Article detail · 2020
An Intelligent Approach for Bearing FaultDiagnosis: Combination of 1D-LBP and GRA
- Year
- 2020
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue IEEE Access
- Catalog match (ISSN) IEEE Access
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
Bearings are vital automation machine elements that are used quite frequently for power transmission and shaft bearing in rotating machines. The healthy operation of the bearings directly affects the performance of the rotating machines. Bearing faults may cause more vibration than normal in rotating machines, which wastes power. However, further bearing failures can cause vital damage to rotating machines. In this study, bearing vibration values are obtained through a special test setup. Different types and different sizes of artificial faults have been created in the bearings for the testing process. Data on these bearings are collected at different speeds. The purpose of the study is to diagnose faults in the bearings. In this context, a new approach is proposed. First, the one-dimensional local binary pattern (1D-LBP) method is applied to vibration signals, and all signal data are carried to the 1D-LBP plane. Statistical features are obtained from the signals in the 1D-LBP plane by using these features, and then the vibrational signals are classified by the gray relational analysis (GRA) model. Four different data sets are organized to test the proposed approach. The results of the test process with this proposed model have an accuracy of 99.044% for Dataset1 (different speed -300 rpm intervals), 94.224% for Dataset2 (different speed -60 rpm intervals), and 99.584% for Dataset3 (fault size (mm)); a 100% average success rate is observed for Dataset4 (fault type - error free bearing (EFB), inner ring fault (IRF), outer ring fault (ORF), and ball fault (BF)).
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
108 citations
OpenAlex cited_by_count (cache / database)
10 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- A new automatic bearing fault size diagnosis using time-frequency images of CWT and deep transfer learning methods 2022
- A new content-free approach to identification of document language: Angle patterns 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
- Sensor-based bearing fault diagnosis using a hybrid CNN–LSTM framework with explainable AI 2026
- A New Bearing Fault Diagnosis Approach based on Common Spatial Pattern Features 2023