Wu Kunyu, Ni Keqi, Chen Liwei, Xu Hengyuan, Wang Junqiao, Zhou Jingyi, Zhou Xinzhi
Sichuan University Pittsburgh Institute, Sichuan University, Chengdu 610065, China.
College of Design and Engineering, National University of Singapore, Singapore 117575, Singapore.
Sensors (Basel). 2025 Apr 18;25(8):2567. doi: 10.3390/s25082567.
Accurate and swift evaluation of the temperature distribution in boiler furnaces is essential for maximizing energy efficiency and ensuring operational safety. Traditional temperature field reconstruction algorithms, while effective, often suffer from accumulated errors, difficulty in solving ill-posed problems, low accuracy, and poor generalization. To overcome these limitations, a Temperature Field Reconstruction Network based on an acoustic information encoder (AIE) and a temperature field reconstruction decoder (TFRD) is proposed (ATTRN). This method directly utilizes acoustic measurement data for temperature field prediction, effectively balancing global semantic capture and local detail preservation. The proposed approach avoids complex traditional mathematical processing and empirical parameter selection, enhancing both accuracy and generalization. Simulation studies and engineering validations demonstrate the performance and industrial applicability of the proposed method.
