Attention-Based Two-Dimensional Dynamic-Scale Graph Autoencoder for Batch Process Monitoring

Traditional two-dimensional dynamic fault detection methods describe nonlinear dynamics by constructing a two-dimensional sliding window in the batch and time directions. However, determining the shape of a two-dimensional sliding window for different phases can be challenging. Samples in the two-dimensional sliding windows are assigned equal importance before being utilized for feature engineering and statistical control. This will inevitably lead to redundancy in the input, complicating fault detection. This paper proposes a novel method named attention-based two-dimensional dynamic-scale graph autoencoder (2D-ADSGAE). Firstly, a new approach is introduced to construct a graph based on a predefined sliding window, taking into account the differences in importance and redundancy. Secondly, to address the training difficulties and adapt to the inherent heterogeneity typically present in the dynamics of a batch across both its time and batch directions, we devise a method to determine the shape of the sliding window using the Pearson correlation coefficient and a high-density gridding policy. The method is advantageous in determining the shape of the sliding windows at different phases, extracting nonlinear dynamics from batch process data, and reducing redundant information in the sliding windows. Two case studies demonstrate the superiority of 2D-ADSGAE.