Study on stochastic resonance gas weak signal detection
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摘要: 为了解决检测煤矿复杂环境中的瓦斯信号时易受周围噪声干扰以至微弱信号被掩埋或产生异常数据的问题,提出一种基于随机共振的微弱瓦斯信号检测方法。采用欠采样原理对大频率信号尺度变换及粒子群算法优化系统结构参量,对大参量微弱信号在随机共振系统中的共振效果进行了理论分析和研究。结果表明,该方法可以以较低的采样频率,自适应地达到较好的共振效果;可有效地滤除噪声并增强系统辨识微弱信号的灵敏度以及信号检测的动态范围。该研究为瓦斯突出信息的早期辨识提供了一定的理论依据。Abstract: In order to detect the gas signals in complex environments of coal mine and solve the problem of the buried weal signal and the abnormal data because of surrounding noise interference on gas signal, a detection method for weak gas signal was introduced based on stochastic resonance. Sub-sampling method was used to transform large frequency signal scale and particle swarm optimization algorithm was used to optimize structural parameters. The resonance effect of large-parameter weak signal in a stochastic resonance system was analyzed. The results show that optimum matching between the nonlinear system, the input signal and the noise could be achieved adaptively with lower sampling frequency. The large-parameter multi-frequency weak signal can be distinguished from strong background noise effectively, and the detection sensitivity and dynamic range are enhanced. The research provides theory basic for early identification of gas outburst information.
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