# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
## 4 Methodology Evaluation Results
### Performance Comparison with Difference Model for Classification  
To further validate the performance of the diagnostic method, the proposed DCNN model is compared against several state-of-the-art approaches: (1) K-nearest-neighbor + principal component analysis (KNN+PCA), (2) Multiclass Support Vector Machines + principal component analysis (MCSVM+PCA), (3) LeNet-5, and (4) AlexNet. The KNN and SVM methods using the feature extraction (FE)-based approach where the features are texture features extracted from the images of different types of 2-D representation include vibration image, conventional wavelet spectrogram and the proposed of DSWI. These features are extracted using the uniform local binary pattern method [31]. The method is employed based on the concept of the certain local binary patterns, termed uniform, that are fundamental characteristics of local image texture. The image's occurrence histogram is indicated to be a very useful texture feature. Then, the KNN or MCSVM algorithm is utilized to carry out the fault classification after decreasing the feature space's dimensionality by principal component analysis. The LeNet-5 and AlexNet are two well-known CNN structures commonly used in the literature for image processing. The input of the LeNet-5 and AlexNet is the vibration image, the conventional wavelet spectrogram and the DSWI analogous to the input of the proposed DCNN. The experiment comparing the DCNN with the other approaches in literature is conducted with the same dataset that is used to evaluate the proposed model. The recorded dataset used to evaluate the proposed DCNN and other machine learning models is detailed in Section 2. The prediction accuracy for the testing part of the dataset for each implemented method is gathered and presented in Table 2. As can be seen from Table 2, the other 2-D representation methods (i.e., vibration image and conventional wavelet spectrogram) showed inferior fault diagnostic performance when compared to  
Figure 7: The confusion matrix for the test dataset with the proposed DCNN using different types of input image: (**a**) Vibration image (**b**) Conventional Wavelet spectrogram image (**c**) DSWI.  
the DSWI approach employed for the signal processing step. Thus, the comparison results show that the proposed DSWI clearly outperformed the other types of 2-D representations for all experimental scenarios with different classifier methods.  
Table 2 also presents a collation of the other classifier models that are investigated with the proposed DCNN. Therefore, by comparison with the recently researched deep learning architectures, our approach provides a better result. The results show that the proposed DCNN approach attains a result superior to that of the other methods. The prediction accuracy is 98.79%, 97.98%, 95.97%, 87.76% and 61.63% for proposed DCNN, AlexNet and LeNet-5, MCSVM+PCA, and KNN + PCA, respectively. This result also shows the superior performance of the proposed DCNN approach. For the KNN + PCA and MCSVM + PCA which are based on the feature extraction method, the results illustrate lower accuracy because they depend on the characteristic of features, while the design of features needs the help of the experts for different types of application. The results from the LeNet-5 and AlexNet showed high accuracies proximate to the proposed DCNN. However, the LeNet-5 is the simplest architecture and is not a strong enough structure for learning the information from the DSWI which is highly complex. AlexNet gives a better result but it is more complex and requires more time spent on training. According to the results reported in Table 2, the diagnostic performance of the DCNN is best in all scenarios.