煤矿瓦斯传感器人工调校噪声数据处理方法

Manual adjustment noise data processing method for coal mine gas sensor

传统噪声数据处理方法对输入数据有一定的要求,且运行时间较长。而煤矿瓦斯传感器人工调校噪声数据存在数量少、质量差、时间不一致、易受环境影响等问题,采用传统噪声处理方法难以滤除该噪声数据。针对上述问题,提出了一种煤矿瓦斯传感器人工调校噪声数据处理方法。采用数据平均值填充煤矿瓦斯传感器浓度数据缺失值;采用多时间粒度构建煤矿瓦斯传感器浓度数据的特征集和样本集;采用高斯函数、混合高斯函数、二项式函数、三项式函数、分段二项式函数5种曲线拟合函数拟合人工调校噪声数据,并基于最小二乘法确定拟合函数参数,根据拟合效果得到最优的拟合函数;通过分析人工调校噪声数据得出该噪声数据与瓦斯浓度上升的斜率、峰值、调校前后浓度差等基本特征有关,根据这些基本特征识别出人工调校噪声数据并删除。实验结果验证了该方法的有效性。

Traditional noise data processing methods have certain requirements for input data,and have a long running time.However,there are some problems of manual adjustment noise data of coal mine gas sensor such as less quantity,poor quality,inconsistent time and easy to be affected by environment,it is difficult to filter the noise data by traditional noise processing methods.For the above problems,a manual adjustment noise data processing method for coal mine gas sensor is proposed.The data average value is used to fill the missing value of concentration data of coal mine gas sensor;the feature set and sample set of concentration data of coal mine gas sensor are constructed by using multi time granularity;five curve fitting functions,namely Gaussian function,mixed Gaussian function,binomial function,trinomial function and piecewise binomial function,are used to fit manual adjustment noise data,and parameters of the fitting function are determined based on the least square method,and the optimal fitting function is obtained according to fitting effect;through analysis of manual adjustment noise data,it is concluded that the noise data is related to the slope,peak and difference of gas concentration before and after adjustment,according to these basic characteristics,the manual adjustment noise data is identified and deleted.The experimental results verify effectiveness of the method.