Time series prediction for lock nuts production quality driven by information fusion and data-model hybrid

The locking performance of nuts directly impacts the lifespan and reliability of assembled products. However, certain aerospace nuts undergo over 120 h of rigorous testing per batch, causing increased costs and delayed product delivery. Therefore, accurately predicting production quality and remaining testing time (RTT) is crucial for improving efficiency. Facing this new challenge, this paper proposes a data-model hybrid time series prediction method based on quality information fusion. First, considering that the monitoring data contains two sets of related features, we introduce a multi-task parallel deep learning (MTL) network with a temporal self-attention mechanism (TSAM). The TSAM assigns importance to key degradation information, while MTL leverages shared feature information to capture more accurate long-term trends. Next, considering the multi-stage nature and uncertainty of the degradation process, a semi-empirical physical degradation model is constructed, where stage identification is achieved using the Pruned Exact Linear Time (PELT) method, and uncertainty is estimated through Particle Filtering (PF). The Bayesian framework enables hybrid correction between the data-based and the model-based methods, integrating the strengths of both. Finally, experimental results demonstrate that the proposed method outperforms traditional models, effectively achieving more accurate quality predictions.