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

Abstract

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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“…Similarly, Zhao et al conducted a separate study employing the particle swarm optimization algorithm to explore the optimal sensor placement for 20 joints, ultimately determining that six sensors were optimal [38]. In a recent study, Blachowski et al presented a comprehensive approach for damage identification in spatial truss structures, achieving remarkable results using only eight sensors placed at 23 joints [39].…”

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“…Similar approaches by using search metaheuristics were also delivered by Yi et al [18], Zhao et al [19], and Shan et al [20]. In more recent study, Capellari et al [21] proposed an optimal sensor placement by employing Polynomial Chaos Expansion and stochastic optimization to maximize the gain in Shannon information.…”

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“…Furthermore, Sun et al [17] performed discrete optimization by using the artificial bee colony algorithm in order to optimize their objective function which is based on modal assurance criterion (MAC). Similar approaches by using search metaheuristics were also delivered by Yi et al [18], Zhao et al [19], and Shan et al [20]. In more recent study, Capellari et al [21] proposed an optimal sensor placement by employing Polynomial Chaos Expansion and stochastic optimization to maximize the gain in Shannon information.…”