## 3 Experiment Result  
### Performance Evaluation  
In the experiments of this study, the accuracy, precision, recall, and F1-Score were measured using True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). The accuracy, precision, recall, and F1 Score can be expressed using Equations (4)-(7), respectively. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]\[F_{1}Score=2\times\frac{\textit{Precision}\times\textit{Recall}}{\textit{Precision}+ \textit{Recall}} \tag{7}\]  
At this time, to demonstrate the superiority of the methods used in this experiment, they were compared with one of the most commonly used methods, the method of applying SVM (Support Vector Machine), after feature selection based on the GA (genetic algorithm) after extracting the hand-crafted features from a raw signal and [3]. The [3] method is a method of applying SVM by mixing a GA and PCA (Principal Component Analysis) from a list of hand-crafted feature values through feature engineering. The proposed methods were also compared with the MLP (Multi-Layer Perceptron) method [5] from the same feature engineering [3] to determine if it shows better performance in training after data visualization. Table 3 lists the experimental results. Under Normal, Rubbing, Unbalance, and Misalignment conditions, the proposed methods showed better performance than the existing methods [3,5]. In this study, an attempt was made to improve performance through k-fold cross-validation, but the following problems were encountered. First, the accuracy was low compared to the results not applied because the number of datasets was not large. Second, the experimental results of the current dataset were not applied because they were unnecessary owing to the very high accuracy. The performance of the deep learning methods was superior to that of a method based on MLP or SVM, as listed in Table 3. This can be attributed to a large amount of information that cannot be expressed as features that are lost when selecting the features of input data in the preprocessing stage. Although all hand-crafted features were selected and learned using the MLP algorithm, a performance equal to or better than that of deep learning could not be achieved. As shown in the results in Table 3, the results of our DNN models using two methods transforming raw data into images through STFT and MFCC were almost identical. Any preprocessing method such as STFT or MFCC doesn't impact to extracting semantic information from CNN filters and output metric of the DNN model. Table 4 compares the training results based on the dataset that has undergone an STFT transformation with the existing deep learning model [6, 7, 8, 33]. The training hyperparameters of each model were trained under the same conditions, as listed in Table 2. Table 4 shows that DNN is superior to traditional machine learning models with hand crafted characteristics. In addition, in the case of the proposed model, the number of SqueezeNet parameters was large, but the performance of Equations (4)-(7) was excellent. Also, if we compare our method with two Alex Net and VGG19, the performance of the equations are the same but the parameters are much lower than others. As a result, it was confirmed that the proposed model works well with GAP without the existing FCL.