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.

翻译：基于加速度计的过程监控在现代加工系统中得到广泛应用。当此类传感器安装在移动的机器部件上时，会隐式捕获与机器运动和刀具轨迹相关的运动学信息。若此类信息能够被重建，状态监测数据将构成严重的安全威胁，尤其对于改造型或弱防护的传感器系统。由于传感器及工艺特有的非理想特性（如噪声或传感器安装效应），传统信号处理方法难以从宽带加速度计信号中重建位置。本研究证明，序列到序列机器学习模型能够克服这些非理想特性，实现数控轴与刀具位置的重建。我们采用基于LSTM的序列到序列模型，并在工业铣削数据集上进行评估。结果表明：相较于双重积分法，基于学习的模型对低复杂度运动轨迹的重建误差降低达98%，对复杂加工序列的重建误差降低达85%。此外，刀具轨迹的关键几何特征及工件相关运动特性均得以保持。据我们所知，这是首个基于工业状态监测加速度计数据、通过学习方法实现数控位置重建的研究。