# A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine
## 4 Discussion and Conclusions  
On the basis of ELM, the BCOA-KELM method was presented for TE process fault diagnosis. Kelm is used to diagnose and classify problems. The internal parameters c and g, however, have an impact on KELM's performance. To optimize this parameter, BCOA is employed. The proposal of BCOA and combination of the KELM and BCOA algorithms is the paper's most significant contribution. The algorithm's overall performance and accuracy are improved, while its training time is lowered. The F1-score and the model's accuracy outcomes are compared to determine the model's efficacy. One of the key parameters to consider when evaluating defect diagnosis systems is classification accuracy. Overfitting issues, on the other hand, can cause a discrepancy in accuracy results. However, overfitting problems can lead to a mismatch between accuracy results and fault diagnosis ability, which can be reflected by the F1-score. As a result, combining the two can more properly reflect the model's diagnostic results. The experiments reveal that BCOA-KELM has a fast training time, higher classification accuracy in terms of fault diagnosis than other algorithms, and a significant improvement in diagnostic accuracy for fault 16. The model outperforms the commonly used diagnostic models in terms of diagnostic findings. As a  
\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  
& 0.3032 & 0.2284 & 0.2896 & 0.1157 \\ \hline \hline \end{tabular}
\end{table}
Table 9: FPR of the base algorithm and related improved algorithms.  
result, BCOA-KELM can be used to diagnose Tennessee Eastman process faults as well as other classification and prediction issues.  
Although the method has achieved some good results, there are still some limitations that need to be improved in future work. First, the quality of the raw data has a significant impact on the performance of the method, which is an important reason for the low correctness of partial fault diagnosis. A specific data-cleaning process for partial fault datasets is crucial in practical applications. Second, although our optimization of a large number of network hyperparameters of KELM leads to a significant improvement in the diagnostic performance of KELM, there is an upper limit to the accuracy of the KELM classifier. Next, we have to optimize the structure of the classifier itself. Third, for the extracted features, we can combine the characteristics of the chemical process itself and consider the TE process itself rather than just the data-driven diagnostic aspects to explore the chemical connection between the feature variables, which will be a new cross-optimization direction. Finally, the time complexity of the proposed approach needs to be considered during the design and validation of deep learning models. For example, when low-level features are sufficient for high-precision fault diagnosis, there is no need to extract high-level features of the chemical process, which may help to improve efficiency.  
Conceptualization, X.H. In addition, M.H.; methodology, X.H.; software, X.H.; validation, X.H. In addition, M.H.; formal analysis, X.H. In addition, M.H.; investigation, X.H.; resources, X.H., M.H. In addition, X.Y.; data curation, X.H. In addition, M.H.; writing--original draft preparation, X.H. In addition, M.H.; writing--review and editing, X.H. In addition, M.H.; visualization, X.H. In addition, M.H.; supervision, X.Y.; project administration, X.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.