一种声学层析成像温度分布高分辨率重建方法

doi:10.16182/j.issn1004731x.joss.21-0447

摘要/Abstract

摘要：

准确测量温度分布对工业生产具有重要的意义。针对声学层析成像中有限的网格划分数目会影响重建精度的问题，提出TR-RBF(tikhonov regularization-radial basis function)重建算法对温度场进行高分辨率重建。采用Tikhonov正则化对超声飞行时间(time of flight, TOF)重建，得到粗网格下的温度分布，并用局部加权回归法对数据进行平滑处理，进而采用RBF神经网络将粗解进行预测得到细化后的温度分布。通过有噪声和无噪声的数值仿真，本算法与ART、SVD和Tikhonov三种算法相比，在典型峰型温度分布情况下的重建精度提升明显且抗噪性最好。

关键词: 声学层析成像, 高分辨率温度重建, Tikhonov正则化, RBF神经网络, 局部加权回归, 预测

Abstract:

Accurate measurement temperature distribution is important for industrial production. In order to solve the number of mesh divisions will impact reconstruction accuracy in acoustic tomography, the TR-RBF (Tikhonov regularization-radial basis function) reconstruction algorithm is rebuilt to reconstruct the temperature field with high resolution. The Tikhonov regularization is used to reconstruct the ultrasound time of flight (TOF) to obtain a temperature distribution on coarse grids, and use local weighted regression method to smooth processing; use RBF neural networks to predict the temperature distribution on fine grids. Through numerical simulation with and without noise, compared with ART,SVD and Tikhonov, the proposed algorithm improves the reconstruction accuracy greatly and has the best anti-noise performance in the case of typical peak temperature.

Key words: acoustic tomography, high resolution temperature reconstruction, Tikhonov regularization, RBF neural networks, local weighted regression, prediction

中图分类号:

TP391.9

张立峰, 苗雨. 一种声学层析成像温度分布高分辨率重建方法[J]. 系统仿真学报, 2022, 34(09): 2065-2073.

Lifeng Zhang, Yu Miao. A High Resolution Reconstruction Method of Temperature Distribution in Acoustic Tomography[J]. Journal of System Simulation, 2022, 34(09): 2065-2073.

