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首页> 外文期刊>Mechanical systems and signal processing >Development of a stochastic effective independence (SEFI) method for optimal sensor placement under uncertainty
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Development of a stochastic effective independence (SEFI) method for optimal sensor placement under uncertainty

机译：随机性有效独立性（SEFI）方法的开发，用于不确定性条件下的最佳传感器放置

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摘要

Finding optimal sensor placement for data acquisition is an essential initial stage in the development of many engineered systems. For this purpose, the effective independence (EFI) method was developed and has been widely used. In this method, sensor locations are selected to maximize the linear independency of the target mode shape matrix. However, the EFI method lacks consideration of the uncertainty that is present in real applications. To overcome this limitation, in this study, a newstochastic EFImethod is derived. The resultant equation is composed of the conventional deterministic term and an additional random term. Using the derived equation, optimal sensor locations are found. The results of stochastic EFI give the best linear independency of the mode shape matrix in the mean sense. In this paper, stochastic EFI is also extended to the energy-based EFI method. In the energy-based EFI method, the mass or stiffness matrix is weighted to have the kinetic or strain energy form of the EFI method. By decomposing the weighted matrix, the same form is obtained as in the EFI method; thus, its stochastic version follows naturally, as in the stochastic EFI method. Further, the stochastic sensor placement method is also derived for a different optimization criterion called the A-optimality criterion, which uses a matrix trace for its measure. Finally, the proposed method is validated using a truss bridge case. Its results are compared with the Monte Carlo simulation based method, which is another approach used to handle the system uncertainty. The case study indicates that the suggested method shows higher linear independency than the EFI method and the energy-based EFI method; it is also better with different optimality conditions.
机译：在许多工程系统的开发中，寻找用于数据采集的最佳传感器放置是必不可少的初始阶段。为此，开发了有效独立性（EFI）方法并已广泛使用。在这种方法中，选择传感器位置以使目标模式形状矩阵的线性独立性最大化。但是，EFI方法没有考虑实际应用中存在的不确定性。为了克服这一限制，在这项研究中，推导了一种新的随机EFI方法。结果方程式由常规确定性项和一个附加随机项组成。使用导出的方程式，可以找到最佳的传感器位置。从平均意义上讲，随机EFI的结果给出了模式形状矩阵的最佳线性独立性。在本文中，随机EFI也扩展到了基于能量的EFI方法。在基于能量的EFI方法中，对质量或刚度矩阵进行加权以具有EFI方法的动能或应变能形式。通过分解加权矩阵，可以得到与EFI方法相同的形式。因此，它的随机版本自然遵循随机EFI方法。此外，还针对称为A-最优性准则的不同优化准则推导了随机传感器放置方法，该准则使用矩阵迹线作为其度量。最后，使用桁架桥箱对所提方法进行了验证。将其结果与基于Monte Carlo仿真的方法进行了比较，后者是用于处理系统不确定性的另一种方法。案例研究表明，所提出的方法具有比EFI方法和基于能量的EFI方法更高的线性独立性。在不同的最佳条件下效果也更好。

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