Application and exploration of self-attention mechanism in dynamic process monitoring
Xin Ma, Zhanzhan Liu, Mingxing Zheng, Youqing Wang
Abstract
The self-attention mechanism comes from the human visual function, which imitates the internal process of living beings when observing, and is widely used in the field of deep learning, such as natural language processing and image recognition. In the dynamic industrial process, it not only needs to mine the information of massive data, but also needs to analyze the correlation between samples. The self-attention mechanism can analyze the internal characteristics of data well and focus on global and local important information, so it is suitable for process monitoring problems. Currently, in the field of process monitoring, the self-attention mechanism has not been widely used. This paper innovatively proposes a dynamic process monitoring framework based on self-attention mechanism named self-attention principal component analysis (self-attention PCA). Experiments have verified that self-attention PCA has a great monitoring effect on incipient faults in the dynamic process.