Automatic Fault Classification of Rolling Element Bearing using Wavelet Packet Decomposition and Artificial Neural Network

In this work an automatic fault classification system is developed for bearing fault classification of three phase induction motor. The system uses the wavelet packet decomposition using ‘db8’ mother wavelet function for feature extraction from the vibration signal, recorded for various bearing fault conditions. The selection of best node of wavelet packet tree is performed by using best tree algorithm along with minimum Shannon entropy criteria. The ten statistical features such as peak value, root mean square value (RMS), kurtosis, skewness etc. are extracted from the wavelet packet coefficient of optimal node. The extracted feature then was used to train and test neural network fault classification. The artificial neural network system was designed to classify the rolling element bearing condition: healthy bearing (HB) rolling element fault (REF), inner race fault (IRF) and Outer race fault (ORF) for fault classification. The over all fault classification rate is 98.33% of the artificial neural network fault classifier.