## 6 Conclusions  
In this study, an advanced wavelet-based signal processing method has been applied to extract the required information out of that monitored signal. In order to be one hundred percent sure that the selected sub-band after WPD is a fault-oriented sub-band, the combination of WPT and FFT, which is named WPSA, is used to complement the BRB fault diagnosis reliability. The WPSA extracts the superimposed spectral harmonics of the wavelet packet reconstructed signal using FFT. As a result of WPSA, the overall effectiveness of defect-dependent feature extraction is enhanced. The defect frequency band in wavelet packet decomposition is directly related to the sampling frequency and follows the frequency order which is not the same as node order. Daubechies 44 showed the most similarity across faulty rotor bar for no load, the low, medium, and high load of the rated torque.  
The proposed method is able to introduce a sub-band that involves the fault characteristics. By implementing wavelet statistical parameters, it is shown that the RMS, RSSQ, StD, and energy value of wavelet packet coefficients are the appropriate features for BRB detection, even in no load condition. The WP-based statistical parameters are found to be fast, accurate, and easy to implement. The present approach based on statistical feature of WPSA gets stronger with a computationally more efficient and intelligent decision-making technique. To generalize the design network especially for small datasets, the Leave-One-Out (LOO) cross validation (CV) technique is applied to multiple MLP-based NN for available train data. 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, the most superior features are selected based on simple sequential feed-forward selection. RMSE on testing samples are very small, which means that the NN classifies and estimate correctly. The lower value of RMSE indicates the closer to NN convergence value. It has been found that network is able to detect the faults in the rotor with average classification accuracies of 98.80% on testing data to identify rotor bar breakage severity in four load range (80%, 50%, 35% and 10% of full load).  
In most NN-based fault classification schemes, a hybrid complex network is used, whereas it is seen that a simple, small-sized multilayer perceptron network works as magnificent fault classifier for intelligent condition-based monitoring of three-phase induction. As compared to existing structures, the suggested scheme is simple, cost-effective, reliable, and accurate.  
Figure 16: The Error between Real Output and Desired for Testing Data Set in 10% of Full Load.  
**Acknowledgments:** 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.  
**Author Contributions:** [PERSON] analyzed the data and prepare the manuscript. [PERSON] revised and finalized the manuscript. [PERSON] designed and performed the experiments, and revised and finalized the manuscript. [PERSON] and [PERSON] revised and finalized the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.