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Integration of model and sensor data for smart condition monitoring in mechatronic devices

机译:模型与传感器数据集成在机电装置中智能条件监控

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In this paper the use of features is described in order to individuate the best set-up conditions of a mechatronic system. In particular, features are obtained from data of quantities measured in different positions of the kinematic linkage, the operation of the system is based on. A kinematic and dynamic model of the device is realized and used in order to obtain the features of interest also in positions different from the ones the measurements are carried out throughout the kinematic link. This procedure allows us to merge information from both internal and external sensors, in order to identify the best features for identification of working conditions, taking into account their resolution, selectivity, easiness of calculation, and data processing time and load. The most efficacious features are identified with reference to the time and frequency domain and the most suitable position and quantity the information is originated from. This methodology step is also a preliminary action toward the use of neural networks for smart identification of specific statuses of interest of a mechatronic system, during both the set-up phase and the functioning of the system for its condition monitoring.
机译:在本文中,描述了使用特征,以便为机电系统的最佳设置条件进行分类。特别地,从在运动连杆的不同位置测量的量的数据中获得特征,系统的操作基于。实现并使用该装置的运动和动态模型,以便在与整个运动链路中进行不同的位置,以获得感兴趣的特征。此过程允许我们从内部和外部传感器合并信息,以便识别用于识别工作条件的最佳功能,以考虑到它们的分辨率,选择性,计算和数据处理时间和负载。参考时间和频域确定最有效的特征,并且信息来自信息的最合适的位置和数量。该方法步骤也是利用神经网络来识别用于智能识别机电系统的特定状态的神经网络的初步动作,在设置阶段和系统的状态监视的运行期间。

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