基于小波分解的油料火焰光谱特性分析研究
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摘要
针对油料火焰光谱特性研究不足的现状,通过构建全火焰红外测试系统,在室外开放空间条件下对多种油料及其他可燃物火焰的发射光谱进行了测试分析研究,光谱范围为1~14 mm。结果表明,蜂窝煤原始光谱信号最为特殊。其他燃料火焰光谱信号经db2小波5层分解后,92#汽油、95#汽油、0#柴油、航空煤油、润滑油火焰光谱低频分量特征相似,在1.2、3.4、4.5 mm附近存在较强的发射峰。各油料火焰光谱第5层细节系数重叠度较高。92#汽油、0#柴油火焰光谱低频分量及细节系数与其他燃料(木柴、酒精及纸张)相比特征明显。实验结论对基于光谱特性分析的油料火焰探测识别具有重要借鉴意义。
Due to the lack of adequate studies on the characteristics of infrared spectra of oil flame, an analytical study on flame spectra of various oil types and other combustible objects in outdoor space is carried out by establishing an all-flame infrared testing system with the spectral range of 1~14 mm. The results show that the signal of honeycomb briquette spectrum is the most special among all the spectra. After 5-layer decomposition of other fuel flame spectral signals by the db2 mother wavelet, low frequency components of 92# gasoline, 95# gasoline, 0#diesel oil, aviation kerosene and lube flame spectral features are similar and there exist strong emission peaks in the vicinity of 1.2, 3.4 and 4.5 mm. The 5thlayer detail coefficients of various fuel oil flame are in line with each other.The low frequency components and the detail coefficients of 92# gasoline and 0# diesel oil flame spectra have obvious features compared with those of other fuels(wood, alcohol and paper). The experimental conclusion is of great significance in the detection and identification of oil flame based on spectral characteristic analysis.
Due to the lack of adequate studies on the characteristics of infrared spectra of oil flame, an analytical study on flame spectra of various oil types and other combustible objects in outdoor space is carried out by establishing an all-flame infrared testing system with the spectral range of 1~14 mm. The results show that the signal of honeycomb briquette spectrum is the most special among all the spectra. After 5-layer decomposition of other fuel flame spectral signals by the db2 mother wavelet, low frequency components of 92# gasoline, 95# gasoline, 0#diesel oil, aviation kerosene and lube flame spectral features are similar and there exist strong emission peaks in the vicinity of 1.2, 3.4 and 4.5 mm. The 5thlayer detail coefficients of various fuel oil flame are in line with each other.The low frequency components and the detail coefficients of 92# gasoline and 0# diesel oil flame spectra have obvious features compared with those of other fuels(wood, alcohol and paper). The experimental conclusion is of great significance in the detection and identification of oil flame based on spectral characteristic analysis.
引文
1 Liu Ping.Research of Flame Recognition Method Based on Multi-Feature Fusion[D].Xi′an:Xi′an University of Architecture and Technology,2012:2-3.刘萍.基于多特征融合的火焰识别方法研究[D].西安:西安建筑科技大学,2012:2-3.
2 J A Fay.Model of large pool fires[J].Journal of Hazardous Materials,2006,136(2):219-232.
3 Xia Qing,Zuo Hongfu,Li Shaocheng,et al..Remote passive sensing of aeroengine exhausts using FTIR system[J].Spectroscopy and Spectral Analysis,2009,29(3):616-619.夏卿,左洪福,李绍成,等.航空发动机尾气的FTIR被动遥感[J].光谱学与光谱分析,2009,29(3):616-619.
4 Nie Wansheng,Yang Junhui,He Haobo,et al..The IR radiation characteristic of exhaust plume of the liquid rocket engine[J].Journal of National University of Defense Technology,2005,27(5):91-94.聂万胜,杨军辉,何浩波,等.液体火箭发动机尾喷焰红外辐射特性[J].国防科技大学学报,2005,27(5):91-94.
5 Cai Xiaoshu,Ji Kun,Zhao Zhijun.The measurement of radiation spectrum of flame of different fuel[J].Journal of Engineering Thermophysics,2004,25(1):171-173.蔡小舒,季锟,赵志军.不同种类燃料火焰的辐射光谱测量[J].工程热物理学报,2004,25(1):171-173.
6 J M Suo-Anttila,T K Blanchat,A J Ricks,et al..Characterization of thermal radiation spectra in 2 m pool fires[J].Proceedings of the Combustion Institute,2009,32(2):2567-2574.
7 P K Raj.LNG pool fire spectral data and calculation of emissive power[J].Journal of Hazardous Materials,2007,142(3):720-729.
