# A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
## 4 Experiment Results and Discussion
### The Test Results of the Whole Condition Monitoring Method  
Based on the results of the above models with two methods utilizing multi-channel vibration data on the validation dataset, the mean fusion is selected as the multi-channel data processing method, and AKPCA is selected as the UAD model, while the SVM is selected as the FD model. Therefore, the framework consists of AKPCA and the SVM within mean fusion processing five channels of vibration signals, as shown in Figure 6. \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
1 DCNN (together) & 0.927 & 0.967 & 0.764 & 0.931 & 0.915 & 0.876 & 0.963 & 0.941 & 0.912 & 0.711 & 0.89 \\
1 DCNN (fusion) & 0.997 & 1 & 0.996 & 0.997 & 0.992 & 0.996 & 0.997 & 0.999 & 0.996 & 0.963 & 0.993 \\ SVM (together) & 0.842 & 0.842 & 0.842 & 0.842 & 0.842 & 0.842 & 0.842 & 0.842 & 0.842 & 0.842 \\ SVM (fusion) & 0.996 & 0.996 & 0.996 & 0.996 & 0.996 & 0.996 & 0.996 & 0.996 & 0.996 & 0.996 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The ten-fold cross-validation accuracy results comparison of 1 DCNN and SVM. Figure 6: The online condition monitoring model with specific algorithms. \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
1 DCNN (together) & 0.924 & 0.962 & 0.781 & 0.918 & 0.917 & 0.859 & 0.962 & 0.922 & 0.91 & 0.748 & 0.89 \\
1 DCNN (fusion) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0.974 & 0.997 \\ SVM (together) & 0.838 & 0.838 & 0.838 & 0.838 & 0.838 & 0.838 & 0.838 & 0.838 & 0.838 & 0.838 & 0.838 \\ SVM (fusion) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The \(f_{1}\)-_score_ results of the ten-fold cross-validation of the 1 DCNN and SVM. After obtaining the detailed models of the framework, all samples' test results accuracy and _FNR_ of AKPCA (fusion) are 0.83 and 0.144. All samples' test results in terms of the accuracy and _f1-score_ of SVM (fusion) are 0.996 and 1. The confusion matrix of SVM (fusion) is shown in Figure 7. The confusion matrix shows that most faults are diagnosed correctly, but 16.7% of coupling misalignment (CM) and 16.7% of casing ring wear (CRW) samples are misdiagnosed. The diagnostic accuracy of SVM (fusion) for each fault is greater than 0.8, which meets the GB/T 43555-2023 [36] standard requirement for qualified models. That is, SVM (fusion) models are qualified. Each status's test samples are independently evaluated using the entire framework, with results detailed in Table 10. According to AKPCA's findings, the model successfully identified coupling misalignment, mass eccentricity, casing ring wear, blade wear, bench instability, and strong blade wear (two-blade wear, not learned by the SVM), all with _ADR_ exceeding 10% [23]. However, normal status samples, with an _ADR_ of 11%, were incorrectly detected as abnormal status samples. In the SVM's results, the unlearned strong blade wear fault was identified as bench instability with 58.3% probability, while other faults were diagnosed correctly with probabilities exceeding 80%. No abnormal status information would be obtained in scenarios where only a UAD module is available. Conversely, if only an FD model were used, the unlearned fault might be misclassified as one of the learned faults, resulting in a misdiagnosis. By employing the information integration algorithm, consistent results from both the UAD and FD models accurately identify all classical faults with 100% accuracy. Inconsistent results, such as normal status and unlearned strong blade wear, are flagged for expert