Self-attention-based Multi-block regression fusion Neural Network for quality-related process monitoring

Abstract

Background

For plant-wide process with multiple operation units, local-global modeling is an efficient method to achieve quality-related fault detection. However, most of algorithms based on local-global modeling ignore the correlation between sub-blocks. This will result in poor performance of the extracted global quality-related features.

Methods

This paper focus on the correlation between sub-blocks and proposes Self-attention-based Multi-block regression fusion Neural Network (SMNN) to achieve efficient quality-related fault detection for nonlinear multi-unit process. Firstly, to focus on quality-related information, the key variables are selected. Then, to extract quality-related features in each sub-block, a pre-training approach is used, i.e. a deep neural network-based regression network between process variables and quality variables is constructed in each sub-block. Secondly, considering the correlation between the sub-blocks, self-attention mechanism is used to integrate the quality-related feature from each block. With the help of an additional regression network, the quality-related features of sub-blocks are fine-tuned and the global features are extracted. Finally, quality-related statistic is constructed to detect faults.

Findings

The proposed method shows good performance in Tennessee-Eastman process, which demonstrates the effectiveness of the method. It also shows that considering the potential relationships between sub-blocks during model construction helps in the extraction of global features.