# Fault Diagnosis of Universal Circuit Breakers Based on Variational Mode Decomposition and WOA-DBN
## 5 Conclusions  
This paper suggests an approach based on VMD and WOA-DBN to diagnose the different types of faults in universal circuit breakers. The approach extracts features from the modal components in the time and frequency domains using variational modal decomposition to decompose the original vibration signal. The fault diagnostic model uses these feature vectors as its dataset. For the purpose of training DBN around the ideal solution, WOA was used to conduct a global search on the network learning rate of DBN, the number of backward fine-tuning, and the number of neurons in the hidden layers of DBN. The optimal hyperparameters of the DBN fault diagnosis model were obtained. Fault data of universal circuit breaker model HSW1-2000 were selected for validation, and SVM, BPNN, LSTM, SDAE, and DBN were chosen as diagnostic methods for comparison. According to the results, the experimental WOA-DBN had a diagnostic accuracy of 96.63%, with a standard deviation of 0.0103. Compared with the comparison model, the WOA-DBN could select more suitable training parameters, effectively distinguish different features, and had evident benefits in the area of classification recognition accuracy, which effectively solved the inaccuracy of the single vibration signal feature extraction with the problem of low accuracy of the fault diagnosis model, as it could accurately achieve the fault classification and identification of universal circuit breakers. The empirical results indicate \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Standard**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**Time (s)**} \\  & & & & & \\ \hline SVM & 0.7253 & 0.0247 & 0.7309 & 0.7328 & 0.7326 & 0.029 \\ BPNN & 0.7938 & 0.0374 & 0.7917 & 0.7938 & 0.7934 & 0.025 \\ LSTM & 0.7929 & 0.0153 & 0.7911 & 0.7971 & 0.7948 & 0.017 \\ SDAE & 0.8403 & 0.0191 & 0.8421 & 0.8403 & 0.8402 & 0.031 \\ DBN & 0.8631 & 0.0167 & 0.8677 & 0.8631 & 0.8650 & 0.039 \\ WOA- & 0.9663 & 0.0103 & 0.9702 & 0.9663 & 0.9668 & 0.052 \\ DBN & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification results of each model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**VMD Parameters** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \(K=6,\alpha=2000\) & 0.9160 & 0.9204 & 0.9160 & 0.9167 \\ \(K=6,\alpha=3000\) & 0.9385 & 0.9413 & 0.9385 & 0.9386 \\ \(K=6,\alpha=4000\) & 0.9244 & 0.9310 & 0.9244 & 0.9249 \\ \(K=8,\alpha=2000\) & 0.9308 & 0.9349 & 0.9308 & 0.9312 \\ \(K=8,\alpha=3000\) & 0.9663 & 0.9702 & 0.9663 & 0.9668 \\ \(K=8,\alpha=4000\) & 0.9411 & 0.9420 & 0.9412 & 0.9414 \\ \(K=10,\alpha=2000\) & 0.9077 & 0.9126 & 0.9077 & 0.9075 \\ \(K=10,\alpha=3000\) & 0.9231 & 0.9257 & 0.9230 & 0.9231 \\ \(K=10,\alpha=4000\) & 0.9154 & 0.9214 & 0.9154 & 0.9160 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: WOA-DBN-based performance evaluation of diagnostic models under diverse VMD parameter settings. that the fault diagnosis approach proposed in this paper provides fresh concepts and approaches for fault diagnosis using vibration signals, and the proposal of this model will not only provide the necessary guidance for the research of universal circuit breaker fault diagnosis but also can be used to fault diagnosis of rolling bearings, rotating machinery and other mechanical equipment that generates vibration signals. But the method presented in this paper still presents issues that deserve further research; for example, no attempt has been made to fuse vibration signals, current signals, and sound signals to perform fault diagnosis together. Therefore, the next step will be to introduce multi-category data for information fusion and try to use more advanced algorithms to optimize the model to further improve the generality of the proposed method. Conceptualization, G.L. and H.D. ; Methodology, G.L. ; Software, X.C. ; Formal analysis, H.D. ; Investigation, T.Z. ; Data curation, S.D.