD) based energies.
The distinctive feature of the method is that it takes advantage of the combinational information of the decomposed frequency
components based on the WPD so the extracted features can be customized according to the specific monitored object to get
better diagnosis effects. Experiments are researched on monitoring of grinding wheel wear states under different machining
conditions. The results show that the energy ratio extracted from the measured vibration signals is consistent with the grinding
wheel wear condition evaluated by experiment and the further extracted feature ratio can be used in prediction of wheel wear
condition. 相似文献
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