磨削功率信号采集与动态功率监测数据库建立方法

Methods of grinding power signal acquisition and dynamic power monitoring database establishment

以PPC−3功率传感器和NI 9203数据采集卡搭建磨削功率监控实验平台,基于LabVIEW软件开发过程监测功率数据驱动的智能磨削工艺决策系统,促进磨削加工的绿色高效智能化。为克服决策系统底层过程监测数据(即在线采集的磨削动态功率信号)的数据量巨大且混入有噪声、典型特征不明显等问题,提出一种磨削功率信号特征提取和关系型数据库建立方法。采用Ⅱ型切比雪夫低通滤波器滤波,提高磨削功率信号的信噪比,基于寻峰寻谷法提取功率信号峰谷特征点并进行时域标记,且为保证磨削功率数据的完整性及精度进行首尾修正及插值修正。同时,基于二值化对磨削加工过程进行工作状态标记,并将动态流数据转换为字符串存储在关系型数据库单元格中。轴承钢磨削实验结果表明:数据库建立方法能够精确提取磨削功率特征,并将2090000个动态数据点转变为2×52998个单元格数据,其数据量降至原数据的5.07%,数据的存储规模显著降低,磨削功率数据库的访问速度加快。

The grinding power monitoring experimental platform was built with PPC−3 power sensor and NI 9203 acquisition card.An intelligent grinding process decision-making system driven by monitored power data was developed based on LabVIEW software to promote green,efficient and intelligent grinding.In order to overcome the problems of huge amount of bottom process monitoring data(i.e.grinding dynamic power signals collected online)of the decision-making system,mixture with noise and unclear typical characteristics,a method of feature extraction of grinding power signals and establishment of relational database is proposed.The typeⅡChebyshev low-pass filter was used to filter and improve the signal-to-noise ratio of grinding power signals.The peak and the valley characteristic points of power signals were extracted and marked in time domain based on the peak and the valley searching method,and the head and the tail correction and interpolation correction were carried out to ensure the integrity and accuracy of grinding power data.At the same time,the working state of grinding process was marked based on binarization,and the dynamic flow data was converted into string and stored in the cells of relational database.The grinding test results of bearing steel show that the database establishment method can accurately extract the grinding power characteristics and transform 2090000 dynamic data points into 2×52998 cell data,the data volume is reduced to 5.07%of the source data,which significantly reduces the storage scale of data and speeds up the access speed of grinding database.