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A Bayesian Approach for Sensor Optimisation in Impact Identification
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DOI：10.3390/ma9110946.png)
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摘要
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This paper presents a Bayesian approach for optimizing the position of sensors aimed at impact identification in composite structures under operational conditions. The uncertainty in the sensor data has been represented by statistical distributions of the recorded signals. An optimisation strategy based on the genetic algorithm is proposed to find the best sensor combination aimed at locating impacts on composite structures. A Bayesian-based objective function is adopted in the optimisation procedure as an indicator of the performance of meta-models developed for different sensor combinations to locate various impact events. To represent a real structure under operational load and to increase the reliability of the Structural Health Monitoring (SHM) system, the probability of malfunctioning sensors is included in the optimisation. The reliability and the robustness of the procedure is tested with experimental and numerical examples. Finally, the proposed optimisation algorithm is applied to a composite stiffened panel for both the uniform and non-uniform probability of impact occurrence.
Keyword:
probability of detection
sensor malfunctioning
genetic algorithm
non-linear finite element method
artificial neural network
structural health monitoring
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IF:
3.2
论文数:
5.7W
被引数:
15.1W
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A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing用于结构健康监测的最佳传感器放置的贝叶斯方法，并应用于主动传感
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