基于鲁棒正则化极限学习机的声学层析温度分布重建  

Reconstruction of Temperature Distribution in Acoustic Tomography Based on Robust Regularized Extreme Learning Machine

作　　者：张立峰[1] 董祥虎 ZHANG Lifeng;DONG Xianghu(Department of Automation,North China Electric Power University,Baoding 071003,Hebei Province,China)

机构地区：[1]华北电力大学自动化系,河北省保定市071003

出　　处：《发电技术》2025年第2期361-369,共9页Power Generation Technology

基　　金：国家自然科学基金项目(61973115)。

摘　　要：【目的】声学层析成像作为非侵入式温度检测技术,在工业过程监测中具有重要价值,但其受限于病态反演导致的空间分辨率不足与噪声敏感问题。为此,提出了一种基于鲁棒正则化极限学习机(extreme learning machine,ELM)的声学层析温度分布重建方法。【方法】构建两阶段重建框架,第一阶段基于声波飞行时间与低分辨率温度数据训练网络,获得粗网格下的低分辨率温度分布;第二阶段利用低分辨率温度分布与高分辨率温度分布训练网络,得到细网格下温度分布的高分辨率重建。对典型的温度场模型进行数值模拟,并与传统的Tikhonov正则化方法、Landweber算法、代数重建算法(algebraic reconstruction technique,ART)及ELM算法进行对比。【结果】鲁棒正则化ELM算法的平均相对误差和均方根误差分别为0.28%和0.38%,重建质量明显高于其他算法。【结论】基于鲁棒正则化ELM的声学层析温度分布重建方法兼顾计算效率与重建精度,为电站锅炉等设备的高分辨率温度监测提供了新方案,特别是在高温、强干扰等恶劣工况下展现出重要工程应用价值。[Objectives]Acoustic tomography,as a noninvasive temperature detection technology,holds significant value in industrial process monitoring.However,it is constrained by insufficient spatial resolution caused by illposed inversion and sensitivity to noise.To address these issues,a acoustic tomography temperature distribution based on robust regularized extreme learning machine(ELM)is proposed.[Methods]A two-stage reconstruction framework is established.In the first stage,the network is trained using acoustic time of flight data and low-resolution temperature data to obtain a low-resolution temperature distribution on a coarse grid.In the second stage,the network is further trained using low-resolution and high-resolution temperature distribution,enabling high-resolution temperature reconstruction on a fine grid.Numerical simulations are conducted on typical temperature field models,and the proposed method is compared with traditional algorithms,including Tikhonov regularization,Landweber algorithm,algebraic reconstruction technique(ART),and ELM algorithm.[Results]The robust regularized ELM algorithm achieves an average relative error of 0.28%and a root mean square error of 0.38%,significantly outperforming the other algorithms in reconstruction quality.[Conclusions]The acoustic tomography temperature distribution based on robust regularized ELM balances computational efficiency and reconstruction accuracy,providing a new solution for highresolution temperature monitoring in power plant boilers and similar equipment.It demonstrates significant engineering application value,particularly under harsh conditions such as high temperature and strong interference.

关 键 词：发电厂 电站锅炉 声学层析成像 温度分布 高分辨率重建 鲁棒正则化 极限学习机 数值模拟 重建算法 

分 类 号：TK31[动力工程及工程热物理—热能工程]