# Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning
[PERSON] and coworkers have used K-means clustering [26] of stainless steel by focusing on the analysis of frequency bands in AE signals. A convolutional neural network (CNN) was implemented by [27] to recognize dispersion curves on the spectrograms of AE signals or identifying different vehicle engines [28] rather than crack events. In bridge engineering, a binary classification problem has been successfully analyzed using CNNs to recognize noise and fracture AE signals [29]. These contributions clearly show the promising results either in terms of classification and remaining fatigue life prediction as well as in status-driven AE monitoring capabilities. Here, we describe a procedure to address the above cited problems in order to achieve both automatic and robust classification of crack events in AE data. In our work, we consider different statistical operators, i.e., instantaneous frequency, spectral entropy and spectral kurtosis, which have already been effective for characterizing a variety of non-stationary signals [30; 31] as the inputs of a deep learning (DL) neural network. Instead of applying DL methodology to image input data of concrete surfaces [32; 33], in this paper, we use DL to classify different AE solicitations represented by time varying signals. The chance to properly characterize the temporal behavior of AE events using DL depends on the type of information that contributes to the learning process. For example, traditional AE parameters, which generally provide a single number as a result (e.g., AF, RA, ENE), are not a favorable choice for feeding a DL network because they cause an increase in the learning time and decrease in the overall performance [34]. Temporal series classification is a standard analysis problem, which requires the identification of a functional dependence between the set of possible time series and the finite number of classes using a training set with labeled results. There are several works illustrating that DL is suitable for classification problems and can outperform other algorithms [35; 36] which encourage further work in this direction. DL models exist, such as recurrent neural networks (RNNs), that are designed specifically for processing sequential data, and thus could be applied for time series. Here, we have built an RNN model constituted of bidirectional long short-term memory units (Bi-LSTM) [37] to properly classify different classes of crack events. The main motivation for this choice is that Bi-LSTMs are: (i) easier to train than RNNs [38],(ii) effective in the classification and prediction of time-series [34], and (iii) capable of learning long-term dependencies [39; 40]. The performance of the DL neural network is evaluated on a dataset of AE measurements and compared against other DL-based techniques in terms of classification accuracy. Furthermore, results are comparable to other achievements [41; 42; 43] when applying DL to detecting faults in rotating machinery. Notably, an additional benefit of our framework is that it can be embedded into SHM systems for the lifetime monitoring of large-scale structures. The paper is organized as follows. Section 2 provides information about the acquisition system and the experimental setup. Section 3 describes the proposed framework. Section 4 presents results and the discussion followed by conclusions provided in Section 5.