Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of signal's time-varying statistical parameters and features obtained through

In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of signal's time-varying statistical parameters and features

Using these variants, we explore different forms to improve the lower bounds and to update the initial set of binary variables. The various performances of the described

The proposed method is composed of a feature selection procedure and a two-step classification strategy, both based on a specific mass function construction method inspired by

Also the obtained accuracies of different classifiers on the selected fea- tures obtained by proposed method are better that the obtained accuracies of the same

Also, as there have been developed methods for automating this process, the feature set size increases dramatically (leading to what is commonly refer to as

The aim of the first step is the extraction of the relevant parameters; the proposed technique consists of preprocessing the bearing fault vibration signal using

The article is structured in the following manner: in the section 2, we present briefly some basic theoretical aspects concerning the discriminant analysis; in the section 3 we