Dang Duy Ngoc, Do Tri Minh, Araújo Rui Alexandre de Matos, Nguyen Khang Hoang Vinh, Le Can Duy
Electrical and Computer Engineering, Vietnamese-German University, Ben Cat 75000, Binh Duong, Vietnam.
Institute of Systems and Robotics (ISR-UC), Department of Electrical and Computer Engineering (DEEC-UC), University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal.
Sensors (Basel). 2025 Jan 23;25(3):678. doi: 10.3390/s25030678.
This study proposes a novel approach for predicting the output behaviors of the Pepperl+Fuchs 3RG6232-3JS00-PF ultrasonic sensor. The sensor, integrated into the Festo MPS-PA Didactic System, serves to monitor the water level in a tank, facilitating water extraction to bottles delivered via a conveyor belt. This modeling approach represents the initial phase in the creation of a digital twin of the physical sensor, providing the capability for users to observe the sensor's response and forecast its life cycle for maintenance objectives. This study utilizes the Festo MPS-PA Compact Didactic System and support vector regression (SVR) for data acquisition (DAQ), preprocessing, and model training with hyperparameter optimization. The objective of this modeling approach is to establish a digital framework for transition towards Industry 4.0. It holds the potential for creating a digital counterpart of the entire MPS-PA System when combining the proposed sensor modeling technique with computer-assisted design (CAD) software such as Siemens NX in the future. This would enable users to oversee the entire process in a three-dimensional visualization engine, such as Tecnomatix Plant Simulation. This research significantly contributes to the comprehension and application of digital twins in the realm of mechatronics and sensor systems technology. It also underscores the importance of digital twins in enhancing the efficiency and predictability of sensor systems. The method used in this paper involves predicting the rate of change (RoC) of the water level and then integrating this rate to estimate the actual water level, providing a robust approach for sensor data modeling and digital twin creation. The result shows a promising 6.99% error percentage.
本研究提出了一种预测倍加福3RG6232 - 3JS00 - PF超声波传感器输出行为的新方法。该传感器集成在费斯托MPS - PA教学系统中，用于监测水箱中的水位，便于将水抽取到通过传送带输送的瓶子中。这种建模方法代表了创建物理传感器数字孪生体的初始阶段，为用户提供了观察传感器响应并预测其生命周期以实现维护目标的能力。本研究利用费斯托MPS - PA紧凑型教学系统和支持向量回归（SVR）进行数据采集（DAQ）、预处理以及超参数优化的模型训练。这种建模方法的目标是建立一个向工业4.0过渡的数字框架。未来，当将所提出的传感器建模技术与诸如西门子NX等计算机辅助设计（CAD）软件相结合时，它有可能创建整个MPS - PA系统的数字对应物。这将使用户能够在诸如Tecnomatix Plant Simulation等三维可视化引擎中监督整个过程。本研究对数字孪生体在机电一体化和传感器系统技术领域的理解和应用做出了重大贡献。它还强调了数字孪生体在提高传感器系统效率和可预测性方面的重要性。本文所采用的方法包括预测水位的变化率（RoC），然后对该变化率进行积分以估计实际水位，为传感器数据建模和数字孪生体创建提供了一种稳健的方法。结果显示误差百分比为6.99%，前景良好。
