矿井瓦斯监测数据特征分析及预处理
发布时间：2018-08-13 19:11
【摘要】：针对矿井瓦斯监测数据包含异常数据、存在数据缺失及数据含噪等特征,提出了瓦斯监测数据预处理方法。首先利用移动平均线处理法或自回归模型处理法进行异常数据替代,然后采用三次指数平滑法补齐缺失数据,最后通过小波软阈值法进行数据消噪处理。实例分析表明,该方法可在不改变瓦斯监测数据统计特征的基础上,消除异常数据的干扰,保证监测数据的完整性,使监测数据表现特征平滑、波动性较小。
[Abstract]:Aiming at the characteristics of mine gas monitoring data including abnormal data, lack of data and data noise, the preprocessing method of gas monitoring data is put forward. First, the method of moving average line processing or autoregressive model processing is used to replace the abnormal data, then the cubic exponential smoothing method is used to correct the missing data, and finally the wavelet soft threshold method is used to remove the noise of the data. The analysis of examples shows that the method can eliminate the interference of abnormal data and ensure the integrity of monitoring data without changing the statistical characteristics of gas monitoring data, so that the characteristics of monitoring data are smooth and the volatility is low.
【作者单位】： 西安科技大学能源学院;西安科技大学教育部西部矿井开采及灾害防治重点实验室;天地(常州)自动化股份有限公司;
【基金】：国家自然科学基金资助项目(51104116) 西安科技大学博士启动基金资助项目(2012QDJ030)
【分类号】：TD712
，
本文编号：2181915
[Abstract]:Aiming at the characteristics of mine gas monitoring data including abnormal data, lack of data and data noise, the preprocessing method of gas monitoring data is put forward. First, the method of moving average line processing or autoregressive model processing is used to replace the abnormal data, then the cubic exponential smoothing method is used to correct the missing data, and finally the wavelet soft threshold method is used to remove the noise of the data. The analysis of examples shows that the method can eliminate the interference of abnormal data and ensure the integrity of monitoring data without changing the statistical characteristics of gas monitoring data, so that the characteristics of monitoring data are smooth and the volatility is low.
【作者单位】： 西安科技大学能源学院;西安科技大学教育部西部矿井开采及灾害防治重点实验室;天地(常州)自动化股份有限公司;
【基金】：国家自然科学基金资助项目(51104116) 西安科技大学博士启动基金资助项目(2012QDJ030)
【分类号】：TD712
，
本文编号：2181915
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