Accurately predicting the tool remaining useful life (RUL) is critical for maximizing tool utilization and saving machining costs. Various physical model-based or data-driven prediction methods have been developed and successfully applied in different machining operations. However, many uncertain factors affect tool RUL during the cutting process, making it challenging to create a precise physical model to characterize the degradation of tool performance. The success of the purely data-driven technique depends on the amount and quality of the training samples, it does not consider the physical law of tool wear, and the interpretability of the prediction results is poor. This paper presents a data-model linkage approach for tool RUL prediction based on deep feature fusion and Wiener process to address the above limitations. A convolutional stacked bidirectional long short-term memory network with time-space attention mechanism (CSBLSTM-TSAM) is developed in the data-driven module to fuse the multi-sensor signals collected during the cutting process and then obtain the mapping relationship between signal features and tool wear values. In the physical modeling module, a three-stage tool RUL prediction model based on the nonlinear Wiener process is established by considering the evolution law of different wear stages and multi-layer uncertainty, and the corresponding probability density function is derived. The real-time estimated tool wear of the data-driven module is used as the observed value of the physical model, and the model parameters are dynamically updated by the weight-optimized particle filter (WOPF) algorithm under a Bayesian framework, thereby realizing the data-model linkage tool RUL prediction. Milling experiments demonstrate that the proposed method not only improves RUL prediction accuracy, but also has good generalization ability and robustness for prediction tasks under different working conditions.

中文翻译：

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基于深度特征融合和维纳过程的刀具剩余寿命数据模型联动预测

准确预测刀具剩余使用寿命 (RUL) 对于最大限度提高刀具利用率和节省加工成本至关重要。各种基于物理模型或数据驱动的预测方法已被开发并成功应用于不同的加工操作。然而，在切削过程中，许多不确定因素会影响刀具的 RUL，这使得创建精确的物理模型来表征刀具性能的退化具有挑战性。纯数据驱动技术的成功取决于训练样本的数量和质量，它没有考虑刀具磨损的物理规律，预测结果的可解释性较差。本文提出了一种基于深度特征融合和维纳过程的工具 RUL 预测的数据模型链接方法，以解决上述局限性。在数据驱动模块中开发了具有时空注意机制的卷积堆叠双向长短期记忆网络（CSBLSTM-TSAM），以融合切割过程中采集的多传感器信号，进而获得信号特征之间的映射关系和刀具磨损值。在物理建模模块中，考虑不同磨损阶段的演化规律和多层不确定性，建立了基于非线性维纳过程的三阶段刀具RUL预测模型，并推导了相应的概率密度函数。将数据驱动模块实时估计的刀具磨损量作为物理模型的观测值，通过贝叶斯框架下的权重优化粒子滤波器（WOPF）算法动态更新模型参数，从而实现数据模型链接工具 RUL 预测。铣削实验表明，该方法不仅提高了RUL预测精度，而且对于不同工况下的预测任务具有良好的泛化能力和鲁棒性。