Quality Prediction Modeling for Industrial Processes Using Multiscale Attention-Based Convolutional Neural Network

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often have different scales of topology and highly coupled spatiotemporal features. However, the existing soft sensing models usually face difficulties in extracting the multiscale local spatiotemporal features in multicoupled complex process data and harnessing them to their full potential to improve the prediction performance. Therefore, a multiscale attention-based CNN (MSACNN) is proposed in this article to alleviate such problems. In MSACNN, convolutional kernels of different sizes are first designed in parallel in the convolutional layers, which can generate feature maps containing local…

• NN
  National Natural Science Foundation of China

  Awards: 62303494, 62103360, 61988101, 62173346, 92267205
• CS
  Central South University

  Award: 2023ZZTS0300