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.
|Titolo:||A Bayesian Approach for Sensor Optimisation in Impact Identification|
MALLARDO, Vincenzo (Primo) (Corresponding)
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||03.1 Articolo su rivista|
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|Paper_published_22_nov_2016_materials-09-00946.pdf||Articolo pubblicato||Full text (versione editoriale)||Open Access Visualizza/Apri|