## 5 Results and Discussion
Root-mean-square of wavelet packet coefficients is depicted in Figure 8.  
Stability and convergence of the network are directly dependent on the distinguished feature vector with enough samples. In order to have adequate data without redundancy and remove extraneous information and reduce the burden of the classification system, some combination of features is tested to select the most superior features. To arrange the input vector for training, a simple sequential floating forward selection is used. As a result, three features are selected among all 15 available features and fed to the network to train the network and observations are recorded. In next step, the selection is repeated for the remained 12 features, and other two selected features add to the first three ones. The new five features are also used to train and test the network. Gradually, based on this trend, the number of inputs is increased to 7, 9, 11, 13, and finally all 15 features are fed to the network, and the network performance is observed carefully in terms of cross-validation (CV) classification accuracy and testing Mean Squared Error (MSE). As it can be observed from Figure 9, the combination of five features in high load condition designs a network with 98.39% \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Features** & **Healthy** & **1 BRB** & **2 BRB** & **3 BRB** \\ \hline \multirow{8}{*}{_No Load (9,21)_} & _RMS_ & _20.571_ & _21.243_ & _21.326_ & _21.686_ \\  & _RSSQ_ & _195.16_ & _201.53_ & _202.31_ & _205.73_ \\  & _Kurtosis_ & 3.3268 & 3.2732 & 3.1821 & 3.0076 \\  & _Skevness_ & \(-0.0579\) & \(-0.0624\) & \(-0.0654\) & \(-0.0694\) \\  & _Mann_ & \(-0.8348\) & \(-0.8774\) & \(-0.7704\) & \(-1.0566\) \\  & _PloP_ & 111.70 & 114.22 & 112.69 & 112.25 \\  & _P to RMS_ & 2.8716 & 2.8379 & 2.7286 & 2.6786 \\  & _Log Detect_ & 10.060 & 10.593 & 10.400 & 11.484 \\  & _PAPR_ & 8.2465 & 8.0539 & 7.3546 & 7.1889 \\  & _Shap Factor_ & \(-2.94190\) & \(-2.8452\) & \(-29.177\) & \(-20.766\) \\  & _Impulse Factor_ & \(-71.518\) & \(-70.555\) & \(-80.108\) & \(-55.783\) \\  & _Energy_ & **38129** & **40617** & **40938** & **42330** \\  & _StD_ & **20.669** & **21.343** & **21.430** & **21.781** \\  & _6 th-Moment_ & **1.28 \(\times\) 10\({}^{9}\)** & **1.47 \(\times\) 10\({}^{9}\)** & **1.39 \(\times\) 10\({}^{9}\)** & **1.4 \(\times\) 10\({}^{9}\)** \\ \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 6: Mean Value of Statistical Features for Healthy and Faulty Motor in No Load. Figure 8: Root-mean-square (RMS) of wavelet packet coefficients (WPC). average classification accuracy during training and 0.005 as average of MSE for testing data. In case of medium load when nine features are selected for inputs, testing MSE is minimum (0.003) and CV classification accuracy is maximum (98.37%), according to Figure 10. Figure 11 demonstrates low load condition when five features are used as input vectors to get 100% classification accuracy. The MSE for testing dataset is 0.003. As can be seen from Figure 12, in case of no load when seven features are conjugated as input vector, testing MSE is 0.004 and CV classification accuracy is 96.61%. Figure 11: Variation of average classification and MSE with a number of features as inputs in Low Load. Figure 9: Variation of average classification and mean squared error (MSE) with a number of features as inputs in High Load. As observed from the training result, some measured properties of the classifier performance on CV data, tabulated in Table 7, indicate the network classification capability with correct ratio around one (CorrectRate = Correctly Classified Samples/Classified Samples).