An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis


KAPLAN K. , KAYA Y., KUNCAN M., MİNAZ M. R. , ERTUNÇ H. M.

APPLIED SOFT COMPUTING, vol.87, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 87
  • Publication Date: 2020
  • Doi Number: 10.1016/j.asoc.2019.106019
  • Title of Journal : APPLIED SOFT COMPUTING

Abstract

Bearings are one of the most widespread components used for energy transformation in machines. Mechanical wear and faulty bearings reduce the efficiency of rotating machines and thus increase energy consumption. The feature extraction process is an essential part of fault diagnosis in bearings. In order to diagnose the fault caused by the bearing correctly, it is necessary to determine an effective feature extraction method that best describes the fault.