New technique for feature selection: Combination between elastic net and relief

One of the most advanced forms of industrial maintenance is predictive maintenance. Indeed, the present analysis of the behavior of a material helps to predict future behavior. The analysis is also used to predict the nature of the failure and to minimize the delay in the fault detection. So as the diagnosis of faults in rotating machines is an important subject in order to increase their productivity and reliability, the choice of features to be used for classification and diagnosis constitutes a crucial point. The use of all the possible features will cause an increase in the computational cost and it will even lead to the increase of the classification error because of the existence of redundant and non-significant features. In this context, we are interested in presenting different methods of feature selection and proposing a new approach that tends to select the best features among existing ones and perform the classification-identification using the selected features. The evaluation of the proposed technique is done using real signals from a rotating machine.