Dynamic Genetic Algorithm-based Feature Selection Scheme for Machine Health Prognostics

This paper proposed a dominant feature selection scheme to enable the high performance prognostics of machine health. Statistical features were extracted from decomposed sub-modes by wavelet transform. Fisher ratio was employed to evaluate the extracted feature vectors, and dynamic searching strategy-based genetic algorithm was used to select the optimal feature subsets on the basis of maximizing the fitness function. Then dominant features with minimum mean square errors were used to predict the performance of machine health. Experimental results on predicting the lifetime of an unbalance vibration rotor system demonstrated that the proposed method can achieve better prognosis performance with less predicting errors.