Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions
In this paper, to process the non-stationary vibration signals, the re-sampling technique at constant angle increment is combined with the continuous wavelet transform. The key idea is to select the optimal range of wavelet scales based on the maximum energy to the Shannon entropy ratio criteria. Energy and Shannon entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. A feed-forward multilayer perceptron (MLP) neural network uses the extracted features for classification.
wavelet shannon entropy gearbox fault detection non-stationary vibration neural network