A Bee Swarm Algorithm for Optimising Sensor Distributions for Impact Detection on a Composite Panel

Scott, Matilda S; Worden, Keith

doi:10.1111/str.12128

Cited by 10 publications

(10 citation statements)

References 15 publications

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“…Swarm intelligence optimization algorithms have many attractive features, such as strong global optimization abilities, blind searching, and high computational efficiency, compared with deterministic optimization methods or sequential sensor placement methods. In recent years, a shift from the use of traditional algorithms for optimal sensor placement determination toward the use of swarm intelligence optimization algorithms has occurred . The wolf algorithm (WA), which mimics the social behavior of wolf packs and has the capability of solving various complex optimization problems modeled by linear, nonlinear, or high‐dimension functions, is a competitive method in the family of swarm intelligence optimization algorithms.…”

Section: Solution Methodsmentioning

confidence: 99%

Optimal placement of triaxial sensors for modal identification using hierarchic wolf algorithm

Zhou

et al. 2016

Struct. Control Health Monit.

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Optimal triaxial sensor placement plays a crucial role in tridimensional modal identification; however, few studies have been conducted on this topic. In this paper, a holistic approach, including a tridimensional optimal criterion and solution method, is proposed for finding the optimal locations to deploy triaxial sensors. The tridimensional optimal criterion is established by combining the tridimensional modal assurance criterion and the redundancy function. The tridimensional modal assurance criterion is deduced from the one-dimensional modal assurance criterion by using the widely accepted Fisher information matrix, adopted to ensure the linear independence of identified tridimensional mode shapes. The redundancy function, which is defined by the similarity index among nodes, is developed to measure the information redundancy and to maintain effective visualization of the identified tridimensional mode shapes. To efficiently find the optimal triaxial sensor configuration with the proposed tridimensional optimal criterion, the hierarchic wolf algorithm (HWA) is developed by imitating the swarm intelligence embedded in the wolf pack. Five strategies, which are termed as coding and spreading wolves, searching behaviors, attacking behaviors, hierarchic population, and distributing food process, are employed to enhance the global searching ability of the HWA. The proposed approach is verified by a benchmark bridge model. The results indicate that the established tridimensional optimal criterion has the capability of ensuring optimal triaxial sensor configurations that make the identified tridimensional mode shapes have features of excellent linear independence and good visualization, and the HWA has strong ability and high efficiency in determining the global optimal triaxial sensor configuration.

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Section: Solution Methodsmentioning

confidence: 99%

Optimal placement of triaxial sensors for modal identification using hierarchic wolf algorithm

Zhou

et al. 2016

Struct. Control Health Monit.

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“…The trajectory-based algorithms include the ant colony optimization (ACO), 94,95 the bee colony optimization, 45,96 the monkey algorithm, 97,98 firefly algorithm (FA), 99,100 wolf algorithm 134,135 and particle swarm optimization (PSO) 93,101 and many other methods which use multiple agents for thorough search and improvement of the fitness. The most widely used swarm-based technique and the pioneer in this area is the PSO technique.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

“…The algorithm has three search phases which are named after the type of bees and their corresponding foraging duties, namely, the employed phase, the onlooker phase and the scout phase. 45,96 Only a common number of control parameters are needed to set up the algorithm, for example, the population size, the maximum number of iterations and the limit cycle which yields the algorithm its simplicity and ease for customization to the application at hand. The scout phase is for locating a suitable solution around the hive (global search).…”

Section: Optimization Algorithmsmentioning

confidence: 99%

“…The bee colony optimization along with ANN was employed for the optimization of the sensor placement for impact detection by Scott and Worden. 96 They formulated the algorithm first for the travelling salesman problem and they report favourable performance of the ABC over the ACO. The optimization also performs satisfactorily for the impact detection problem.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

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Optimization of sensor placement for structural health monitoring: a review

Ostachowicz

Soman

Malinowski

2019

Structural Health Monitoring

278

154

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The deployment cost of the structural health monitoring (SHM) system is the major argument against the more widespread use of the structural health monitoring techniques. Optimization of sensor placement offers an opportunity to reduce the cost of the SHM system without compromising on the quality of the monitoring approach. Several studies in the area of optimization of sensor placement for SHM applications have been undertaken but the approach has been rather application specific. This article is an attempt to present an unbiased state of the art of the work carried out in the area. The article is targeted towards researchers working in the field of structural health monitoring and optimization of sensor placement as well as practising engineers. This article reviews the work in the area of optimization of sensor placement. It first presents the definition of the optimization problem and then describes each step of the optimization. The current state of the art is then classified based on the techniques for which the optimization of sensor placement has been optimized. The article covers vibration-based monitoring, strain monitoring and elastic wave-based monitoring, as in the eyes of the authors these three techniques are most commonly used and accepted in the SHM community. The article later discusses the different optimization algorithms that have been applied in the literature. The article highlights the different pitfalls of the optimization algorithms and the countermeasures different researchers have proposed to overcome the known shortcomings. In the later section, the multi-objective optimization or the problem definition, keeping in mind the structural as well as executional demands, is discussed. A section has also been developed to showcase the use of optimization of sensor placement techniques’ data fusion–based systems.

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“…[34] is a random variable conditioned on the initial weights given to the network. Scott and Worden [35] describe the development of a bee swarm algorithm, again via application to the NN-based objective function proposed in ref. [31].…”

Section: Ant Colony and Bee Swarm Metaphorsmentioning

confidence: 99%

Emerging Trends in Optimal Structural Health Monitoring System Design: From Sensor Placement to System Evaluation

Barthorpe

Worden

2020

JSAN

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This paper presents a review of advances in the field of Sensor Placement Optimisation (SPO) strategies for Structural Health Monitoring (SHM). This task has received a great deal of attention in the research literature, from initial foundations in the control engineering literature to adoption in a modal or system identification context in the structural dynamics community. Recent years have seen an increasing focus on methods that are specific to damage identification, with the maximisation of correct classification outcomes being prioritised. The objectives of this article are to present the SPO for SHM problem, to provide an overview of the current state of the art in this area, and to identify promising emergent trends within the literature. The key conclusions drawn are that there remains a great deal of scope for research in a number of key areas, including the development of methods that promote robustness to modelling uncertainty, benign effects within measured data, and failures within the sensor network. There also remains a paucity of studies that demonstrate practical, experimental evaluation of developed SHM system designs. Finally, it is argued that the pursuit of novel or highly efficient optimisation methods may be considered to be of secondary importance in an SPO context, given that the optimisation effort is expended at the design stage.

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A Bee Swarm Algorithm for Optimising Sensor Distributions for Impact Detection on a Composite Panel

Cited by 10 publications

References 15 publications

Optimal placement of triaxial sensors for modal identification using hierarchic wolf algorithm

Optimal placement of triaxial sensors for modal identification using hierarchic wolf algorithm

Optimization of sensor placement for structural health monitoring: a review

Emerging Trends in Optimal Structural Health Monitoring System Design: From Sensor Placement to System Evaluation

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