## 5 Results and Discussion  
After wavelet packet decomposition to the eleven levels of resolution using the selected mother wavelet \"db44\", coefficients-related features were extracted and calculated by the 14 statistical parameters as sorted in Table 3, having the average of 20 samples for each mode of rotor in the case of high load. In Tables 4-6, the features with increasing trend, such as root-mean-square (RMS), root-sum-square (RSSQ), energy, and standard deviation (StD), are tabulated in the case of medium, low, and no load respectively. Figure 7: Schematic diagram of the multi-layer perceptron (MLP) network Classifier. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & _Features_ & _Healthy_ & _1 BRB_ & _2 BRB_ & _3 BRB_ \\ \hline \multirow{8}{*}{_Medium Load (7,5)_} & _RMS_ & **13.049** & **13.386** & **13.647** & **14.064** \\  & _RSSQ_ & **131.14** & **134.52** & **137.13** & **141.34** \\ \cline{1-1} \cline{2-6}  & _Kurtosis_ & 2.4943 & 2.4792 & 2.4466 & 2.5265 \\ \cline{1-1}  & _Skurness_ & 0.0650 & 0.0398 & 0.0218 & 0.1119 \\ \cline{1-1}  & _Mean_ & 0.1199 & 0.1154 & 0.1080 & 0.1445 \\ \cline{1-1}  & _PtoP_ & 54.545 & 56.185 & 57.470 & 58.061 \\ \cline{1-1}  & _P to RMS_ & 2.2327 & 2.2165 & 2.1998 & 2.2450 \\ \cline{1-1}  & _Log Detect_ & 6.4912 & 6.6992 & 7.1041 & 6.7506 \\ \cline{1-1}  & _PAPR_ & 4.9851 & 4.8775 & 4.8401 & 5.0403 \\ \cline{1-1}  & _Shupe Factor_ & 109.78 & 119.47 & 127.91 & 97.700 \\ \cline{1-1}  & _Impulse Factor_ & 245.04 & 264.03 & 281.06 & 219.36 \\ \cline{1-1}  & _Energy_ & **17,241** & **18,099** & **18,807** & **19,979** \\ \cline{1-1}  & _StD_ & **13.114** & **13.452** & **13.713** & **14.134** \\ \cline{1-1}  & _6 th-Moment_ & **3.93 \(\times\) 10\({}^{8}\)** & **4.38 \(\times\) 10\({}^{8}\)** & **4.84 \(\times\) 10\({}^{8}\)** & **6.05 \(\times\) 10\({}^{8}\)** \\ \hline \multicolumn{6}{l}{The bold numbers mean: the values of a feature follow an incresing or decreasing trend regarding the fault severity.} \\ \end{tabular}
\end{table} 
Table 4: Mean Value of Statistical Features for Healthy and Faulty Motor in Medium Load. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & _Features_ & _Healthy_ & _1 BRB_ & _2 BRB_ & _3 BRB_ \\ \hline \multirow{8}{*}{_Low Load (8,10)_} & _RMS_ & **15.61** & **15.99** & **16.158** & **16.226** \\  & _RSSQ_ & **151.38** & **155.05** & **156.66** & **157.32** \\ \cline{1-1}  & _Kurtosis_ & 2.778 & 2.837 & 2.712 & 2.885 \\ \cline{1-1}  & _Skurness_ & \(-0.0462\) & \(-0.0316\) & 0.0364 & \(-0.0495\) \\ \cline{1-1}  & _Mean_ & 0.4242 & 0.4635 & 0.8483 & 0.4565 \\ \cline{1-1}  & _PtoP_ & 76.782 & 77.907 & 74.351 & 79.597 \\ \cline{1-1}  & _P to RMS_ & 2.5607 & 2.5570 & 2.4571 & 2.5794 \\ \cline{1-1}  & _Log Detect_ & 8.3186 & 8.4276 & 8.4875 & 8.3089 \\ \cline{1-1}  & _PAPR_ & 5.5706 & 5.6598 & 6.0381 & 5.4074 \\ \cline{1-1}  & _Shupe Factor_ & 37.076 & 34.528 & 33.375 & 35.564 \\ \cline{1-1}  & _Impulse Factor_ & 95.543 & 88.380 & 82.011 & 91.802 \\ \cline{1-1}  & _Energy_ & **23015** & **24055** & **24546** & **24753** \\ \cline{1-1}  & _StD_ & **15.691** & **16.071** & **16.238** & **16.307** \\ \cline{1-1}  & _6 th-Moment_ & **1.76 \(\times\) 10\({}^{8}\)** & **1.98 \(\times\) 10\({}^{8}\)** & **1.83 \(\times\) 10\({}^{8}\)** & **2.23 \(\times\) 10\({}^{8}\)** \\ \hline \hline \end{tabular} The bold numbers mean: the values of a feature follow an incresing or decreasing trend regarding the fault severity.  
\end{table} 
Table 5: Mean Value of Statistical Features for Healthy and Faulty Motor in Low Load. The distances between healthy and faulty conditions indicate the more efficiency of RMS, RSSQ, energy, and StD features of wavelet packet coefficients. Therefore, these indices are compared for (Level 6--Depth 3) for High Load, (Level 7--Depth 5) for half of the full load, (Level 8--Depth 10) for low load, and (Level 9--Depth 21) under no load condition to define the most appropriate frequency band to represent the frequency components caused by the BRB malfunction in the induction motor.