|
|
|
|
|
|
| Detection of Trace Methane Gas Concentration Based on 1D-WCWKCNN |
| KAN Ling-ling, ZHU Fu-hai, LIANG Hong-wei* |
School of Electrical Information Engineering，Northeast Petroleum University，Daqing 163318，China
|
|
|
|
|
Abstract In detecting methane concentration by tunable laser absorption spectroscopy (TDLAS), the second harmonic signal amplitude of methane transmitted light intensity is directly proportional to the concentration of trace methane gas. How to accurately and quickly screen the amplitude of the second harmonic signal of the target methane transmitted light intensity is crucial. The photodetector obtains the 1 000 methane gas transmitted light intensity signal samples, and it is demodulated to obtain the second harmonic signal. When obtaining a variety of trace methane gas transmitted light intensity and demodulating the second harmonic signal by transmitted light intensity, noise and artificial operation affect the amplitude of the second harmonic signal，resulting in an increase in the time for manual screening of the second harmonic signal. Using traditional TDLAS technology to screen trace methane second harmonic signals had the problem of high time cost. A trace methane concentration detection method based on wide convolution and wide kernel 1D convolutional neural networks (1D-WCWKCNN) was proposed. Firstly, the 1D-WCWKCNN model is trained with the help of the methane gas dataset, and the model parameters are continuously adjusted according to the training results. Secondly, the method used a wide convolution layer and wide convolution kernel 1D convolution layer to extract the features of the trace methane second harmonic signal so that the network obtained the characteristic relationship between a longer sequence and the sequence boundary information in the methane concentration signal and the gas concentration after one convolution. The second harmonic signal of methane transmitted light intensity is extracted through the 6-layer convolutional layer to extract the main characteristics of the relationship between the signal and methane gas concentration. The 6-layer maximal pooling layer retains the main characteristics. The Flatten layer processes the signal data processed by the previous layer in one dimension. Finally, the trained 1D-WCWKCNN model outputs trace methane gas concentration through the Dense layer. The trace methane gas concentration detection model based on 1D-WCWKCNN replaces manually screening second harmonic signals for detecting trace methane gas concentration in a fitted straight line in TDLAS technology. The effectiveness of this method is verified in actual experiments. The results show that it can effectively detect the concentration of trace methane in 50~1 000 mg·L-1, and its accuracy reaches 99.85%. Compared with other methods, it has strong signal feature extraction ability and high detection accuracy of methane gas.This method facilitates the screening of gas concentration signals to be measured in the field of gas detection.
|
|
Received: 2022-08-12
Accepted: 2022-11-14
|
|
|
|
Corresponding Authors:
LIANG Hong-wei
E-mail: lianghongwei@nepu.edu.cn
|
|
[1] YANG Dong-shang，ZENG Yi，XI Liang，et al(杨东上，曾 议，奚 亮，等). Acta Optica Sinica(光学学报)，2020，40(5): 0501002.
[2] REN Hong-mei，LI Ang，HU Zhao-kun，et al(任红梅，李 昂，胡肇焜，等). Acta Physica Sinica(物理学报)，2020，69(20)：204204.[3] HUANG Ye-yuan，LI Ang，QIN Min，et al(黄业园，李 昂，秦 敏，等). Acta Optica Sinica(光学学报)，2021，41(10)：1030002.
[4] CAI Hao-ze，BU Ling-bing，GONG Yu，et al(蔡镐泽，卜令兵，龚 宇，等). Acta Photonica Sinica(光子学报)，2019，48(7)：701001.
[5] WANG Yu-hao，LIU Jian-guo，XU Liang，et al(王钰豪，刘建国，徐 亮，等). Acta Physica Sinica(物理学报)，2022，71(9)：093201.
[6] DING Wu-wen，SUN Li-qun，YI Lu-ying(丁武文，孙利群，衣路英). Acta Physica Sinica(物理学报)，2017，66(10)：100702.
[7] Shao L G，Fang B，Zheng F，et al. Spectrochimica Acta A: Molecular and Biomolecular Spectroscopy，2019，222：117118.
[8] Li G L，Ma K，Jiao Y，et al. Microwave and Optical Technology Letters，2021，63(4)：1147.
[9] Jiang C L，Liu Y F，Yu B，et al. Review of Scientific Instruments，2018，89(8)：083106.
[10] Jiang J，Wang Z W，Han X，et al. IEEE Transactions on Dielectrics and Electrical Insulation，2019，26(1)：153.
[11] Li B，Xue L，Ji N，et al. International Journal of Optics，2021，2021：8829790.
[12] Jiang J，Zhao M X，Ma G M，et al. IEEE Sensors Journal，2018，18(6)：2318.
[13] JI Wen-hai，LÜ Xiao-cui，HU Wen-ze，et al(季文海，吕晓翠，胡文泽，等). Optics and Precision Engineering (光学精密工程)，2018，26(8)：1837.
[14] ZHOU Fei-yan，JIN Lin-peng，DONG Jun(周飞燕，金林鹏，董 军). Chinese Journal of Computers(计算机学报)，2017，40(6)：1229.
[15] Zhao X J，Wen Z H，Pan X F，et al. IEEE Access，2019，7：12630.
[16] Zhang T J. International Journal of Computational Intelligence Systems，2020，13(1)：1198.
[17] Peng P，Zhao X J，Pan X F，et al. Sensors，2018，18(1)：157.
[18] Wei G F，Li G，Zhao J，et al. Sensors，2019，19(1)：217.
[19] HAO Hui-min，LIANG Yong-guo，WU Hai-bin，et al(郝惠敏，梁永国，武海彬，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2021，41(3)：782.
[20] WANG Xiu-fang，SUN Shuang，DING Chun-yang(王秀芳，孙 双，丁春阳). Journal of Jilin University (Engineering and Technology Edition)[吉林大学学报(工学版)]，2022，52(2)：310.
[21] Zhang Y Q，Miyamori Y，Mikami S，et al. Computer‐Aided Civil and Infrastructure Engineering，2019，34(9)：822.
[22] Van Der Linde A，Tutz G. Statistics，2008，42(1)：4.
|
|
|
|
