Attention-guided low-rank convolutional weighting for industrial missing data attacks

The widespread deployment of data acquisition units in industrial settings has significantly improved the efficiency of control and anomaly detection. However, when facing high proportions of missing data attacks, data quality often deteriorates, limiting the potential of data-driven optimization. To address this issue, we propose an attention-guided low-rank convolutional weighting method aimed at effectively estimating missing elements impacted by attacks. The proposed method combines attention mechanisms with convolutional unfolding techniques to fully exploit the low-rank characteristics of the data and faithfully reconstruct the missing data structure. Specifically, a low-rank convolutional weighting regularizer is introduced to capture low-rank subspace features of the original data and effectively guide the data recovery process. Meanwhile, an attention mechanism is used to accurately handle sparse noise, enhancing global robustness to sparse missing data. By employing iterative optimization, closed-form updates are achieved for solving variables and regularizers. Experimental results demonstrate that the proposed method provides advantages across several industrial application scenarios, including monoclonal antibody production in perfusion bioreactors using Chinese hamster ovary cells, multi-mode wastewater treatment plants, and image datasets. Even under high missing rates and non-uniform missing attack patterns, our method outperforms existing state-of-the-art methods.