电池电芯制造装备智能状态监测：一种动态扩张Transformer方法

With the rapid development of the new energy sector, production equipment for battery cells faces increasing challenges in maintaining efficiency and quality. Among these, the laser die cutting and winding machine plays a pivotal role in transforming electrode sheets into finished cells. Its performance directly affects the dimensional precision and internal structural consistency of the cells, which are critical to product quality and production-line efficiency. To tackle the challenges in monitoring and maintaining this critical equipment, we propose a time-series data-driven method based on the Transformer architecture, named multiscale dynamic dilated attention, which effectively predicts sensor offset trajectories and provides early shutdown fault warnings when correction sensors approach their operational limits. Furthermore, this model incorporates adjustable segment sizes and counts, and assigns distinct dilation rates to individual attention heads, this design enables a dynamic tradeoff among receptive field, modeling capacity, and computational cost, allowing fine-grained control over long-range dependence modeling while reducing the canonical self-attention complexity from $O(\mathit {L}^{2})$ to approximately $O(\mathit {L})$. Extensive experiments and real-world applications demonstrate that the proposed method achieves state-of-the-art performance in both prediction accuracy and practical deployment, while exhibiting excellent adaptability across diverse operating conditions.