Article detail · 2021
A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification
Journal
Journal of Experimental & Theoretical Artificial IntelligenceISSN 1362-3079
The ISSN points to another catalog journal; the name is from the YÖKSİS record.
- Year
- 2021
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue Journal of Experimental & Theoretical Artificial Intelligence
- Catalog match (ISSN) Journal of Experimental and Theoretical Artificial Intelligence
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
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.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
97 citations
OpenAlex cited_by_count (cache / database)
15 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Brain tumor classification using modified local binary patterns (LBP) feature extraction methods 2020
- Brain tumor classification using modified local binary patterns (LBP) feature extraction methods 2020
- An Intelligent Approach for Bearing FaultDiagnosis: Combination of 1D-LBP and GRA 2020
- A new automatic bearing fault size diagnosis using time-frequency images of CWT and deep transfer learning methods 2022
- An efficient approach based on a novel 1D-LBP for the detection of bearing failures with a hybrid deep learning method 2024
- An efficient approach based on a novel 1D-LBP for the detection of bearing failures with a hybrid deep learning method 2024
- An efficient approach based on a novel 1D-LBP for the detection of bearing failures with a hybrid deep learning method 2024
- An Effective Method for Detection of Demagnetization Fault in Axial Flux Coreless PMSG With Texture-Based Analysis 2021
- An Effective Method for Detection of Demagnetization Fault in Axial Flux Coreless PMSG with Texture-Based Analysis 2021
- Enhancing robotic manipulator fault detection with advanced machine learning techniques 2024