Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi-type sensory systems

2014 ◽  
Vol 44 (5) ◽  
pp. 757-774 ◽  
Author(s):  
Ka-Veng Yuen ◽  
Sin-Chi Kuok
Keyword(s):  
Sensor Placement ◽  
Structural Identification ◽  
Sensory Systems ◽  
Placement Algorithm

Optimal Sensor Placement Algorithm for Structural Damage Identification

2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimization Procedure ◽  
Frame Structure ◽  
Truss Structures ◽  
Optimal Sensor Placement ◽  
Structural Damage Identification ◽  
Placement Algorithm ◽  
Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

Robust sensor placement for structural identification

2021 ◽  
Author(s):  
Ka‐Veng Yuen ◽  
Xiao‐Han Hao ◽  
Sin‐Chi Kuok
Keyword(s):  
Sensor Placement ◽  
Structural Identification

A theoretical framework for sensor placement, structural identification and damage detection in tensegrity structures

2019 ◽  
Vol 28 (12) ◽  
pp. 125004 ◽  
Author(s):  
Omar Aloui ◽  
Jan Lin ◽  
Landolf Rhode-Barbarigos
Keyword(s):  
Damage Detection ◽  
Sensor Placement ◽  
Structural Identification ◽  
Theoretical Framework ◽  
Tensegrity Structures

Using an information-theoretic sensor placement algorithm to assess classifier robustness

2016 ◽  
Author(s):  
John S. Wilcher ◽  
Aaron D. Lanterman ◽  
William L. Melvin
Keyword(s):  
Sensor Placement ◽  
Information Theoretic ◽  
Placement Algorithm

scholarly journals Optimal sensor placements using modified Fisher information matrix and effective information algorithm

2021 ◽  
Vol 17 (6) ◽  
pp. 155014772110230
Author(s):  
Eun-Taik Lee ◽  
Hee-Chang Eun
Keyword(s):  
Fisher Information ◽  
Mode Shape ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Sensor Placement ◽  
Degree Of Freedom ◽  
Optimal Sensor Placement ◽  
Parameter Matrix ◽  
Placement Algorithm ◽  
Sensor Locations

This article presents an optimal sensor placement algorithm for modifying the Fisher information matrix and effective information. The modified Fisher information matrix and effective information are expressed using a dynamic equation constrained by the condensed relationship of the incomplete mode shape matrix. The mode shape matrix row corresponding to the master degree of freedom of the lowest-contribution Fisher information matrix and effective information indices is moved to the slave degree of freedom during each iteration to obtain an updated shape matrix, which is then used in subsequent calculations. The iteration is repeated until the target sensors attain the targeted number of modes. The numerical simulations are then applied to compare the optimal sensor placement results obtained using the number of installed sensors, and the contribution matrices using the Fisher information matrix and effective information approaches are compared based on the proposed parameter matrix. The mode-shape-based optimal sensor placement approach selects the optimal sensor layout at the positions to uniformly allocate the entire degree of freedom. The numerical results reveal that the proposed F-based and effective information–based approaches lead to slightly different results, depending on the number of parameter matrix modes; however, the resulting final optimal sensor placement is included in a group of common candidate sensor locations. However, the resulting final optimal sensor placement is included in a group of common candidate sensor locations.

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