**4.3 Implementation of WPS –ANN for bearing fault classification**

The derived WT-ANN fault classification technique was validated through real and simulated rolling element bearing vibration signals. MATLAB software has been used for the wavelet feature extraction and ANN classification based on the code flowchart shown in Figure 29.

Fig. 29. WT-ANN automatic bearing fault diagnosis MATLAB codes flow chart.

Wavelet Analysis and Neural Networks for Bearing Fault Diagnosis 347

Fig. 30. (a) the extracted features distribution, (b) ANN learning process, (c) ANN

epochs, with overall classification MSE less than 6.0E-9, and 100% classification rate.

**(b) Simulated vibration data** 

classification MSE, (d) ANN Training/Test process, for the CWRU bearing vibration data.

Using the same bearing specifications but CWRU data with 0.6 dB signal to noise ratio and random slip of 10 percent the period T. Figure 31 shows the Wavelet-ANN bearing fault training/classification process for the simulated bearing vibration signal. The results show that the Wavelet-ANN training process reached the specified stopping criteria after 67
