# A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions
## 3 Validation of Proposed SRDCNN Model
### Case Study III: Performance across Different Working Loads  
In this section, the adaptation performance of the proposed SRDCNN model under different working loads is investigated. The experimental settings are illustrated in Table 3. The dataset adds additive white Gaussian noise and the SNR value is fixed at 6.  
The results of the experiments are shown in Figure 17. It can be seen that LSTM-CNN model performs poorly in generalization test under different working conditions, and its average classification accuracy in the six scenarios is approximately 63.3%. In addition, 1d-DCNN and 2d-DCNN model show better workload adaptability, and they achieve average classification accuracy of 87.7% and 83.9%, respectively. The proposed SRDCNN model outperforms the other three models, and it achieves an average classification accuracy of 94.7%. From the above results, it can be concluded that the features learned by the SRDCNN model from raw signals are much more essential than that of 1d-DCNN, 2d-DCNN, and LSTM-CNN models.  
It should be noted that the 1d-DCNN model achieves high diagnosis accuracy of approximately 89% from dataset B to C and dataset D to C, but its diagnosis accuracy drops by 2% from dataset C to B and dataset C to D. As for the 2d-DCNN model, it achieves high diagnosis accuracy of approximately 95% from dataset B to D, but it remains only 71.4% from dataset D to B. Moreover, the LSTM-CNN model obtains its highest diagnosis accuracy of 71.6% from dataset C to D and its lowest diagnosis accuracy of 56.4% from dataset C to D. In contrast, the SRDCNN model obtains the classification accuracy of over 84% irrespective of the source dataset and target dataset. On the whole, the proposed SRDCNN based intelligent fault diagnosis method achieves the best fault diagnosis effect in almost all the generalization experiments, except from dataset D to B. The above results further demonstrate that  
Figure 17: Results of these three methods under different working loads.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Source Dataset** & \multicolumn{2}{c}{**Target Dataset**} \\ \hline \multirow{3}{*}{Dataset details} & Training set B & Test set C & Test set D \\  & Training set C & Test set B & Test set D \\  & Training set D & Test set B & Test set C \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiments Scenario settings for load adaptation.  
the proposed SRDCNN model is able to learn to extract the essential features and avoid local false features caused by fluctuations in the working conditions.  
To visually understand the learning effects of the SRDCNN model, it is first trained using the training set of dataset C, and then its feature representation distribution on test set D is shown in Figure 18. It can be observed that there are a few crossovers between data points of different categories. Thus, it can be naturally concluded that the proposed SRDCNN model successfully learns the essential feature representation, which can be effectively used for fault diagnosis on different working loads.  
In conclusion, with the raw vibration signal as the input, the 1d-DCNN model has the weakest denoising ability, but it has good workload adaptation. Conversely, the LSTM-CNN model has good denoising ability and poor workload adaptation. As for the 2d-DCNN model, it makes a compromise between denoising ability and workload adaptability. Compared with 1d-DCNN, 2d-DCNN, and LSTM-CNN models, the proposed SRDCNN model has higher denoising ability and better workload adaptability.