A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification

Kaya Y., Kuncan M., Kaplan K., Minaz M. R., Ertunç H. M.

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, vol.33, no.1, pp.161-178, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.1080/0952813x.2020.1735530
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC, Psycinfo, zbMATH
  • Page Numbers: pp.161-178
  • Keywords: Feature extraction, 1d-LBP, 1d-GLCM, fault classification, bearing fault diagnosis, LOCAL BINARY PATTERNS, EPILEPTIC EEG, ALGORITHM, IDENTIFICATION, TRANSFORM, DIAGNOSIS, DEFECTS, SCHEME
  • Kocaeli University Affiliated: Yes


Recently, precise and deterministic feature extraction is one of the current research topics for bearing fault diagnosis. For this aim, an experimental bearing test setup was created in this study. In this setup, vibration signals were obtained from the bearings on which artificial faults were generated in specific sizes. A new feature extraction method based on co-occurrence matrices for bearing vibration signals was proposed instead of the conventional feature extraction methods, as in the literature. The One (1) Dimensional-Local Binary Patterns (1D-LBP) method was first applied to bearing vibration signals, and a new signal whose values ranged between 0-255 was obtained. Then, co-occurrence matrices were obtained from these signals. The correlation, energy, homogeneity, and contrast features were extracted from these matrices. Different machine learning methods were employed with these features to carry out the classification process. Three different data sets were used to test the proposed approach. As a result of analysing the signals with the proposed model, the success rate is 87.50% for dataset1 (different speed), 96.5% for dataset2 (fault size (mm)) and 99.30% for dataset3 (fault type - inner ring, outer ring, ball) was found, respectively.