批量钻削监测信号双谱特征融合研究及应用

Research and Application of Bi-spectrum Features Fusion Based on Batch Drilling Monitoring Signals

基于钻削工步质量波动与监测信号特征变化之间的耦合现象,提出一种基于监测信号双谱特征的高精度批量钻削工步质量一致性控制检测方法。认为正常钻削过程的声发射和三向加速度振动监测信号可视为随机过程,满足或近似高斯分布,信号偏离高斯分布的程度与各钻孔加工质量波动间存在对应关系;以各钻孔声发射和加速度振动监测信号为研究对象,提取各钻孔监测信号的双谱幅值平均值为特征,对不同钻削情况下信号偏离高斯分布的程度进行定量分析;采用基于Relief F算法的特征加权模糊聚类分析,进行基于监测信号双谱幅值均值特征矩阵的钻孔质量分类,并与人工检测的工步质量一致性结果对比分析。计算和分析结果表明,监测信号双谱特征与各钻削工步质量之间存在有机联系,对信号双谱特征进行融合聚类可分析批量钻削工步质量的一致性。

Based on the coupling phenomena between the batch drilling process quality fluctuation and monitoring signals features changes, a kind of signals feature extraction method based on bi-spectrum feature is proposed to solve the quality consistency control and testing problem of the high-precision batch drilling step. Theoretically, the acoustic emission signal and acceleration vibration signal of a normal drilling process is viewed as a stochastic process to meet with Gaussian distribution, and there could be a corresponding relationship between the deviation degree from Gaussian distribution of signal and the drilling step quality fluctuation. The acoustic emission signals and acceleration vibration signals of batch drilling are taken as research objects, the quantitative analysis of the deviation degree from Gaussian distribution under different conditions could be expressed by the average bi-spectrum amplitude of each signal. The ReliefF algorithm is used to assign the weights for every feature, these step quality classification is performed using feature weighted fuzzy cluster algorithm and to be contrast with the manual detection result. The results show that there are organic connections between the bi-spectrum feature of monitoring signals and drilling step quality, and the consistency quality testing of batch drilling step is realized by fusion of clustering bi-spectrum features.