图/表 15

图1

图2

图3

表1

图4

表2

图5

表3

图6

图7

图8

图9

表4

表5

表6

参考文献 22

[1]	Yan Hua, Ma Zhao, Zhou Yinggang. Acoustic Tomography System for Online Monitoring of Temperature Fields[J]. IET Science, Measurement and Technology (S1751-8822), 2017, 11(5): 623-630.
[2]	Bramanti M, Salerno E A. An Acoustic Pyrometer System for Tomographic Thermal Imaging in Power Plant Boilers[J]. IEEE Transactions on Instrumentation and Measurement (S0018-9456), 1996, 45(1): 159-167.
[3]	田丰, 邵富群, 王福利, 等. 基于弯曲路径的复杂温度场重建算法仿真研究[J]. 系统仿真学报, 2003, 15(5): 621-623.
	Tian Feng, Shao Fuqun, Wang Fuli, et al. Simulation Research on Complex Temperature Field Reconstruction Algorithm Based on Bending Path[J]. Journal of System Simulation, 2003, 15(5): 621-623.
[4]	Ohyama S, Takayama J, Oshima K. Acoustic CT System for Temperature Distribution and Wind Velocity Vector Measurement[C]// IEEE 2008 Congress on Image and Signal Processing, CISP. Sanya, China: IEEE, 2008: 13-17.
[5]	颜华, 崔柯鑫, 续颖. 基于少量声波飞行时间数据的温度场重建[J]. 仪器仪表学报, 2010, 31(2): 470-475.
	Yan Hua, Cui Kexin, Xu Ying. Reconstruction of Temperature Field Based on Small Sonic Time of Flight Data[J].Chinese Journal of Scientific Instrument, 2010, 31(2): 470-475.
[6]	王然, 安连锁, 沈国清, 等. 基于奇异值分解的炉膛三维温度场声学重建仿真研究[J]. 中国电机工程学报, 2014, 34(增1): 147-152.
	Wang Ran, An Liansuo, Shen Guoqing, et al. Simulation of Acoustic Reconstruction of Three-dimensional Temperature Field in Furnace Based on Singular Value Decomposition[J]. Proceedings of The Chinese Society for Electrical Engineering, 2014, 34(S1): 147-152.
[7]	刘厦, 刘石, 任婷. 基于SA-ELM的声学层析成像温度分布重建算法[J]. 化工学报, 2017, 68(6): 2434-2446.
	Liu Sha, Liu Shi, Ren Ting. Temperature Distribution Reconstruction Algorithm for Acoustic Tomography Based on SA-ELM[J]. CIESC Journal, 2017, 68(6): 2434-2446.
[8]	Zhou Hao, Yan Jiafeng. Numerical and Experimental Investigations on the Total-variation Regularization Method of Temperature Distribution Reconstruction in Acoustic Tomography[J]. Measurement Science and Technology (S0957-0233), 2021, 32: 035112.
[9]	刘岩. 温度场超声传感成像算法研究[J]. 现代信息科技, 2018, 2(12): 146-149,152.
	Liu Yan. Research on Ultrasonic Sensing Imaging Algorithm for Temperature Field[J]. Modern Information Science and Technology, 2018, 2(12): 146-149,152.
[10]	顾梦楠. 声学CT温度场测量重建算法研究[D]. 沈阳: 沈阳工业大学, 2019.
	Gu Mengnan. Research on Reconstruction Algorithm of Acoustic CT Temperature Field Measurement[D].Shenyang: Shenyang University of Technology, 2019.
[11]	Jia Ruixi, Xiong Qingyu. Two-dimensional Temperature Field Distribution Reconstruction Based on Least Square Method and Radial Basis Function Approximation[J].Mathematical Problems in Engineering (S1024-123X), 2017: 1213605.
[12]	刘厦. 声学层析成像温度分布重建研究[D]. 北京: 华北电力大学, 2018.
	Liu Sha. Research on Reconstruction of Temperature Distribution from Acoustic Tomography[D]. Beijing: North China Electric Power University, 2018.
[13]	Zhang Juqi, Qi Hong, Jiang Donghang, et al. Acoustic Tomography of Two Dimensional Velocity Field by Using Meshless Radial Basis Function and Modified Tikhonov Regularization Method[J]. Measurement (S0263-2241), 2021, 175: 109107.
[14]	黄陈昱, 郑飞虎, 张冶文. 热脉冲法数据处理的反问题求解研究[J]. 中国电机工程学报, 2021, 41(5): 1557-1565.
	Huang Chenyu, Zheng Feihu, Zhang Yewen. Research on Inverse Problem Solving for Thermal Pulse Data Processing[J]. Proceedings of the Chinese Society for Electrical Engineering, 2021, 41(5): 1557-1565.
[15]	刘厦, 刘石. 基于声学层析成像的炉内温度场重建算法研究[J]. 动力工程学报, 2017, 37(7): 525-532,568.
	Liu Sha, Liu Shi. Research on Reconstruction Algorithm of Furnace Temperature Field Based on Acoustic Tomography[J]. Journal of Power Engineering, 2017, 37(7): 525-532,568.
[16]	杨鹏史, 丁卉, 陈同, 等. 基于局部加权线性回归的城市公交车排放能耗预测[J]. 中山大学学报 (自然科学版), 2019, 58(6): 111-118.
	Yang Pengshi, Ding Hui, Chen Tong, et al. Urban Bus Emission Energy Consumption Prediction Based on Local Weighted Linear Regression[J]. Journal of Sun Yat-sen University (Natural Science Edition), 2019, 58(6): 111-118.
[17]	Liu Kang, Shao Weiming, Chen Guoming.Autoencoder-Based Nonlinear Bayesian Locally Weighted Regression for Soft Sensor Development[J].ISA Transactions (S0019-0578), 2020, 103: 143-155.
[18]	张育贵, 王义, 杨人静. 基于径向基神经网络的天气预测模型[J]. 贵州大学学报 (自然科学版), 2018, 35(1): 69-72,103.
	Zhang Yugui, Wang Yi, Yang Renjing. Weather Forecasting Model Based on Radial Basis Neural Network[J]. Journal of Guizhou University (Natural Science Edition), 2018, 35(1): 69-72,103.
[19]	Zuzana Majdisova, Vaclav Skala. Radial Basis Function Approximations: Comparison and Applications[J].Applied Mathematical Modelling (S0307-904X), 2017, 51: 728-743.
[20]	Kong Qian, Jiang Genshan, Liu Yuechao, et al. 3D High-quality Temperature-field Reconstruction Method in Furnace Based on Acoustic Tomography[J]. Applied Thermal Engineering (S1359-4311), 2020, 179: 115693.
[21]	Han Ziyang, Qian Xusheng, Huang He, et al. Efficient Design of Multicolumn RBF Networks[J].Neurocomputing (S0925-2312), 2021, 450(5): 253-263.
[22]	吴善杰, 王新. 基于AGA-DBSCAN优化的RBF神经网络构造煤厚度预测方法[J]. 计算机科学, 2021, 48(7): 308-315.
	Wu Shanjie, Wang Xin. A Coal Thickness Prediction Method Based on AGA-DBSCAN Optimized RBF Neural Network[J]. Computer Science, 2021, 48(7): 308-315.

重建方法	E_mse/%	R_e	K_em
ART	2.73	0.981	35.9
SVD	1.81	0.992	54.8
Tikhonov	1.83	0.991	54.1
TR-RBF	1.17	0.995	85.0

重建方法	E_mse/%	R_e	K_em
ART	2.72	0.959	35.2
SVD	1.96	0.968	49.3
Tikhonov	2.01	0.967	48.1
TR-RBF	1.71	0.983	57.4

重建方法	E_mse/%	R_e	K_em
ART	12.11	0.894	7.38
SVD	10.31	0.902	8.75
Tikhonov	10.40	0.910	8.75
TR-RBF	4.18	0.981	23.47

重建方法	单峰	双峰	四峰
ART	9.31	10.52	6.87
SVD	10.21	10.58	6.54
Tikhonov	11.04	10.93	6.64
TR-RBF	23.93	17.20	11.10

重建方法	单峰	双峰	四峰
ART	5.62	4.81	3.07
SVD	6.10	4.61	3.22
Tikhonov	6.24	5.21	3.08
TR-RBF	17.32	10.09	9.19