## 3 Results and Discussion  
The training material is drawn randomly from 80% of the whole data set; the testing material is taken from the remaining 20%. In both the training and testing phases, each class has an equal share, e.g., 56 and 14, respectively. As a means of comparing the two proposed classification approaches fairly, the training and testing samples from the first algorithm, that is, the LSTM, are saved to be applied in the second method as well. Initially, the extracted features from the WTS were fed into the designed classification models. In Figure 6, the training progression is plotted along with the accuracy and loss for each epoch in the LSTM network. The training process was run on a single GPU (NVIDIA Quadro P2000) of a workstation and took 75 s. From Figure 6, it can be understood that the training accuracy increased gradually parallel with a sensible reduction in loss after each epoch. Figure 6: Training process of the LSTM. The confusion matrices in the training and testing steps are also represented by the graphs in Figure 7. Only 1 sample out of the 112 that were assigned to the training process is incorrectly classified in the validation, despite the testing accuracy being 100%. Figure 8 presents the confusion matrices of the training and testing phases when the SVM model is applied as the classifier for the explored features by the WTS. The training accuracy in the method was 100%, whereas, in the testing phase, 1 sample among the 28 samples, e.g., 3.6%, is classified mistakenly. Even though there has been a drop in performance between the training and testing phases, a 3.6% drop in performance does not necessarily mean that the network is overfitted. When a network becomes excessively customized to the training data and then performs horribly on unobserved test data, this is known as overfitting. The problem's inherent complexity and variations in the test data, such as outliers, can both result in a 3.6% reduction in performance. The whole procedure of executing WTS and SVM on the same workstation in a single GPU took only 42 s.  
Now that the previous two classification algorithms have been trained and tested using features extracted by WTS, the same procedures should be tested with raw signals. There are two possible methods for using LSTM for time series signals: The first is to feed the signals directly into the network, while the second is to apply a feature extraction step before the LSTM network; in this work, the first approach is followed. The same parameters listed in Table 2 were used to insert the time series data into the LSTM network directly, and both the training and testing phases produced an accuracy of 50% (the confusion matrices are not plotted); indeed, the network assigned Class 1 to every system that had a fault. Performing hyperparameters tuning via a grid division tantamount to 20, the accuracy in both steps--training and testing--improved by 62.5% and 64.3%, respectively. On this condition, the optimized learning rate, hidden layers, mini-batch size, and maximum epoch were found to be 0.2, 4000, 9, and 1000, respectively. However, the outcomes are still far from the previously attained feature extraction and trained LSTM model. Figure 8: Confusion matrix using WTS and SVM: (a) training step and (b) testing step. Figure 7: Confusion matrix using WTS and LSTM: (a) training step and (b) testing step. Figure 9 represents the training and testing stages' performance of the adjusted LSTM network in the form of confusion matrices. The effectiveness of LSTM models in analyzing raw signals was investigated in previous sections. The results, however, demonstrate that the raw signals are not highly instructive. As a result, in this section, a different feature extraction strategy is aimed to be examined. The objective is to assess this new scenario's effectiveness before putting the created SVM model to use for classification.