Novel Distributed GRUs Based on Hybrid Self-Attention Mechanism for Dynamic Soft Sensing

Nowadays, the deep learning technique has been widely applied in soft sensing benefiting from its strong ability of feature representation. However, classic dynamic soft sensing methods based on deep learning do not consider spatial-temporal and quality-relevant information simultaneously in the process data. Additionally, feature extraction methods encounter the challenge of information redundancy when dealing with large datasets. To address these challenges, we propose a novel hybrid self-attention mechanism with distributed GRUs (HSAM-dGRUs) within an encoder-decoder framework. The HSAM-dGRUs architecture contains a hybrid self-attention encoder, integrating multi-channel multi-head self-attention (MC-MSA) and quality-related self-attention (QR-SAM). The MC-MSA method effectively extracts local features through multi-channels to capture spatio-temporal characteristics in sequence data. Subsequently, the QR-SAM method introduces supervisory knowledge into the features and capture the quality-relevant information adaptively. The distributed GRUs decoder is designed to extract local dynamic hidden states from multi-channel features for accurate prediction. Two case studies on real industrial process datasets demonstrate the effectiveness and superiority of the proposed HSAM-dGRUs soft sensing approach. Note to Practitioners—In the process industry, monitoring quality variables is beneficial for tracking process status, saving energy, and reducing emissions. However, it is difficult to implement accurate measurement and real-time control for process quality in general. In this work, we propose a novel hybrid self-attention mechanism utilizing distributed gated recurrent units for soft sensing modeling of dynamic processes. The proposed method integrates an multi-channel multi-head self-attention mechanism and a quality self-attention mechanism into the encoder. The encoder is responsible for adaptively extracting spatio-temporal features and assigning weights based on quality-related information. Furthermore, a decoder based on distributed GRUs is employed to capture the dynamic information from the extracted features, enhancing the prediction task. First, the proposed methods can be trained offline based on historical dataset. Then, the well-trained model can be uploaded and use the new data for accurate online prediction. Preliminary experiments in this paper have demonstrated the feasibility of the proposed method, but it has not been tested in actual industrial production. In the future, we will work on developing a quality prediction system based on the proposed method so that it can be applied in actual industrial production.