Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), 20500 Arrasate-Mondragon, Spain.
Faculty of Engineering in Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain.
Sensors (Basel). 2023 May 12;23(10):4706. doi: 10.3390/s23104706.
Virtual sensing is the process of using available data from real sensors in combination with a model of the system to obtain estimated data from unmeasured points. In this article, different strain virtual sensing algorithms are tested using real sensor data, under unmeasured different forces applied in different directions. Stochastic algorithms (Kalman filter and augmented Kalman filter) and deterministic algorithms (least-squares strain estimation) are tested with different input sensor configurations. A wind turbine prototype is used to apply the virtual sensing algorithms and evaluate the obtained estimations. An inertial shaker is installed on the top of the prototype, with a rotational base, to generate different external forces in different directions. The results obtained in the performed tests are analyzed to determine the most efficient sensor configurations capable of obtaining accurate estimates. Results show that it is possible to obtain accurate strain estimations at unmeasured points of a structure under an unknown loading condition, using measured strain data from a set of points and a sufficiently accurate FE model as input and applying the augmented Kalman filter or the least-squares strain estimation in combination with modal truncation and expansion techniques.
虚拟传感是利用实际传感器的可用数据结合系统模型，从未测量的点获取估计数据的过程。本文使用实际传感器数据测试了不同的应变虚拟传感算法，在不同方向施加未测量的不同力的情况下。使用不同的输入传感器配置测试了随机算法（卡尔曼滤波器和增广卡尔曼滤波器）和确定性算法（最小二乘应变估计）。使用风力涡轮机原型机应用虚拟传感算法并评估获得的估计值。在原型机的顶部安装了一个带有旋转底座的惯性振动器，以在不同方向产生不同的外力。对所进行的测试中获得的结果进行了分析，以确定能够获得准确估计的最有效传感器配置。结果表明，使用一组点的测量应变数据和足够精确的 FE 模型作为输入，并应用增广卡尔曼滤波器或最小二乘应变估计与模态截断和扩展技术相结合，在未知载荷条件下，有可能获得结构中未测量点的准确应变估计。
