Stacked Attention-based Autoencoder with Feature Fusion and Its application for Quality Prediction

In recent years, deep learning like stacked autoencoder (SAE) has been widely applied to soft sensor modeling for industrial process quality prediction. However, the original SAE based models cannot extract the quality-relevant features due to its unsupervised feature learning, which may even cause negative impact on the final prediction. To cope with this problem, a novel stacked attention-based autoencoder (SAAE) is proposed. By introducing quality information and attention mechanism to the pre-training process, the quality-relevant features can be obtained and simultaneously their importance can be distinguished. Furthermore, to fully utilize these features, feature fusion strategy is integrated with the SAAE method. Finally, a hydrocracking industrial process is used to validate the effectiveness of the proposed method.