Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network - ZHANG Lifeng; LI Jing; WANG Zhi (发电技术)
In order to obtain the online monitoring information of boiler furnace temperature field in thermal power plant quickly and accurately, a temperature field reconstruction algorithm of acoustic tomography (AT) based on deep neural network (DNN) was proposed. After normalizing the measured values, combined with principal component analysis (PCA) dimension reduction, a fully connected network was constructed to distinguish the peak type. Moreover, DNN and BP neural network were built to predict the normalized slowness value and its maximum value, respectively. Finally, the temperature field distribution was reconstructed. Four typical temperature field models were simulated by using this method. The results show that the reconstruction quality of DNN algorithm is better than that of Tikhonov regularization algorithm and conjugate gradient algorithm. In addition, the average relative error and root mean square error of reconstructed image are less than 0.36% and 0.85% respectively.