# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 4 Deep-Learning (DL) Methods for AE Testing
### Recurrent Neural Network (RNN) Based Applications
The AEs deriving from seismic events show a fair variability of the stress waveforms and considerable differences in the trigger source phases of the rupture sources that make them difficult to identify with traditional techniques. The authors showed that RNNs can identify microseismic waves with discrete anti-interference capabilities. However, to achieve these results, you need to have a significant number of labeled data sequences. [PERSON] et al. [96] detected plain bearing wear using the AE and RNN signals. The AE signals measured on a planetary gearbox plain bearing test stand were sent to an LSTM: the network was able to reconstruct the bearing wear history. [PERSON] et al. [97] identified three events in an AE generated by the uniaxial loading of a sample of western granite using RNNs. The method also allows calculating the position of the source AE of the tensor of the source moment. [PERSON] et al. [98] faced the gear pitting fault diagnosis problem with the RNNs and CNNs. The authors adopted the gated recurrent unit (GRU) network, an RNN with only three ports and no internal cell status. The information stored in the internal cell state is embedded in the hidden state of the gated recurring unit. This collective information is passed on to the next gated recurring unit. The authors sent the raw AE signals to a CNN and the vibration signals to a GRU network: finally, the chained outputs are sent to a softmax layer to diagnose gear pitting failures. [PERSON] et al. [99] used the AE from a U-shaped aluminum plate generated with multiple pencil-lead breaks for structural health monitoring. Two broadband AE sensors collect the time series of the direct and reflected waves of the AE signals, which are sent to an LSTM. Figure 6: RNN architecture unfolded: feedforward version of network of arbitrary length depending on a sequence of inputs. Number of blocks of unfolded version essentially depends on length of sequence to be analyzed. Within the network, it represents a pattern that temporally links elements of the series that RNN analyzes. [PERSON] et al. [100] predicted the failure of a concrete structure using the AE and RNN signals. The raw signals are first preprocessed by an SVM (support vector machine) to extract only the signals relevant to the construction of the health indicator of the concrete product. These indicators are then sent by an LSTM for the forecast of the remaining useful life. [PERSON] et al. [101] used the diamond wheel AE signals when grinding brittle materials to construct a wheel condition regression prediction model. The AE components are well-separated in the frequency domain highlighting wheel deterioration events. The frequency spectrum of the AE signal was detected by constructing a time sequence during the rectification. These data were sent to an LSTM-based regression prediction model. [PERSON] et al. [102] used RNNs to detect railway cracks from AE signals. In this work, a NARX (nonlinear autoregressive with exogenous input) network was used to eliminate noise from the useful signal. First, the AE signals were collected under real operating conditions, and subsequently, the crack signals were added with an artificial generation system. AE noise has been effectively eliminated by bringing the SNR to values above 20 dB. In this way, it was easy to detect the AE signal deriving from the railway cracks with respect to the background noise. [PERSON] et al. [103] used the raw AE signal to accurately estimate the remaining useful life of the machines. Sequential data from the sensor network are merged and sent to the model, bypassing the feature extraction procedure that requires prior knowledge. The prediction model is based on a hybrid approach that exploits the long-term memory levels of an LSTM and classical neural networks. This extracts the temporal information of the sequential data. [PERSON] et al. [104] used acoustic emission signals for the detection and localization of structural damage.