Optimal sensor placement for spatial lattice structure based on genetic algorithms

Wei, Liu; Gao, Weicheng; Sun, Yi; Xu, Min-jian

doi:10.1016/j.jsv.2008.03.026

Cited by 160 publications

(98 citation statements)

References 11 publications

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“…All the optimal results for 2, 4, and 6 sensor locations are calculated by using PSO algorithm and listed in Table 4 anti-symmetrical positions or near to these positions, which are similar to the previous results reported. 19 The primary reason is that the truss model and constraints are symmetrical. Further to verify the effectiveness and robustness of the proposed method in this paper, the PSO process of optimising sensors placement has been run for 10 times each with a stochastic initial population.…”

Section: Numerical Examplementioning

confidence: 99%

“…Abdullah et al 17 utilised the GA in conjunction with gradient-based optimisation techniques to place sensors and actuators simultaneously in an effective structural control system. Guo et al 18 and Liu et al 19 developed an improved GA to find the optimal placement of sensors on a spatial lattice structure with the aim of SHM and detecting structural damage. Although abundance of literature has been published on the optimal placement of sensors, the novel and effective approaches are desirable to deal with the practical application.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Sensor Placement for a Truss Structure Using Particle Swarm Optimisation Algorithm

Wu¹,

Zhao²,

Zhang³

et al. 2017

IJAV

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The problem of placing sensors plays a significant role in the domains of structural health monitoring (SHM) applications and parameter estimation in structural dynamics. In this paper, the particle swarm optimisation (PSO) algorithm is introduced firstly and utilised to place sensors optimally on a truss structure for the purpose of modal identification. Then, two different types of fitness functions are constructed as to be the optimal criteria, which are based on modal assurance criterion (MAC) and maximising measures of the observability gramian matrix, respectively. The former one is mainly used to distinguish between two sets of mode shapes with the aim of maximising the off-diagonal elements in MAC matrix, and the latter one ensures proper observability of the structure. Finally, a truss structure model is considered as an example to demonstrate the efficiency and validity of the proposed method. Numerical results show that the two approaches using the PSO algorithm can find the optimal location of sensors successfully and identify the modal frequencies of the truss structure accurately by acceleration FRF method.

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Section: Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Placement for a Truss Structure Using Particle Swarm Optimisation Algorithm

Wu¹,

Zhao²,

Zhang³

et al. 2017

IJAV

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show abstract

“…For example, the sub-optimal addition methods are proposed in [6,7]. The genetic algorithms is used in [8,9], and the monkey algorithm is used in [10]. A Bayesian approach is taken in [11], and a multiple optimization approach is adopted in [12].…”

Section: Sensor Placementmentioning

confidence: 99%

Survey of Sensor Placement Methods for Structural Health Monitoring

Park¹,

Lim²

2015

IJEIC

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“…An improved simulated annealing algorithm was proposed by Tong et al (2014). Besides, some improved genetic algorithms (GA) were also proposed to improve the convergence speed (Yao et al 1993, Guo et al 2004, Liu et al 2008, Kang et al2008.…”

Section: Introductionmentioning

confidence: 99%

Application of model reduction technique and structural subsection technique on optimal sensor placement of truss structures

Lu¹,

Wang²,

Liao³

et al. 2015

Smart Structures and Systems

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Abstract. An optimal sensor placement (OSP) method based on structural subsection technique (SST) and model reduction technique was proposed for modal identification of truss structures, which was conducted using genetic algorithm (GA). The constraints of GA variables were determined by SST in advance. Subsequently, according to model reduction technique, the optimal group of master degrees of freedom and the optimal objective function value were obtained using GA in a case of the given number of sensors. Correspondingly, the optimal number of sensors was determined according to optimal objective function values in cases of the different number of sensors. The proposed method was applied on a scaled jacket offshore platform to get its optimal number of sensors and the corresponding optimal sensor layout. Then modal kinetic energy and modal assurance criterion were adopted to evaluate vibration energy and mode independence property. The experiment was also conducted to verify the effectiveness of the selected optimal sensor layout. The results showed that experimental modes agreed reasonably well with numerical results. Moreover the influence of the proposed method using different optimal algorithms and model reduction technique on optimal results was also compared. The results showed that the influence was very little.

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Optimal sensor placement for spatial lattice structure based on genetic algorithms

Cited by 160 publications

References 11 publications

Optimal Sensor Placement for a Truss Structure Using Particle Swarm Optimisation Algorithm

Optimal Sensor Placement for a Truss Structure Using Particle Swarm Optimisation Algorithm

Survey of Sensor Placement Methods for Structural Health Monitoring

Application of model reduction technique and structural subsection technique on optimal sensor placement of truss structures

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