# A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
## 4 Experiment Results and Discussion
### FD Model Preselection Results  
To select a suitable FD model and signal preprocessing method, the SVM and one-dimensional CNN (1 DCNN) are trained and validated based on the experimental data, and the structure and main parameters of the trained 1 DCNN are recorded in Table 6. The main parameters of the SVM referred to in [23] are used here, as shown in Table 7.  
The validation accuracy results of the 1 DCNN and SVM with different multi-signal processing methods are recorded in Table 8. From this table, the mean of the ten-fold cross-validation accuracy results of all SVM and 1 DCNN models are larger than 0.8. The mean accuracy of 1 DCNN (fusion) and SVM (fusion) is very close to 1, while the mean accuracy of 1 DCNN (together) and SVM (together) is less than 0.9, which means the mean fusion method should be selected. According to the model preselection algorithm, SVM \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Type** & **Specific Setup** & **Parameters Number** & **Output Shape** \\ \hline Input layer & Sample length = 162 & 0 & (None, 162, 1) \\ Conv1D\_1 (ReLU) & Filters = 16, kernel size = 9, stride = 1 & 160 & (None, 154, 16) \\ Pooling\_1 & Max pooling size = 2, stride = 1 & 0 & (None, 77, 16) \\ Conv1D\_2 (ReLU) & Filters = 32, kernel size = 5, stride = 1 & 2592 & (None, 73, 32) \\ Pooling\_2 & Max pooling size = 2, stride = 1 & 0 & (None, 36, 32) \\ Conv1D\_3 (ReLU) & Filters = 64, kernel size = 3, stride = 1 & 6208 & (None, 34, 64) \\ Flatten & - & 0 & (None, 2176) \\ FC (ReLU) & - & 139,328 & (None, 64) \\ Dropout & 0.5 & 0 & (None, 64) \\ FC (ReLU) & - & 2080 & (None, 32) \\ Dropout & 0.5 & 0 & (None, 32) \\ FC (SoftMax) & - & 198 & (None, 6) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The structure and parameters of the 1 DCNN. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fold** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Mean** \\ \hline OCSVM (together) & 0.356 & 0.342 & 0.356 & 0.342 & 0.375 & 0.368 & 0.38 & 0.382 & 0.384 & 0.383 & 0.367 \\ OCSVM (fusion) & 0.56 & 0.564 & 0.56 & 0.564 & 0.502 & 0.547 & 0.559 & 0.555 & 0.517 & 0.534 & 0.546 \\ AKPCA (together) & 0.717 & 0.705 & 0.717 & 0.705 & 0.711 & 0.734 & 0.737 & 0.733 & 0.732 & 0.721 \\ AKPCA (fusion) & 0.871 & 0.872 & 0.871 & 0.872 & 0.862 & 0.852 & 0.866 & 0.85 & 0.69 & 0.7 & 0.83 \\ ISF (together) & 0.419 & 0.453 & 0.431 & 0.428 & 0.483 & 0.467 & 0.466 & 0.461 & 0.446 & 0.481 & 0.793 \\ ISF (fusion) & 0.817 & 0.821 & 0.841 & 0.828 & 0.793 & 0.826 & 0.831 & 0.845 & 0.798 & 0.767 & 0.817 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The accuracy results of validation samples. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Fold** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Mean** \\ \hline OCSVM (together) & 0.926 & 0.924 & 0.926 & 0.924 & 0.929 & 0.928 & 0.928 & 0.929 & 0.929 & 0.929 & 0.927 \\ OCSVM (fusion) & 0.643 & 0.641 & 0.643 & 0.641 & 0.615 & 0.638 & 0.659 & 0.667 & 0.654 & 0.651 & 0.645 \\ AKPCA (together) & 0.38 & 0.391 & 0.38 & 0.391 & 0.384 & 0.387 & 0.385 & 0.383 & 0.379 & 0.371 & 0.383 \\ AKPCA (fusion) & 0.148 & 0.146 & 0.148 & 0.146 & 0.12 & 0.146 & 0.146 & 0.146 & 0.146 & 0.144 \\ ISF (together) & 0.815 & 0.772 & 0.804 & 0.802 & 0.748 & 0.768 & 0.802 & 0.811 & 0.829 & 0.777 & 0.453 \\ ISF (fusion) & 0.237 & 0.221 & 0.203 & 0.227 & 0.229 & 0.224 & 0.227 & 0.229 & 0.234 & 0.245 & 0.227 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The \(FNR\) results of validation samples. (fusion) with the highest mean value of cross-validation accuracy (0.996) is regarded as the best FD model. The processing of multi-channel signals herein differs from that in reference [37]. Directly inputting multi-channel data into neural networks could achieve good results, subject to the size of the dataset. \(f_{1}\)-_score_ is the evaluation parameter composed of _Precision_ and _Recall_; the calculation is realized by Equations (16)-(18).