Abstract
To better apply the acoustic emission (AE) detection technology in real life, a method of material health detection based on LSTM and multi-channel data fusion was proposed. This method can realize end-to-end processing of acoustic emission signals, directly taking the original signal as the input for modeling, and the output of the model as the health status of the material. TB6 titanium alloy was used to verify the experiment. Comparative experiments were carried out through a variety of neural networks. The experimental results show that LSTM has obvious advantages in processing the data with strong timing of acoustic emission signals, and the final recognition accuracy can reach 94.6%. We use multiple sensors to collect data to mitigate the pollution of environmental noise to the data. The results show that compared with single-channel data, multi-channel data can improve the training effect of the model to some extent.
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