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Condition monitoring of electric-cam mechanisms based on Model-of-Signals of the drive current higher-order differences

机译:基于驱动器电流的信号模型的电泵机制的状态监测电流高阶差异

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摘要

Condition monitoring of electric motor driven mechanisms is of great importance in industrial machines. The knowledge of the actual health state of such components permits to address maintenance policies which results in better exploitation of their actual operational life span and consequently in maintenance cost reduction. In this paper, we exploit the way electric cams are implemented on the vast majority of PLC/Motion controllers to develop a suitable condition monitoring procedure. This technique relies on computing the higher-order differences of the current absorbed by slave motors to get signals that do not depend on a priori knowledge of the cam trajectory and of the mechanism nominal model. Subsequently, we will use these data in the Model-of-Signals framework, to gather information on the mechanism’s health condition, which in turn can be used to perform predictive maintenance policies. The differenced signal is modelled as an ARMA process and the model capabilities in condition monitoring are then shown in simulation and experimental application. Besides, this framework allows exploiting the edge-computing capabilities of the machinery controllers by implementing recursive estimation algorithms.
机译:电动机驱动机构的状态监测在工业机器中具有重要意义。这些组件的实际健康状况的知识许可证解决维护政策,从而更好地利用其实际运营寿命,从而降低维护成本。在本文中,我们利用电凸轮在绝大多数PLC /运动控制器上实现了电力凸轮,以开发合适的条件监测程序。该技术依赖于计算由从电动机吸收的电流的高阶差异以获得不依赖于凸轮轨迹的先验知识和机制标称模型的信号。随后,我们将在信号模型框架中使用这些数据,以收集有关机制的健康状况的信息,从而可以用于执行预测维护策略。差异信号被建模为ARMA过程,然后在仿真和实验应用中显示条件监测中的模型能力。此外,该框架通过实现递归估计算法来利用机器控制器的边缘计算能力。

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