Clustering of Sensor Locations Using the Effective Independence Method

Friswell, Michael I.; Castro-Triguero, Rafael

doi:10.2514/1.j053503

Cited by 28 publications

(11 citation statements)

References 18 publications

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“…Therefore, the two criteria yield different results. Nevertheless, both criteria do agree with each other for two cases of choosing six and nine sensors ("three" and "four" may be typographical errors in reference [1]), as shown in table 1 of the commented paper. This powerfully verifies that two criteria are approximate to each other if both are implemented in the suboptimal sense.…”

Section: Connection Between the Efi And The Condition Number Critmentioning

confidence: 65%

“…As noted by Friswell and Castro-Triguero in the commented paper [1], it is desirable to develop an efficient way to search for the global optimal row combination instead of the prohibitive exhaustive search. In this perspective, subspace approximation may provide a feasible direction [11].…”

Section: Discussionmentioning

confidence: 99%

“…The EFI is computed in an iterative fashion [1,2]; that is, the Degree of Freedom with the lowest value of EFI index is deleted as a candidate sensor location and the corresponding row is removed, and then a new FIM is formed with a reduced mode shape, new EFI indices are computed, and another row with the lowest EFI value is removed.…”

Section: Connection Between the Efi And The Condition Number Critmentioning

confidence: 99%

“…For the cantilever beam model in the commented paper [1], the condition number criterion is implemented in the global optimal sense (i.e., to exhaustively search all possible sensor combinations), whereas the EFI is implemented in a suboptimal sense (i.e., to seek the minimum perturbation of the condition number in each of its iterations). Therefore, the two criteria yield different results.…”

Section: Connection Between the Efi And The Condition Number Critmentioning

confidence: 99%

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Comments on “Clustering of Sensor Locations Using the Effective Independence Method”

Fritzen

2016

AIAA Journal

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Section: Connection Between the Efi And The Condition Number Critmentioning

confidence: 65%

Section: Discussionmentioning

confidence: 99%

Section: Connection Between the Efi And The Condition Number Critmentioning

confidence: 99%

Section: Connection Between the Efi And The Condition Number Critmentioning

confidence: 99%

See 2 more Smart Citations

Comments on “Clustering of Sensor Locations Using the Effective Independence Method”

Fritzen

2016

AIAA Journal

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“…Although the minimization of Equation (16) is necessary for obtaining a good reconstruction of the displacement responses, it does not take into account that two dofs can be spatially correlated. The spatial correlation can be caused by the mesh refinement of a finite element model (FEM) [ 33 , 34 , 40 ]. The FEM with a refined mesh will help an accurate response reconstruction.…”

Section: Two-stage Sensor Placementmentioning

confidence: 99%

Efficient Sensor Placement Optimization for Shape Deformation Sensing of Antenna Structures with Fiber Bragg Grating Strain Sensors

Zhou

Cai

Zhao

et al. 2018

Sensors

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This paper investigates the problem of an optimal sensor placement for better shape deformation sensing of a new antenna structure with embedded or attached Fiber Bragg grating (FBG) strain sensors. In this paper, the deformation shape of the antenna structure is reconstructed using a strain–displacement transformation, according to the measured discrete strain data from limited FBG strain sensors. Moreover, a two-stage sensor placement method is proposed using a derived relative reconstruction error equation. In this method, the initial sensor locations are determined using the principal component analysis based on orthogonal trigonometric (i.e., QR) decomposition, and then a new location is sequentially added into the initial sensor locations one by one by minimizing the relative reconstruction error considering information redundancy. The numerical simulations are conducted, and the comparisons show that the proposed method is advantageous in terms of the sensor distribution and computational cost. Experimental validation is performed using an antenna experimental platform equipped with an optimal FBG strain sensor configuration, and the reconstruction results show good agreements with those measured directly from displacement sensors. The proposed method has a large potential for the strain sensor placement of complex structures, and the proposed antenna structure with FBG strain sensors can be applied to the future wing-skin antenna or flexible space-based antenna.

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