## 4 Discussion  
In the literature review, a clear trend since 2016 of using several feature extraction techniques, such as Fast Fourier Transform, Wavelet Package Energy Transform and Empirical Mode Decomposition (FFT, WPE and EMD), along with a classifier based on a DNN, is identified. Two-dimensional CNNs have been one of the most commonly used networks [32], but one-dimensional ones have not yet been applied to the same extent. One of the first investigations undertaken is presented in [30], where an accuracy of 100% is yielded with a processing time of 62 ms using a seven-layer model with the database reported in [22], requiring around 20,000 signals. The second investigation reported in [31] is a validated eight-layer model with around 20,000 data from the CWRU bearing database, reaching an accuracy of 99.77% with a very high capacity of noise robustness. A similar work is reported in [33], where a performance of 99.27% with 10,000 CWRU signals is achieved. Additionally, a comparable study is described in [34; 35], where the accuracy rate is 100%, 99.76%, 99.77% and 99.57% with more than 177,000 CWRU signals, and 99% with 3500 CWRU signals, respectively. The third investigation reported in [36] is a deep auto-encoder method with fusing discriminant, where the IMS dataset is classified with an accuracy rate of 98.3%. Similar research is proposed in [37], where a methodology based on Deep Feature Learning for fault identification is applied. Each sample consists of 3000 data points; where a multi-domain feature calculation is applied, the accuracy is 99.8%. Finally, another method reported in [38] has above 99% accuracy rate using an optimized method of stacked variational denoising auto-encoder. In comparison, the proposed model has  
* a lower number of layers (four convolutional, one fully connected, one Softmax),
* a reduced processing time of 8 ms per signal,
* an acceptable training time (~14 min) and
* a maximum performance of 99.64% with a standard deviation of 0.25%,  
requiring an acceptable dataset (1904 signals) to reach maximum performance with three databases (see Table 8). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & **Categories** & **Training** & **Testing** & **Training Time** & **Testing Time** & **Accuracy** & **Standard** \\  & **[\# of Classes]** & **[\# of Signals]** & **[\# of Signals]** & **[\# of Signals]** & **[s]** & **[s]** & **[\%]** & **[\%]** \\ \hline
1 & 5 & 1500 & 370 & 147.76 & 0.3363 & 99.01 & 0.5916 \\
2 & 5 & 1500 & 370 & 148.71 & 0.3400 & 98.29 & 0.6788 \\
3 & 4 & 1200 & 296 & 119.16 & 0.2830 & 99.54 & 0.1814 \\
4 & 4 & 1200 & 296 & 121.84 & 0.3029 & 99.82 & 0.1543 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: 1D-CNN performance when only using classes that are difficult to classify. Figure 10: Identification of difficult classification categories. (**a**) Confusion matrix of IMS, (**b**) t-SNE representation of IMS. The method of bearing failure diagnosis has been developed on the basis of feature learning approach. That is, the -CNN is divided into two main procedures: (1) feature extraction/selection that is undertaken by convolutional filters (Kernels) and (2) classification based on Softmax layer. The proposed model for bearing failure detection uses fewer layers and a different filter structure in comparison with similar networks. Furthermore, the network reaches a maximum accuracy of over 99% with three bearing databases and has a low average standard deviation (0.25%) for the multiple combinations and configurations in the training process. Generalizability and repeatability are achievable by selecting appropriate configuration parameters and structure. DLNN presents high computational efficiency and achieves high performance accuracy with raw input signals. DLNNs have the capability to extract and classify features in a single structure, achieving a reliable diagnosis with low-cost computational processing in comparison with traditional feature extraction methods (WPE, EMD, HHT, etc.)