8 Xiao Xizhong,Wang Yueming,Ma Jun,et al..Multispectral measurements of infrared spectral characteristics of an aircraft[J].Acta Optica Sinica,2014,34(10):1030002.肖喜中,王跃明,马骏,等.飞机红外光谱辐射特性的多光谱测量[J].光学学报,2014,34(10):1030002.
9 Chen Quansheng,Zhao Jiewen,Zhang Haidong,et al..Identification of authenticity of tea with near infrared spectroscopy based on support vector machine[J].Acta Optica Sinica,2006,26(6):933-937.陈全胜,赵杰文,张海东,等.基于支持向量机的近红外光谱鉴别茶叶的真伪[J].光学学报,2006,26(6):933-937.
10 Zhao Jiewen,Zhang Haidong,Liu Muhua.Preprocessing methods of near-infrared spectra for simplifying prediction model of sugar content of apples[J].Acta Optica Sinica,2006,26(1):136-140.赵杰文,张海东,刘木华.简化苹果糖度预测模型的近红外光谱预处理方法[J].光学学报,2006,26(1):136-140.
11 Shi Youming,Liu Gang,Liu Jianhong,et al..Identification of Auricularia auricular from different regions by Fourier transform infrared spectroscopy[J].Acta Optica Sinica,2007,27(1):129-132.时有明,刘刚,刘剑虹,等.不同产地黑木耳的傅里叶变换红外光谱鉴别[J].光学学报,2007,27(l):129-132.
12 Zhang Haihong,Zhang Shujuan,Wang Fenghua,et al..Study on fast discrimination of seabuckt horn juice varieties using visible-NIR spectroscopy[J].Acta Optica Sinica,2010,30(2):574-578.张海红,张淑娟,王凤花,等.应用可见-近红外光谱快速识别沙棘汁品牌[J].光学学报,2010,30(2):574-578.
13 Sun Tong,Wu Yiqing,Li Xiaozhen,et al..Discrimination of camellia oil adulteration by NIR spectra and subwindow permutation analysis[J].Acta Optica Sinica,2015,35(6):0630005.孙通,吴宜青,李晓珍,等.基于近红外光谱和子窗口重排分析的山茶油掺假检测[J].光学学报,2015,35(6):0630005.
14 Song Jia,Li Chenliang,Xing Gaoyang,et al..Study on analyzing active ingredient of Marasmius androsaceus via radial basis function neural network combining with near infrared spectroscopy[J].Acta Optica Sinica,2014,34(12):1230001.宋佳,李臣亮,邢高杨,等.径向基神经网络结合近红外光谱技术分析安络小皮伞发酵组分的研究[J].光学学报,2014,34(12):1230001.
15 Wu Shengnan,Chen Yongjie,Xing Zhenfang,et al..Synthesis,characterization,and luminescence properties of rare-earth europium aromatic carboxylate organic complexes with 1,10-phenanthroline[J].Acta Optica Sinica,2015,35(1):0116005.吴胜男,陈永杰,邢贞方,等.稀土铕-芳香羧酸-邻菲罗啉三元有机配合物的合成、表征及发光性能[J].光学学报,2015,35(1):0116005.
16 Zhang Su,Fu Qiang,Duan Jin,et al..Low contrast target polarization recognition technology based on lifting wavelet[J].Acta Optica Sinica,2015,35(2):0211002.张肃,付强,段锦,等.基于提升小波的低对比度目标偏振识别技术[J].光学学报,2015,35(2):0211002.
17 Li Qingzhong,Liu Qing.Adaptive enhancement algorithm for low illumination images based on wavelet transform[J].Chinese J Lasers,2015,42(2):0209001.李庆忠,刘清.基于小波变换的低照度图像自适应增强算法[J].中国激光,2015,42(2):0209001.
18 Yu Fengqin.Ten Lectures on Practical Wavelet Analysis[M].Xi′an:Xidian University Publishing House,2013:3-4.于凤芹.实用小波分析十讲[M].西安:西安电子科技大学出版社,2013:3-4.
2 J A Fay.Model of large pool fires[J].Journal of Hazardous Materials,2006,136(2):219-232.
3 Xia Qing,Zuo Hongfu,Li Shaocheng,et al..Remote passive sensing of aeroengine exhausts using FTIR system[J].Spectroscopy and Spectral Analysis,2009,29(3):616-619.夏卿,左洪福,李绍成,等.航空发动机尾气的FTIR被动遥感[J].光谱学与光谱分析,2009,29(3):616-619.
4 Nie Wansheng,Yang Junhui,He Haobo,et al..The IR radiation characteristic of exhaust plume of the liquid rocket engine[J].Journal of National University of Defense Technology,2005,27(5):91-94.聂万胜,杨军辉,何浩波,等.液体火箭发动机尾喷焰红外辐射特性[J].国防科技大学学报,2005,27(5):91-94.
