HDP-HSMM的磨削声发射砂轮钝化状态识别*

在高精度金属材料磨削加工中,刀具即砂轮的状态对加工效率和加工质量具有重要的影响。钝化程度较高的砂轮不适于加工精密工件,需提前预警并修整更换砂轮。该文提出一种通过磨削声发射信号来检测砂轮钝化状态的方法。首先,对于采集到的信号进行小波软阈值降噪。然后,将其分割成多个有重叠的帧,并提取每帧信号的8个特征组成声发射数据集。最后,通过分层Dirichlet过程-隐半马尔可夫模型来建立声发射数据集和不同的砂轮钝化状态之间的非线性关系,旨在识别砂轮钝化状态。结果表明,上述检测方法能有效识别砂轮的不同钝化状态并能对整个加工过程中的砂轮钝化程度进行自动划分,其在测试数据集上的准确率达到93.7%,可以为实际工业应用提供理论指导。

The blunt state identification of acoustic emission for grinding wheel based on HDP-HSMM

In the grinding process, the different blunt state of the grinding wheel significantly affects the processing efficiency and quality. A seriously blunted grinding wheel would even lead to the occurrence of waste products. Therefore, attention has been aroused on how to monitor the blunt state of the grinding wheel in the grinding process. In this paper, an online monitoring method based on acoustic emission signal is proposed. Firstly, the signal collected by the acoustic emission sensor is de-noised by the wavelet soft threshold denoising method, following by the segmented analysis for dividing the denoised acoustic emission signal into multiple overlapping segments. In the second step, by setting a threshold voltage, the acoustic emission hits are intercepted for each frame of acoustic emission signal and 8 statistical features of each acoustic emission hit are extracted. The average value of 8 dimensional features of the acoustic emission hits in the frame is calculated to form the acoustic emission vector instead of the frame acoustic emission signal. In this way, the acoustic emission vectors of all frame acoustic emission signals are acquired to constitute the acoustic emission data set. Finally, the hierarchical Dirichlet processs - implicit semi Markov model is employed to build a nonlinear relationship between the acoustic emission data set and different grinding wheel blunt level. Good agreement is observed between the HDP-HSMM trained by the acoustic emission data set and our expectations, evidenced by the 93.7% accuracy of the trained model on the test data set. The results strongly prove that the method can effectively identify the different blunt state of grinding wheel accurately, which is of great value for industrial applications.