基于小波包能量分析的红外火焰信号识别  

Infrared Flame Signal Recognition Based on Wavelet Packet Energy Analysis

作　　者：周永杰[1] 余震虹[1] 张赵良 ZHOU Yongjie YU Zhenhong ZHANG Zhaoliang(College oflnternet of Things Engineering, Jiangnan University, Wuxi 214122, China Wuxi GLT Safety Equipment Co., Ltd, Wuxi 214073, China)

机构地区：[1]江南大学物联网工程学院,江苏无锡214122 [2]无锡格林通安全装备有限公司,江苏无锡214073

出　　处：《红外技术》2017年第3期232-236,共5页Infrared Technology

摘　　要：在设计红外火焰探测器的过程中,人工光源常常会引起探测器的误报。为有效区分人工光源与火焰信号,本文首先对采集的1种人工光源以及3种火焰信号进行分析,将采集的信号进行小波包4层分解,得到信号的能量谱。通过分析发现选取第2,3,4,7频段的能量值能够将这4种信号有效区分。为进一步验证实验结果,本文将双通道两路信号的第2,3,4,7频段的8个能量值作为一组特征向量,与BP神经网络结合进行模式识别。结果表明,通过这样的方法不仅可以区分火焰和人工光源,同时可以对3种火焰进行识别,其识别的正确率为84.1%。因此,基于小波包能量分析的方法提取这8个能量值作为特征值具有一定的可行性,能有效减少人工光源引起的误报,同时为火焰种类的识别以及以后的灭火自动化提供了新的可能性。In process of design of infrared flame detector, the recognition rate of which was reduced by some artificial lights. In order to distinguish artificial light and flame, an artificial light and three kinds of flame were analyzed firstly. The collected signals were decomposed by wavelet packet, then the wavelet packet energy spectrum was acquired. The results showed that the four kinds of signal can be distinguished by the energy of 2,3,4 and7 frequency band. In order to verify the experimental results, the eight energy values of the 2,3,4,7 spectrum from two sensors were regarded as a group of feature vector. The mode of recognition was carried out by applying BP neural network and the feature vector. The results showed that the method can distinguish the artificial light and the flame. In addition, the three kinds of flame were also identified. The recognition rate can reach 84.1%. Therefore, the method based on wavelet packet energy analysis has certain feasibility, which can increase the accuracy of flame identification. It also provides a new possibility to put out the fire automatically in the feature.

关 键 词：红外火焰探测器 小波包 BP神经网络 特征值 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]