An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA
| dc.authorid | KUNCAN, Melih/0000-0002-9749-0418 | |
| dc.contributor.author | Kuncan, Melih | |
| dc.date.accessioned | 2024-12-24T19:28:32Z | |
| dc.date.available | 2024-12-24T19:28:32Z | |
| dc.date.issued | 2020 | |
| dc.department | Siirt Üniversitesi | |
| dc.description.abstract | 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)). | |
| dc.identifier.doi | 10.1109/ACCESS.2020.3011980 | |
| dc.identifier.endpage | 137529 | |
| dc.identifier.issn | 2169-3536 | |
| dc.identifier.scopus | 2-s2.0-85089586228 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 137517 | |
| dc.identifier.uri | https://doi.org/10.1109/ACCESS.2020.3011980 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12604/7110 | |
| dc.identifier.volume | 8 | |
| dc.identifier.wos | WOS:000557771400001 | |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | IEEE-Inst Electrical Electronics Engineers Inc | |
| dc.relation.ispartof | Ieee Access | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_20241222 | |
| dc.subject | Vibrations | |
| dc.subject | Feature extraction | |
| dc.subject | Fault diagnosis | |
| dc.subject | Monitoring | |
| dc.subject | Gears | |
| dc.subject | Rotating machines | |
| dc.subject | Rolling bearings | |
| dc.subject | Bearing faults | |
| dc.subject | fault detection | |
| dc.subject | fault classification | |
| dc.subject | feature extraction | |
| dc.subject | gray relational analysis | |
| dc.subject | local binary pattern | |
| dc.title | An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA | |
| dc.type | Article |
