Acoustic emission source location for composite tubes using finite element simulation and machine learning

Acoustic emission is widely used in engineering structural health monitoring, where damage location based on acoustic emission have great advantages. Currently, most of the acoustic emission location work requires extensive experimental studies. In this study, acoustic emission source location model was proposed based on BP neural network and acoustic emission wave propagation simulation, thus reduced the experiments required for acoustic emission localisation and improved the location accuracy. Initially, finite element simulation was conducted to study the propagation of acoustic emission waves in composite tubes. The calculated findings showed a high level of agreement with the experimental data. Additionally, the first arrival time of the signal was extracted according to the Akaike information criterion, and the neural network was trained and tested using the simulated data, while the trained network model was validated using the experimental data. The results show that the finite element method is a reliable alternative test method. More than 94% of the sound source location results have absolute location error within ± 3 mm, and the all points is less than ± 5 mm. The deviation of the location results showed significant improvement compared to the traditional time difference of arrival method, verifying the feasibility of the method.