Industrial Data Imputation Based on Multiscale Spatiotemporal Information Embedding with Asymmetrical Transformer

In the process industry, the challenge of missing data significantly impairs the efficacy of data-driven process monitoring systems and soft sensor modeling, particularly due to issues, such as unbalanced sampling intervals and sensor malfunctions. Process data, inherently nonlinear and characterized by spatiotemporal coupling, are prone to distribution shifts, which traditional imputation techniques often fail to address comprehensively. To overcome these limitations, this article introduces a novel data imputation framework, termed multiscale spatiotemporal information embedding with asymmetrical Transformer (MSST-Former). This framework reconceptualizes the missing data problem by integrating both global and local perspectives on time series and input variables. The proposed approach initiates with a hybrid 1-D convolutional network module that effectively captures local spatiotemporal correlations and dependencies within the time-series data. This is followed by an encoder-decoder structure, incorporating an inverted Transformer (iTransformer) in conjunction with a Transformer block, to embed series representations with a focus on long-term multivariate correlations and overarching spatiotemporal dependencies. Finally, a multilayer residual network executes the data imputation by leveraging the features embedded at multiple scales. Comparative experiments with several baseline and state-of-the-art models on two real-world industrial datasets verify the superiority and robustness of the proposed MSST-Former.