矿井瓦斯监测数据消噪方法

De-noising method of mine gas monitoring data

针对矿井瓦斯监测数据采用小波消噪容易剔除有效信号成分的问题,提出了一种基于希尔伯特-黄变换的矿井瓦斯监测数据消噪方法。该方法将原始瓦斯监测数据序列通过经验模态分解处理成若干固有模态函数分量的集合,进而通过Hilbert变换得到边际谱,依据原始瓦斯监测数据序列与各固有模态函数分量边际谱中的幅频关系来分析二者的相关性,确定噪声信号序列并剔除。实例分析表明,通过经验模态分解处理使得瓦斯监测数据序列在时间尺度上特征明显,易于识别信号的高频噪声部分,通过Hilbert谱分析,可消除瓦斯监测数据序列中的高频噪声信号,并保留原始瓦斯监测数据的本征特征,在实现消噪处理的同时避免信号失真,保持了瓦斯监测数据的真实性。

In order to solve problem of easily eliminating effective signal component of mine gasmonitoring data by wavelet de-noising,a de-noising method of mine gas monitoring data based on HilbertHuang transform was proposed.Original gas monitoring data sequence is decomposed into a set number of intrinsic mode function components by using empirical mode decomposition,and marginal spectrum is obtained through Hilbert transform.Correlation between the original sequence and each intrinsic mode function component is analyzed according to amplitude frequency relationship in the marginal spectrum between the original signal and each intrinsic mode function component,so as to determine and eliminate noise signal sequence.The case analysis shows that characteristics of the gas monitoring data is clear in time scale by empirical mode decomposition,which is good for identifing high frequency noise of the signal easily.The high frequency noise of gas monitoring data is eliminated through Hilbert spectrum analysis,and intrinsic characteristic of the original gas monitoring data is retained,which avoids signal distortion while de-noising is achieved,and maintains authenticity of the gas monitoring data.