基于压缩感知的AT温度场重建算法

doi:10.3969/j.issn.1000-1158.2024.05.09

Abstract
Figure/Table
References (17)
Related Citation (11)

Download: PDF (758 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract To improve the temperature field reconstruction ability of acoustic tomography (AT), an AT temperature field reconstruction algorithm based on compressed sensing (CS-AT algorithm) was proposed. The algorithm utilizes signal sparsity to reduce the amount of data to be solved and reduce the difficulty of solving inverse problems. Firstly, selected an appropriate dictionary and constructed a framework based on CS for the forward and inverse problem of temperature field reconstruction by AT. Then, the orthogonal matching pursuit (OMP) algorithm was used for CS reconstruction to obtain the solution of the temperature field reconstruction in the sparse domain. Finally, transformed it back to the original domain and interpolated it to 37×37 pixel fine temperature distribution using cubic splines. Through numerical simulation, for kinds of models temperature fields (average temperature, single peak, bimodal, and four peak) were reconstructed using the classical least squares method (LSM) and CS-AT algorithm under noisy and non-noisy conditions respectively. Average temperature, single peak, and double peak actual temperature fields were reconstructed on an independently developed experimental system. Simulation and experiments have shown that CS-AT algorithm can effectively reduce temperature field reconstruction errors. Under the four peak temperature field, the highest reconstruction error of CS-AT algorithm is only 25.5% of LSM.

Key words： temperature measurement acoustic tomography;temperature field reconstruction;compressed sensing;OMP

Received: 30 January 2023 Published: 23 May 2024

PACS:

TB942

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WEI Yuankun
	YAN Hua
	ZHOU Yinggang

Cite this article:

WEI Yuankun,YAN Hua,ZHOU Yinggang. Reconstruction Algorithm of AT Temperature Field Based on Compressed Sensing[J]. Acta Metrologica Sinica, 2024, 45(5): 671-677.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.05.09 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I5/671

［8］	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(9):2065-2073.
［13］	孔倩, 姜根山, 孙建浩, 等. 三维复杂温度场高精度声学测量方法［J］. 声学学报, 2021, 46 (5): 699-711.
［2］	刘波, 郑伟, 王志远,等.铠装热电偶动态响应校准过程数值研究［J］. 计量学报, 2022, 43 (12): 1593-1597.
［12］	武钰晖, 杨岚斐, 赵丽, 等. 基于AT的锅炉燃烧温度场重建算法研究［J］. 四川大学学报 (自然科学版), 2021, 58 (6): 46-54.
	LIU B, ZHENG W, WANG Y Z,et al. Numerical Simulation for the Dynamic Response Calibration of the Sheathed Thermocouple ［J］. Acta Metrologica Sinica, 2022, 43 (12): 1593-1597.
	CHEN Q Q, PAN J, YUAN D K. Development of a New High Temperature Thermocouple Performance Testing System ［J］. Acta Metrologica Sinica, 2022, 43 (11): 1424-1430.
［3］	WANG H L, ZHOU X Z, YANG Q F, et al. A Reconstruction Method of Boiler Furnace Temperature Distribution Based on Acoustic Measurement ［J］. IEEE Transactions on Instrumentation and Measurement, 2021, 70:9600413.
［6］	YAN H, CHEN G N, ZHOU Y G, et al. Primary study of temperature distribution measurement in stored grain based on acoustic tomography ［J］. Experimental Thermal and Fluid Science 2012, 42 :55-63.
［9］	张立峰, 李晶.基于级联密集残差网络的温度场高分辨率重建［J］. 图学学报, 2023,44 (2): 216-224.
［11］	ZHENG Q S, CHEN Y S, ZHU B, et al. A temperature field reconstruction method based on acoustic thermometry ［J］. Measurement. 2022,200:111642.
［1］	陈清清, 潘江, 袁定琨. 一种新型高温热电偶性能测试系统的研制［J］. 计量学报, 2022, 43 (11): 1424-1430.
［7］	BAO Y, JIA J, POLYDORIDES N. Real-time temperature field measurement based on acoustic tomography ［J］. Measurement Science and Technology, 2017, 28 (7): 074002.
	ZHANG L F, MIAO Y. A High Resolution Reconstruction Method of Temperature Distribution in Acoustic Tomography ［J］. Journal of System Simulation, 2022, 34 (9): 2065-2073.
［10］	ZHU B, ZHONG Q S, CHEN Y S, et al. A Novel Reconstruction Method for Temperature Distribution Measurement Based on Ultrasonic Tomography ［J］. IEEE Transactions on Ultrasonics Ferroelectrics, and Frequency Control, 2022, 69 (7): 2352-2370.
［4］	WU Y H, ZHOU X Z, LI Z, et al. A Method for Reconstruction of Boiler Combustion Temperature Field Based on Acoustic Tomography ［J］. Mathematical Problems in Engineering 2021, 2021:9922698.
［5］	ZHU Z N, ZHU X H, ZHANG C Z, et al. Dynamics of Tidal and Residual Currents Based on Coastal Acoustic Tomography Assimilated Data Obtained in Jiaozhou Bay, China ［J］. Journal of Geophysical Research Oceans, 2021, 126 (6):e2020JC017003.
	ZHANG L F, LI J. High Resolution Reconstruction of Temperature Field Based on Cascaded Dense Residual Network ［J］. Journal of Graphics, 2023,44 (2): 216-224.
	WU Y H, YANG L F, ZHAO L, et al. Research on reconstruction algorithm of boiler combustion temperature field based on AT ［J］. Journal of Sichuan University (Natural Science Edition), 2021, 58 (6): 46-54.
	KONG Q, JIANG G S, SUN J H, et al. 3D high-accuracy complicated temperature distribution reconstruction based on acoustic method ［J］. Acta Acustica, 2021, 46 (5): 699-711.
［15］	YAN H, WANG Y, WANG Y F, et al. Electrical capacitance tomography image reconstruction by improved orthogonal matching pursuit algorithm ［J］. IET Science, Measurement & Technology, 2020, 14 (3): 367-375.
［17］	WEI Y K, YAN H, ZHOU Y G. Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-dictionary Learning ［J］. Sensors, 2023, 23 (1): 208.
［14］	YAN H, MA Z, ZHOU Y G. An acoustic tomography system for online monitoring of temperature fields ［J］. IET Science Measurement & Technology, 2017, 11 (5): 623-630.
［16］	YAN H, WEI Y K, ZHOU Y G. Temperature distribution reconstruction method for acoustic tomography based on compressed sensing ［J］. Ultrasonic Imaging, 2022,44 (2-3): 77-95.