Ma Leilei, Ma Jungang, Zelminbek Manlidan, Zhang Wenjun
Xinjiang Uygur Autonomous Region Research Institute Of Measurement & Testing, Urumqi 830011, China.
Sensors (Basel). 2024 Oct 12;24(20):6564. doi: 10.3390/s24206564.
High-precision measurement of temperature value distributions in production scenarios is of great significance for industrial production, but traditional temperature field reconstruction algorithms rely on the design of manual feature extraction methods with high computational complexity and poor generalization ability. In this paper, we propose a high-precision temperature field reconstruction algorithm based on deep learning, using an efficient adaptive feature extraction method for temperature field reconstruction. We design an improved temperature field reconstruction algorithm based on the ResNet18 neural network; introduce the CBAM attention mechanism in the model; and design a feature pyramid, using M-FPN, a multi-scale feature aggregation network fusing PAN and FPN, to make the extracted feature information propagate multi-dimensionally among different layers to improve the feature characterization ability. Finally, the mean square error is used to guide the model to optimize the training so that the model pays more attention to the data and reduces the large error to ensure that the gap between the predicted value and the real value is small. The experimental results show that the reconstruction accuracy of the improved algorithm presented in this paper is significantly better than that of the original algorithm in the case of typical peaked temperature field distributions.
在生产场景中对温度值分布进行高精度测量对工业生产具有重要意义，但传统的温度场重建算法依赖于人工特征提取方法的设计，计算复杂度高且泛化能力差。本文提出一种基于深度学习的高精度温度场重建算法，采用高效的自适应特征提取方法进行温度场重建。我们设计了一种基于ResNet18神经网络的改进温度场重建算法；在模型中引入CBAM注意力机制；并设计了一个特征金字塔，使用融合了PAN和FPN的多尺度特征聚合网络M-FPN，使提取的特征信息在不同层之间进行多维传播，以提高特征表征能力。最后，使用均方误差来指导模型优化训练，使模型更加关注数据并减少大误差，确保预测值与真实值之间的差距较小。实验结果表明，在典型的峰值温度场分布情况下，本文提出的改进算法的重建精度明显优于原算法。
