煤矿低浓瓦斯传感器奇异信号辨识方法研究

Research on Identification Method of Singular Signal of Safety Monitoring System in Coal Mine

煤矿低浓瓦斯传感器奇异信号辨识是监控系统故障诊断的关键问题。运用小波降噪和傅里叶变换谱分析的方法对奇异信号的进行特征辨识。首先,选择合理阈值将运用小波分析将奇异信号进行滤波,提取低频段信号,然后,通过傅里叶变换对特征信号进行谱分析,得到信号的频域特征分布,从而进行奇异信号辨识。通过对模拟瓦斯监控系统平台加载奇异信号,实验结果表明所提出的辨识方法的正确率较高。

Singular signal recognition of low concentration of methane sensor is the key problem of the gas monito- ring system fault diagnosis. This paper uses wavelet noise reduction and the method of Fourier transform spectrum analysis for feature recognition of singular signal. First, wavelet analysis was applied to the original signal filte- ring to extract low frequency signal under the condition of reasonable selection threshold; and then, Fourier transform spectrum analysis was carried out on the feature of the singular signal to get its distribution in frequency domain; finally, the identification results were obtained. Through loading singular signal on simulating gas moni- toring platform, it is shown that the proposed identification method is of higher accuracy.