机床加工过程监控中的安全风险：用于CNC轴位置重建的序列到序列学习
Security Risks in Machining Process Monitoring: Sequence-to-Sequence Learning for Reconstruction of CNC Axis Positions
AI总结：
本文提出基于序列到序列学习模型，实现从工业状态监控加速度计数据中高精度重建CNC轴和刀具位置，有效降低安全风险。
摘要：
基于加速度计的过程监控在现代加工系统中被广泛应用。当安装在移动的机械部件上时，这些传感器会隐含地捕捉与机床运动和刀具轨迹相关的运动学信息。如果能够重建这种信息，状态监控数据将构成严重的安全威胁，尤其是对于 retrofit 或保护薄弱的传感器系统。传统的信号处理方法无法从宽带加速度计信号中重建位置，因为存在传感器和过程特定的非理想因素，如噪声或传感器放置效应。在本工作中，我们证明序列到序列机器学习模型可以克服这些非理想因素，实现CNC轴和刀具位置的重建。我们的方法采用基于LSTM的序列到序列模型，并在工业铣削数据集上进行评估。我们表明，基于学习的模型在低复杂度运动轨迹中将重建误差减少高达98%，在复杂加工序列中减少高达85%，相比双积分。此外，刀具轨迹的主要几何特征和与工件相关的运动特征得以保留。据我们所知，这是首次通过工业状态监控加速度计数据实现基于学习的CNC位置重建的研究。
英文摘要：
Accelerometer-based process monitoring is widely deployed in modern machining systems. When mounted on moving machine components, such sensors implicitly capture kinematic information related to machine motion and tool trajectories. If this information can be reconstructed, condition monitoring data constitutes a severe security threat, particularly for retrofitted or weakly protected sensor systems. Classical signal processing approaches are infeasible for position reconstruction from broadband accelerometer signals due to sensor- and process-specific non-idealities, like noise or sensor placement effects. In this work, we demonstrate that sequence-to-sequence machine learning models can overcome these non-idealities and enable reconstruction of CNC axis and tool positions. Our approach employs LSTM-based sequence-to-sequence models and is evaluated on an industrial milling dataset. We show that learning-based models reduce the reconstruction error by up to 98% for low complexity motion profiles and by up to 85% for complex machining sequences compared to double integration. Furthermore, key geometric characteristics of tool trajectories and workpiece-related motion features are preserved. To the best of our knowledge, this is the first study demonstrating learning-based CNC position reconstruction from industrial condition monitoring accelerometer data.
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