DeepFilter: A Transformer-Style Framework for Accurate and Efficient Process Monitoring

Effective process monitoring is increasingly vital in industrial automation for ensuring operational safety, necessitating both high accuracy and efficiency. Although Transformers have demonstrated success in various fields, their canonical form based on the self-attention mechanism is inadequate for process monitoring due to two primary limitations: ❶ the step-wise correlations captured by the self-attention mechanism struggle to capture discriminative patterns in monitoring logs due to the lack of semantics of each step, thus compromising accuracy; ❷ the quadratic computational complexity of self-attention hampers efficiency. To address these issues, we propose DeepFilter, which modifies the Transformer for process monitoring. The core modification is replacing the self-attention layer with an efficient filtering layer that excels at capturing long-term discriminative patterns with reduced complexity. Equipped with the efficient filtering layer, DeepFilter enhances both accuracy and efficiency, meeting the stringent demands of process monitoring. Experimental results on real-world process monitoring datasets validate DeepFilterȉs superiority in terms of accuracy and efficiency compared to existing state-of-the-art models.