Quality Prediction Modeling for Industrial Processes Using Multiscale Attention-Based Convolutional Neural Network
针对复杂工业过程中多尺度时空特征提取难的问题，提出一种多尺度注意力卷积神经网络（MSACNN），通过并行不同尺寸卷积核和通道注意力机制提升质量预测性能，在两个实际工业过程中验证了其优越性。
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 spatiotemporal features at different scales. Meanwhile, a channel-wise attention mechanism is designed on the feature maps in parallel to get their attention weights, representing the significance of the local spatiotemporal feature at different scales. The superiority of the proposed MSACNN over the other state-of-the-art methods is validated through the performance evaluation in two real industrial processes.