## 4 Experimental Results
### Performance Across Different Load Domains on CWRU Dataset  
Another important test for the new approach is training the model on a specific sub-dataset and identifying the fault types belonging to another sub-dataset. For example, the model trained on a sub-dataset #1 (0 HP load) should be able to recognize the fault types under other loads. Obviously, after the load shifts, the diagnostic accuracies of any models decreased significantly. Table 7 contains the results of training the LPC-NN method on one class only and predicting defects in other classes. The last four results in Table 7 correspond to the cases when the model trained on the entire dataset could perfectly predict classes for all sub-datasets. It can be seen that the accuracy of the method dropped significantly if the operating conditions of the bearing in the training and test datasets differed greatly. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sub-Dataset** & **CNN-gcForest [4], \%** & **LPC-NN** \\ \hline
2 & 98.24 & 99.73 \\
3 & 99.51 & 100 \\
4 & 99.79 & 100 \\ Entire dataset & 99.2 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparing results of the proposed solution with solution in [4]. Figure 14: Confusion matrix of sub-dataset #2 (for two runs). A similar experiment was carried out by [PERSON] [31] although not all cases were considered. Note that the datasets A, B, and C in the paper [31] correspond to the sub-datasets #2, 3, and 4 in our paper. Table 8 summarizes the comparison between this work and the TICNN and Ensemble TICNN approaches in [31]. Note that the average accuracies for the methods SVM, MLP, DNN, and WDCNN are 66, 80, 78, and 90% [31], respectively, and lower than the average accuracy for LPC-NN. The results in Table 8 show that the LPC-NN model is superior to Ensemble TICNN in the cases \(3\to 2\) and \(4\to 2\). In other cases, the LPC-NN model gives worse results, but even in these instances, the methods have comparable accuracy. Note that it is not mentioned in [31] how the data were divided into training and test sets. It remains unclear whether these two sets were non-intersecting, as in our case. To study the influence of the number of LPC coefficients on the accuracy of the model, we also conducted the following experiment: We considered the number of LPC coefficients equal to 12, 50, and 100 and solved the problem of class prediction on the test sub-dataset 2 while training the model on sub-dataset 1. We thus considered three neural network models N-50-10 for N = 12, 50, 100. Since the neural network training results are subject to stochastic changes, we trained each model 100 times and found the average accuracy of the model for each specific structure. The average accuracy value along with the time to train 100 models is given in Table 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Class Label**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Class Label**} & \multicolumn{1}{c}{**Average**} \\
**Training** & **Test** & **Accuracy, \%** & **Training** & **Test** & **Accuracy, \%** \\
**Sub-Dataset** & **Sub-Dataset** & **Sub-Dataset** & **Sub-Dataset** & **Sub-Dataset** & **Sub-Dataset** \\ \hline
1 & 2 & 97.82 & 3 & 4 & 96.95 \\
1 & 3 & 91.72 & 4 & 1 & 83.82 \\
1 & 4 & 88.54 & 4 & 2 & 93.37 \\
2 & 1 & 99.25 & 4 & 3 & 98.14 \\
2 & 3 & 98.49 & All & 1 & 100 \\
2 & 4 & 91.06 & All & 2 & 100 \\
3 & 1 & 93.79 & All & 3 & 100 \\
3 & 2 & 93.77 & All & 4 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of training the LPC-NN method on one class and testing in other classes.