## 5 Results and Discussion
### DCNA-Based Identification Performance Analysis  
By applying the LADT method, the noise components of the vibration signals were mostly removed. The wealthy feature pool configuration based on CWT, then, translated the output of LADT as insignificant-noise vibration sub-bands to the scalogram images. These scalogram images carried enough information for fault discrimination. The wavelet-based vibration image datasets were used as input datasets for DCNA for the classification task. First, the proposed network tried to perform Scenario 1 to discover the effect of the quantity of input data on the time consumption and classification accuracy. The dataset, which contained all four speeds and seven categories, was randomly split into the training set and validation set. Each input sample was a colorized image with dimensions of 224 \(\times\) 224 \(\times\) 3, which met the demand of the input layer size of the proposed DCNA. From the numerous proportions of the training set, the computational consumptions and accuracies are listed in Table 5. It shows that when 50% to 60% of total samples were used for training, the best performances were obtained (by high accuracies in the acceptable time consumption) in the observed quantities. Thus, a ratio of 60% was used in this study. In Scenario 2, four experiments (in Table 4) were executed in this study to analyze the accuracy and reliability of the proposed framework for an MDTF gearbox under differential speed conditions. In each of the four experiments, the training dataset was composed of two different speed samples (2800 samples), and the data samples of the validation set contained samples collected at speeds that differed from that of the training dataset (1400 samples). Following Scenario 2, the speed-varying datasets were alternately used for the training and testing process over a total of four observed rotational speeds in this paper. The learned features of the activation processes in different layers of the applied network model can be seen in Figure 12. From the input RGB image of the defect type 3 with a speed of 600 RPM (Figure 12a), through the beginning steps of the high-dimensional feature extraction process, performed by 96 kernel filters (Figure 12b) of the first convolutional layer (Cv1), the feature images of the Cv1 of one channel are shown in Figure 12c. With the help of this process, the one time-frequency domain vibration image is mapped to 96 feature images for observing the defect-related elements in high-dimensional feature spaces. Next, the several mapping values in feature images are reduced by the max-pooling layer (Pm1) as shown in Figure 12d. Thus, the feature image in Figure 12d is inclined to be viewed more dubiously and softly than Figure 12c. From Figure 12e to Figure 12h, the complex learned feature images from Cv2 to Cv5 of an example channel are demonstrated the impacts of the kernels of those layers. After flowing through Cv and Pm layers of the applied DCNA network, the learned feature maps were flattened as feature vectors. Those feature vectors, which were outputs of the final fully connected layer (Fc3), were then used as input of a SoftMax layer or output layer for clustering. The t-SNE (t-stochastic neighbor embedding) approach is popular in deep networks for exploring the feature spaces. Figure 13 depicts the three-dimensional distribution of the output feature vectors from the Fc3 layer according to seven defect categories through four experiments. As shown in Figure 13, the samples of the same defect type were close to each other, separate from the samples of another defect type. The clear discrimination between defect types verifies the high accuracy and stable capability of the proposed framework through the condition of the inconsistent speed. Based on this, the classification process can identify the defect types of an MDTF gearbox more easily.