Affiliations: State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
Correspondence:
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Corresponding author: Jinhao Qiu, State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, Jiangsu, China. E-mail:qiu@nuaa.edu.cn
Abstract: The conventional impact localization strategies often
assume that the wave velocity is independent of the propagation angle and
obtain the wave velocity through theoretical calculation. These compromises
may lead to inaccuracies of impact locations. In this paper, a Bayesian
probabilistic methodology for impact localization is proposed. This approach
utilizes the time of flight of diagnostic Lamb waves obtained by a
piezoelectric sensor network for parameter identification. Bayes' theorem is
then used to build the probabilistic relationship between measured time of
flight data and unknown parameters. Finally, Markov chain Monte Carlo method
is presented to implement the identification of probability distributions of
impact location and wave velocity. Experimental studies carried out by
dropping a steel ball on a CFRP panel are conducted to validate the proposed
Bayesian impact localization strategy.
Keywords: Impact localization, lamb waves, Bayesian inference, Markov chain Monte Carlo method, uncertainties
