## 5 Conclusions  
The technique proposed here is well-suited for BRB detection in synchronous motors that start with a squirrel cage. The reason is that these electrical machines carry significant currents during the transient state, and it is difficult to diagnose the failure based on the monitoring of the conventional steady-state current. According to the non-stationary characteristics of BRB faulty signals, a fault-diagnosis method based on DWT is put forward in this paper. The main advantage of the proposed DWT approach is that the feature of a specific fault mode is not at the same frequency band as the fundamental, and hence is not affected by the main frequency, as shown in Figure 3. In this research, the importance of starting load on broken bar detection in LS-PMSMs is also considered, and the results show that the log-energy entropy feature is highly sensitive to the degree of load complexity. Accordingly, the other advantage of this method is that fault detection and diagnosis can be performed under different load conditions. Since the study of faults in LS-PMSM has just started, it is of interest to propose further overall works according to the results achieved. Our future research will be on the following topics: (1) to extend the detection method for the diagnosis of broken rotor  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Coefficient Decomposition Level**} & \multirow{2}{*}{**Frequency**} & \multicolumn{2}{c}{**Simulation**} & \multicolumn{2}{c}{**Experimental**} \\ \cline{3-6}  & & **Shannon Entropy** & **Log-Energy** & **Shannon Entropy** & **Log-Energy Entropy** \\ \hline Approximation (6) & [39.06–0] & Na & A & Na & A \\ Detail (7) & [39.06–19.53] & Na & A & Na & A \\ Approximation (7) & [19.53–0] & Na & Na & Na & Na \\ \hline \hline \end{tabular}  
* A: Applicable; Na: Not applicable.  
\end{table}
Table 6: Conclusion for feature validation in the simulation and experiment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Load (Nm)**} & \multicolumn{3}{c}{**Shannon Entropy**} & \multicolumn{3}{c}{**Log-Energy Entropy**} \\ \cline{2-7}  & **A6** & **A7** & **D7** & **A6** & **A7** & **D7** \\ \hline
0 & 0.448 * & 1.00 * & 0.58 * & 3.5 \(\times\) 10\({}^{-7}\) & 4.2 \(\times\) 10\({}^{-7}\) & 3.0 \(\times\) 10\({}^{-7}\) \\
0.5 & 0.153 * & 0.40 * & 0.84 * & 1.2 \(\times\) 10\({}^{-7}\) & 6.4 \(\times\) 10\({}^{-5}\) & 9.4 \(\times\) 10\({}^{-6}\) \\
1 & 0.911 * & 2.2 \(\times\) 10\({}^{-3}\) & 0.939 * & 6.0 \(\times\) 10\({}^{-8}\) & 1.00 * & 5.5 \(\times\) 10\({}^{-4}\) \\
1.5 & 0.494 * & 8.3 \(\times\) 10\({}^{-4}\) & 1.00 * & 6.0 \(\times\) 1\({}^{-8}\) & 1.3 \(\times\) 10\({}^{-6}\) & 6.0 \(\times\) 10\({}^{-8}\) \\ \hline \hline \end{tabular}
* The \(p\)-value is greater than 0.05 and is not significant.  
\end{table}
Table 5: \(p\)-Value calculation from the post-hoc test procedure for features in different sub-band frequencies.  
bars in LS-PMSM that use drive systems, and (2) the application of intelligent techniques to increase the ability and accuracy of fault detection in the decision-making area.  
The authors would like to express their gratitude to Ministry of High Education Malaysia for financial support through grant number FRGS-5524356 and Universiti Putra Malaysia for the facilities provided during this research work.  
[PERSON] designed and performed the experiments; analyzed the data and prepare the manuscript, [PERSON] is leader of the project and immensely in the preparation of the manuscript, [PERSON] and [PERSON] and [PERSON] revised and finalized the manuscript.  
The authors declare no conflict of interest.