Li Yanqiu, Liu Shi, Inaki Schlaberg H
Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, North China Electric Power University, Beijing 102206, China.
Sensors (Basel). 2017 Sep 12;17(9):2084. doi: 10.3390/s17092084.
Accuracy and speed of algorithms play an important role in the reconstruction of temperature field measurements by acoustic tomography. Existing algorithms are based on static models which only consider the measurement information. A dynamic model of three-dimensional temperature reconstruction by acoustic tomography is established in this paper. A dynamic algorithm is proposed considering both acoustic measurement information and the dynamic evolution information of the temperature field. An objective function is built which fuses measurement information and the space constraint of the temperature field with its dynamic evolution information. Robust estimation is used to extend the objective function. The method combines a tunneling algorithm and a local minimization technique to solve the objective function. Numerical simulations show that the image quality and noise immunity of the dynamic reconstruction algorithm are better when compared with static algorithms such as least square method, algebraic reconstruction technique and standard Tikhonov regularization algorithms. An effective method is provided for temperature field reconstruction by acoustic tomography.
算法的准确性和速度在声学层析成像温度场测量重建中起着重要作用。现有算法基于仅考虑测量信息的静态模型。本文建立了一种声学层析成像三维温度重建的动态模型。提出了一种同时考虑声学测量信息和温度场动态演化信息的动态算法。构建了一个目标函数，该函数将测量信息、温度场的空间约束及其动态演化信息进行融合。采用稳健估计来扩展目标函数。该方法结合了隧道算法和局部最小化技术来求解目标函数。数值模拟表明，与最小二乘法、代数重建技术和标准蒂霍诺夫正则化算法等静态算法相比，动态重建算法的图像质量和抗噪性更好。为声学层析成像温度场重建提供了一种有效方法。
