## 5 Results and Discussion
### DCNA-Based Identification Performance Analysis
Moreover, the confusion matrix, which is shown in Figure 14, provided perfect performance (100% accuracy) of fault identification for seven defect types of the experimental MDTF gearbox under variable speed conditions through the four experiments in Scenario 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Size (Percentages of** & **Time Consumption** & **Overall Classification Result** \\
**5600 Samples)** & **Number of Epochs** & **(Second)** & **(\%)** \\ \hline
1680 Samples (30\%) & 160 & 105.101 & 89.51 \\ \hline
2240 Samples (40\%) & 200 & 138.276 & 94.63 \\ \hline
2800 Samples (50\%) & 210 & 147.846 & 99.79 \\ \hline
3360 Samples (60\%) & 250 & 165.569 & 100 \\ \hline
3920 Samples (70\%) & 300 & 375.497 & 100 \\ \hline
4480 Samples (80\%) & 360 & 458.990 & 100 \\ \hline
5040 Samples (90\%) & 410 & 546.832 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The classification accuracy and time consumption for various size of the training set. Figure 11: Frequency spectrum analysis of the vibration sub-band (for fault state D2 at 900 RPM) in the comparison between an input and output sub-band of the ANC module. Figure 12: The flowing learned feature images through layers of the proposed DCNA for one example channel, here, (**a**) RGB input image, (**b**) the 96 kernels of size \(11\times 11\) (**c**), the feature images of the Cv1, (**d**) the feature images of the Pm1, (**e**) the feature images of the Cv2, (**f**) the feature images of the Cv3, (**g**) the feature images of the Cv4, the feature images of the Cv5 (**h**). Figure 13: Three-dimensional clustering spaces of the four experiments: (**a**) Experiment 1, (**b**) Experiment 2, (**c**) Experiment 3, (**d**) Experiment 4. For robustness analysis of the proposed methodology, a comparison was made between the proposed method and existing state-of-the-art methods such as ANR-GRS + SFE + GA + KNN (Fw1), LADT + GA + KNN (Fw2), LADT + FSE + SVM (Fw3), ANR-GRS + CWT + DCNA (Fw4), LADT + STHT + DCNA (Fw5). Those are explained in detail as follows:  
1. ANR-GRS + SFE + GA + KNN (Fw1): This framework used the denoising method as an adaptive noise-reducer-based Gaussian reference for optimizing vibration signals. Next, the handcraft feature extraction technique was used to extract the statistical features in the time and frequency domain (SFE: statistical feature extraction). The achieved feature pool, then, was processed by a feature-selection-method-based genetic algorithm (GA) to fetch the most discriminate features in preparation for input into the learning model as k-nearest neighbors (KNN). KNN performed fault classification based on the selected features (reduced dimensionality) to identify the gear defect types for validating the accuracy of the constructed model (Fw1). The details of Fw1 can be found in [56]. 2. LADT + GA + KNN (Fw2): To validate the improved denoising technique, the LADT module was used instead of the ANR-GRS module in the Fw1 to construct the Fw2. 3. LADT + FSE + SVM (Fw3): This observed framework was created to explore the noise reduction proficiency of LADT, incorporating the high-dimensional feature pool, which can be well-classified by a support vector machine (SVM). The proposed denoising approach (LADT) in this study was applied to optimize vibration signals. The FSE step tried to configure the feature pool. Then, an SVM was utilized to process fault diagnosis by using the extracted features to input learning data [22]. 4. ANR-GRS + CWT + DCNA (Fw4): By implementing this framework, the effectiveness of the LADT module was straightforwardly compared to the initial adaptive noise technique (ANR-GRS). In this situation, we only replaced the LADT module with ANR-GRS. 5. LADT + STHT + DCNA (Fw5): This framework was implemented by using short-time Fourier transform (STHT) to extract the visualized image features as spectrogram images. It was used for comparison with the proposed scheme in the process of enriching feature extraction.