矿用通风机叶片线激光测量数据优化方法研究

Research on Method of Data Optimization for 2D Laser Measurement of the Mining Ventilator Blade

测量模型的提取是矿用通风机叶片几何变形检测的关键,为了提高叶片测量模型的精度,提出一种基于最小二乘准则的线激光测量数据优化方法。首先,利用卡尔曼滤波算法实时处理线激光传感器原始测量数据,提高坐标测量精度,为后续模型构建提供精准数据;然后,采用三次多项式法、三次B样条法、高阶贝塞尔法分别拟合求解三个测量模型,并基于最小二乘准则评价各模型的显著性水平,从而优选出适合通风机叶片几何特征的测量模型;最后,进行了实验研究。结果表明,采用三次B样条函数构建叶片测量模型可以获得最佳的拟合效果,其显著性评价因子最小(小于0.05),本方法通过数据优化有效提高了叶片测量模型的精度,为后续通风机叶片几何评价提供一种有效的技术解决方案。

The extraction of measurement model is the key issue to detecting the geometric deformation of mining ventilator blades.In order to improve the accuracy of measurement model for the blade,a method of data optimization based on least squares principle for the 2D laser measurement is proposed.Firstly,the raw measurement data of the 2D laser sensor is processed based on Kalman filter algorithm in real-time,the accuracy of coordinate measurement is improved and the authentic data for subsequent model construction is provided.Then,three measurement models are solved respectively using methods of cubic polynomial,cubic B-spline,high-order Bessel,and the significance level of each model is evaluated based on the least squares principle,the measurement model suitable for the geometric characteristics of the ventilator blades is determined.Finally,the experimental research is implemented,and the results show that the best fitting effect can be achieved by establishing the measurement model of blade using cubic B-spline function,with the smallest significance evaluation factor(less than 0.05).The accuracy of the blade measurement model is effectively improved through data optimization,and provides an effective technical solution for subsequent geometric evaluation of fan blades.