主动式火灾光电探测信号基线漂移滤除和去噪方法研究

Research on the baseline drift filtering and denoising methods for active fire photoelectric detection signal

针对数据中心机柜的主动式火灾光电探测报警系统降低误报率和提高检测精度的需求,设计极早期火灾探测腔体结构,完成光电检测数字电路硬件,采集光电信号,分别采用集合经验模态分解(EEMD)算法和小波分析算法来滤除低频基线漂移和降低背景白噪声。经过对比计算处理后信号的相关性和信噪比,结果表明,在本文所述数据采集密度下,EEMD方法的性能明显优于小波分析方法,可有效地降低误报率和提高检测精度,实现对火灾的“极早期”探测和预警。

In order to reduce the false alarm rate and improve the detection accuracy,aiming at the suction smoke sensor system of data center cabinet,a very early fire detection cavity structure was designed.The photoelectric detection digital circuit hardware was completed,and the photoelectric signal was collected.The ensemble empirical mode decomposition(EEMD)algorithm and wavelet analysis algorithm were used respectively to filter the baseline shift and reduce the background noise.After comparing and calculating the correlation and signalto-noise ratio,the results show that the performance of the EEMD method is significantly better than the wavelet analysis method,which can effectively reduce the false alarm rate and improve the detection accuracy,and realize the"very early"detection and warning of fire.