Damage detection in vibrating bodies using genetic algorithms

Rao, M. Ananda; Srinivas, J.; Murthy, B. S.

doi:10.1016/j.compstruc.2004.01.005

Cited by 84 publications

(45 citation statements)

References 12 publications

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“…They showed that the vibration signals obtained from two sensors can effectively diagnose the extent of the crack development. Genetic algorithms appear as a desirable approach to optimize an objective function that depends on crack position and size [7][8][9][10]. Mehrjoo et al [11] used the GAs to search the solution of an inverse problem where the cracks' location and size are calculated.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection of Fan Blade Based on Natural Frequencies

Gao

et al. 2018

International Journal of Rotating Machinery

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A simple method was developed to detect damage based on a discrete mathematical model for fan blades using changes in natural frequencies combined with a fluid-structure analysis. In addition, a numerical approach was developed for the fluid-structure analysis. The results of numerical simulation provided the natural frequency data for each mode under different locations and sizes of a single crack in a blade. A fault database was built using Matlab. The damage of a blade was detected using the changes in natural frequencies. This study will assist in investigating the effect of a crack on a structure from different perspectives; the simulation results show the effectiveness of this approach.

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Section: Introductionmentioning

confidence: 99%

Crack Detection of Fan Blade Based on Natural Frequencies

Gao

et al. 2018

International Journal of Rotating Machinery

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“…The residual force concept has received wide attention with regard to the damage detection and assessment [4][5][6]. This concept provides a minimized objective function numerically calculated from the differences between the vibration characteristics of the actual damage and those of the predicted damage.…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

Boonlong

2014

Advances in Mechanical Engineering

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Vibration-based damage detection, a nondestructive method, is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage happens. This paper presents cooperative coevolutionary genetic algorithm (CCGA), which is capable for an optimization problem with a large number of decision variables, as the optimizer for the vibration-based damage detection in beams. In the CCGA, a minimized objective function is a numerical indicator of differences between vibration characteristics of the actual damage and those of the anticipated damage. The damage detection in a uniform cross-section cantilever beam, a uniform strength cantilever beam, and a uniform cross-section simply supported beam is used as the test problems. Random noise in the vibration characteristics is also considered in the damage detection. In the simulation analysis, the CCGA provides the superior solutions to those that use standard genetic algorithms presented in previous works, although it uses less numbers of the generated solutions in solution search. The simulation results reveal that the CCGA can efficiently identify the occurred damage in beams for all test problems including the damage detection in a beam with a large number of divided elements such as 300 elements.

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“…Although such damage will not affect the normal use of blade immediately, the partial damage may expand or enlarge with accumulated damage, making potential threats to the safe operation of whole wind turbine generator. Therefore, it is very important to monitor the damage of blade in operation online [6,7]. The dynamic characteristics of structure are the sensitivity sign of physical property, so vibration response is always obtained by embedding acceleration sensor under blade skin to judge the condition of blade.…”

Section: Introductionmentioning

confidence: 99%

Optimal Layout of Sensors on Wind Turbine Blade Based on Combinational Algorithm

Zhao

Zhang

2016

International Journal of Distributed Sensor Networks

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This work proposes a comprehensive combinational algorithm for sensor layout to solve the problem that unreasonable sensor layout affects the effectiveness of data selection and reduces the accuracy of monitoring system in healthy monitoring system of wind turbine blade structure. This algorithm integrates the advantages of kinetic energy method, effective independence method, modal assurance criterion (MAC), and many other optimal methods. In order to avoid information redundancy caused by partial concentration of test spots, Euclidean distance was introduced as the iteration condition of algorithm. Finite element simulation and experimental results show that the layout obtained by this method is beneficial to improving signal-to-noise ratio and can maximize the crossing angle between target vibration modes. Therefore, relatively complete characteristics of actual wind turbine blade structure can be obtained.

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Damage detection in vibrating bodies using genetic algorithms

Cited by 84 publications

References 12 publications

Crack Detection of Fan Blade Based on Natural Frequencies

Crack Detection of Fan Blade Based on Natural Frequencies

Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

Optimal Layout of Sensors on Wind Turbine Blade Based on Combinational Algorithm

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