# A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine
## 3 Results
### Ablation Experiment  
To verify the relative importance of both the feature-selection strategy and the BCOA algorithm for the BCOA-KELM algorithm, we divided the experiment into four groups: KELM, BCOA-KELM, KELM after feature selection, and BCOA-KELM after feature selection. We compared their FDR to verify the importance of both in the algorithm.  
The results of the ablation experiments are shown in Table 8. Among the four groups involved in the comparison, the lowest FDR was that of the KELM algorithm alone, whose FDR was only 71.18%; the highest FDR was the feature-selected BCOA-KELM algorithm, whose FDR was 89.32%.  
When using BCOA-KELM, the FDR of BCOA-KELM improved by 5.90% over that of KELM for the two sets of experiments without feature selection, and the FDR of BCOA-KELM improved by 16.62% over that of KELM for the two sets of experiments after feature selection. This proves that the BCOA algorithm we used can optimize the KELM very well.  
When using feature selection, the FDR was improved by 15.18% in the two sets of experiments using the KELM algorithm after feature selection than the KELM without feature selection. In the two sets of experiments using the BCOA-KELM algorithm, the FDR was improved by 12.23%. Feature selection showed a small FDR improvement of 1.52% for the KELM algorithm, while feature selection showed a large FDR improvement for the BCOA-KELM algorithm, which proves the effectiveness of feature selection and also proves that a good combination of BCOA search and feature selection can better improve the fault diagnosis rate of the algorithm.  
To further validate the relative importance of both for the BCOA-KELM algorithm, we also used the FPR metric to elucidate this result.  
The results of the ablation experiments are shown in Table 9. Among the four groups involved in the comparison, the highest FPR was that of the KELM algorithm alone, whose FPR was only 0.3032; the lowest FPR was the BCOA-KELM algorithm after feature selection, whose FPR was 0.1157.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Without Feature Selection**} & \multicolumn{2}{c}{**Feature Selection**} \\ \hline
**Fault** & **KELM** & **BCOA-KELM** & **KELM** & **BCOA-KELM** \\ \hline  
& 71.18 & 77.08 & 72.70 & 89.32 \\ \hline \hline \end{tabular}
\end{table}
Table 8: FDR (%) of the base algorithm and related improved algorithms.  
When BOCA-KELM was used, the FPR of BCOA-KELM was reduced by 0.1157 compared with that of KELM for the two sets of experiments without feature selection, and the FPR of BCOA-KELM was reduced by 0.1739 compared with that of KELM for the two sets of experiments after feature selection. Overall, the combination of BCOA algorithm and KELM effectively reduced the FPR, which proves the optimization effect of BCOA for KELM.  
When using feature selection, the FPR was reduced by 0.0136 for the two sets of experiments when using the KELM algorithm after feature selection, compared to the KELM without feature selection, and by 0.1127 for the two sets of experiments using the BCOA-KELM algorithm after feature selection compared to the BCOA-KELM algorithm without feature selection. This also proves the importance of feature selection in this algorithm.