A Hybrid Approach of Long Short-Term Memory and Machine Learning With Acoustic Emission Sensors for Structural Damage Localization

Various sensors are used for structural health monitoring (SHM). An acoustic emission (AE) sensor detects an elastic wave propagating in the medium, so it can detect the possibility of defects occurring inside the structure. Using multiple sensors enables the estimation of the signal source through the differences in signals measured by each sensor. Among the information for signal analysis, the time difference of arrival is the most commonly used factor for estimating the location of the source. However, it is difficult to accurately determine the arrival time because the measured signal always contains ambient noise. Even though the arrival times of signals are determined, there is the following task to identify the source location, which is also complicated because the signal does not propagate with a constant velocity throughout the medium. To solve this problem, this study adopts a hybrid approach that applies artificial intelligence techniques step by step. In the first phase, the time-series data are classified as signal and nonsignal by the long short-term memory (LSTM) network. The second phase is to identify the source location based on the naive Bayes classifier using the distribution of the arrival times of signals extracted from multiple sensors. Since this approach reduces complex computations in signal processing while minimizing the masking of physical meaning by black-box AI technology, it allows for versatile applications depending on the objectives. The proposed method was validated through an experimental test, and the results showed that the method had reliable performance.