Tool Condition Monitoring (TCM) technology in machining is crucial for maintaining safety and optimizing costs. However, its practical application faces two significant challenges: difficulties in data collection and a decline in generalization performance across different monitoring tasks. To this end, a hybrid feature boundary-enhanced meta-learning network with adaptive gradients (HFBEAML) is proposed. This method combines cutting process parameters with time-series signal features, employing a multimodal one-dimensional Convolutional Neural Network (1D-CNN) based on the DenseNet architecture and a multi-head self-attention mechanism (MHSA) to mine multi-dimensional features sensitive to tool wear. To further enhance the model’s feature discrimination capability, a multi-loss joint optimization strategy is introduced, combining task-level loss with an improved triplet loss. This approach incorporates strategies for adaptive boundaries and handling the hardest positive and negative sample sets, thereby enhancing the robustness of feature metrics. Additionally, this research innovatively proposes an adaptive meta-level gradient update mechanism, dynamically adjusting gradient weights according to the characteristics of multiple tasks, aiming to improve the model’s generalization ability in multi-task learning environments. The effectiveness of the proposed method is demonstrated through experiments in two different scenarios, comparing its results with five other models showcasing its significant advantages in multi-task, small-sample data monitoring environments.

中文翻译：

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基于元学习的多条件小样本场景工具状态监测方法


加工中的刀具状态监测 (TCM) 技术对于维护安全和优化成本至关重要。然而，其实际应用面临两个重大挑战：数据收集困难和跨不同监控任务的泛化性能下降。为此，提出了一种具有自适应梯度的混合特征边界增强元学习网络（HFBEAML）。该方法将切割过程参数与时间序列信号特征相结合，采用基于DenseNet架构的多模态一维卷积神经网络（1D-CNN）和多头自注意力机制（MHSA）来挖掘多维特征对刀具磨损敏感。为了进一步增强模型的特征辨别能力，引入了多损失联合优化策略，将任务级损失与改进的三元组损失相结合。这种方法结合了自适应边界和处理最困难的正负样本集的策略，从而增强了特征度量的鲁棒性。此外，本研究创新性地提出了自适应元级梯度更新机制，根据多任务的特点动态调整梯度权重，旨在提高模型在多任务学习环境中的泛化能力。通过在两种不同场景下的实验证明了该方法的有效性，并将其结果与其他五个模型进行了比较，展示了该方法在多任务、小样本数据监控环境中的显着优势。