5 Cai Xiaoshu,Ji Kun,Zhao Zhijun.The measurement of radiation spectrum of flame of different fuel[J].Journal of Engineering Thermophysics,2004,25(1):171-173.蔡小舒,季锟,赵志军.不同种类燃料火焰的辐射光谱测量[J].工程热物理学报,2004,25(1):171-173.
6 J M Suo-Anttila,T K Blanchat,A J Ricks,et al..Characterization of thermal radiation spectra in 2 m pool fires[J].Proceedings of the Combustion Institute,2009,32(2):2567-2574.
7 P K Raj.LNG pool fire spectral data and calculation of emissive power[J].Journal of Hazardous Materials,2007,142(3):720-729.
8 Xiao Xizhong,Wang Yueming,Ma Jun,et al..Multispectral measurements of infrared spectral characteristics of an aircraft[J].Acta Optica Sinica,2014,34(10):1030002.肖喜中,王跃明,马骏,等.飞机红外光谱辐射特性的多光谱测量[J].光学学报,2014,34(10):1030002.
9 Chen Quansheng,Zhao Jiewen,Zhang Haidong,et al..Identification of authenticity of tea with near infrared spectroscopy based on support vector machine[J].Acta Optica Sinica,2006,26(6):933-937.陈全胜,赵杰文,张海东,等.基于支持向量机的近红外光谱鉴别茶叶的真伪[J].光学学报,2006,26(6):933-937.
10 Zhao Jiewen,Zhang Haidong,Liu Muhua.Preprocessing methods of near-infrared spectra for simplifying prediction model of sugar content of apples[J].Acta Optica Sinica,2006,26(1):136-140.赵杰文,张海东,刘木华.简化苹果糖度预测模型的近红外光谱预处理方法[J].光学学报,2006,26(1):136-140.
11 Shi Youming,Liu Gang,Liu Jianhong,et al..Identification of Auricularia auricular from different regions by Fourier transform infrared spectroscopy[J].Acta Optica Sinica,2007,27(1):129-132.时有明,刘刚,刘剑虹,等.不同产地黑木耳的傅里叶变换红外光谱鉴别[J].光学学报,2007,27(l):129-132.
12 Zhang Haihong,Zhang Shujuan,Wang Fenghua,et al..Study on fast discrimination of seabuckt horn juice varieties using visible-NIR spectroscopy[J].Acta Optica Sinica,2010,30(2):574-578.张海红,张淑娟,王凤花,等.应用可见-近红外光谱快速识别沙棘汁品牌[J].光学学报,2010,30(2):574-578.
13 Sun Tong,Wu Yiqing,Li Xiaozhen,et al..Discrimination of camellia oil adulteration by NIR spectra and subwindow permutation analysis[J].Acta Optica Sinica,2015,35(6):0630005.孙通,吴宜青,李晓珍,等.基于近红外光谱和子窗口重排分析的山茶油掺假检测[J].光学学报,2015,35(6):0630005.
14 Song Jia,Li Chenliang,Xing Gaoyang,et al..Study on analyzing active ingredient of Marasmius androsaceus via radial basis function neural network combining with near infrared spectroscopy[J].Acta Optica Sinica,2014,34(12):1230001.宋佳,李臣亮,邢高杨,等.径向基神经网络结合近红外光谱技术分析安络小皮伞发酵组分的研究[J].光学学报,2014,34(12):1230001.
15 Wu Shengnan,Chen Yongjie,Xing Zhenfang,et al..Synthesis,characterization,and luminescence properties of rare-earth europium aromatic carboxylate organic complexes with 1,10-phenanthroline[J].Acta Optica Sinica,2015,35(1):0116005.吴胜男,陈永杰,邢贞方,等.稀土铕-芳香羧酸-邻菲罗啉三元有机配合物的合成、表征及发光性能[J].光学学报,2015,35(1):0116005.
16 Zhang Su,Fu Qiang,Duan Jin,et al..Low contrast target polarization recognition technology based on lifting wavelet[J].Acta Optica Sinica,2015,35(2):0211002.张肃,付强,段锦,等.基于提升小波的低对比度目标偏振识别技术[J].光学学报,2015,35(2):0211002.
17 Li Qingzhong,Liu Qing.Adaptive enhancement algorithm for low illumination images based on wavelet transform[J].Chinese J Lasers,2015,42(2):0209001.李庆忠,刘清.基于小波变换的低照度图像自适应增强算法[J].中国激光,2015,42(2):0209001.
18 Yu Fengqin.Ten Lectures on Practical Wavelet Analysis[M].Xi′an:Xidian University Publishing House,2013:3-4.于凤芹.实用小波分析十讲[M].西安:西安电子科技大学出版社,2013:3-4.