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 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 87
  • Publication Date: 2020
  • Doi Number: 10.1016/j.asoc.2019.106019
  • Journal Name: APPLIED SOFT COMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
  • Keywords: Feature extraction, Texture analysis, Vibration signals, Local binary pattern, LOCAL BINARY PATTERNS, SEVERITY CLASSIFICATION, FEATURE-SELECTION, EPILEPTIC EEG, BALL-BEARING, RECOGNITION, ALGORITHM
  • Kocaeli University Affiliated: Yes

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.