Diagnosis of Bearing Defects based on the Analysis of Vibration Images Using the RKEM SIFT Descriptor Method
Diagnosing bearing defects is one of the basic tasks in machine health monitoring, because bearings are critical components of rotating machines. This paper proposes a new method for detecting defects in bearings based on a combination of feature extraction algorithms in which a two-dimensional sign...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
Islamic Azad University Bushehr Branch
2024-02-01
|
| Series: | مهندسی مخابرات جنوب |
| Subjects: | |
| Online Access: | https://sanad.iau.ir/journal/jce/Article/869969 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841546093707395072 |
|---|---|
| author | Zohreh Hashempour Hamed Agahi Azar Mahmoodzadeh |
| author_facet | Zohreh Hashempour Hamed Agahi Azar Mahmoodzadeh |
| author_sort | Zohreh Hashempour |
| collection | DOAJ |
| description | Diagnosing bearing defects is one of the basic tasks in machine health monitoring, because bearings are critical components of rotating machines. This paper proposes a new method for detecting defects in bearings based on a combination of feature extraction algorithms in which a two-dimensional signal is used. Different from other classical one-dimensional signal processing methods, the proposed method of this paper converts one-dimensional vibration signals into two-dimensional signal (image), then image processing methods are used to analyze the image signal in order to classify the defects that have occurred. arrive at the bearing. Converted images from vibration signals often have specific texture characteristics, and the texture of each defective category is different. In addition, each descriptor extracts spatial features. Some features are weak and others are strong. In this article, the method of removing additional key points of SIFT (RKEM SIFT) is used. In addition, for each descriptor, the best features are selected using the non-linear principal component analysis method. Finally, the selected features are combined and four classification methods are applied to achieve the best classification performance and after comparison, the best classification method is selected. The performance of the proposed algorithm is evaluated on the standard bearing data set of Case Western Reserve University. The simulation results show that the proposed method performs better than other methods of fault finding of rolling bearings. |
| format | Article |
| id | doaj-art-dd5298dab3fe4b22ba6fb06af9621a4b |
| institution | Kabale University |
| issn | 2980-9231 |
| language | fas |
| publishDate | 2024-02-01 |
| publisher | Islamic Azad University Bushehr Branch |
| record_format | Article |
| series | مهندسی مخابرات جنوب |
| spelling | doaj-art-dd5298dab3fe4b22ba6fb06af9621a4b2025-01-11T05:11:13ZfasIslamic Azad University Bushehr Branchمهندسی مخابرات جنوب2980-92312024-02-0113506784Diagnosis of Bearing Defects based on the Analysis of Vibration Images Using the RKEM SIFT Descriptor MethodZohreh Hashempour0Hamed Agahi1Azar Mahmoodzadeh2Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran, Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, IranDepartment of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, IranDiagnosing bearing defects is one of the basic tasks in machine health monitoring, because bearings are critical components of rotating machines. This paper proposes a new method for detecting defects in bearings based on a combination of feature extraction algorithms in which a two-dimensional signal is used. Different from other classical one-dimensional signal processing methods, the proposed method of this paper converts one-dimensional vibration signals into two-dimensional signal (image), then image processing methods are used to analyze the image signal in order to classify the defects that have occurred. arrive at the bearing. Converted images from vibration signals often have specific texture characteristics, and the texture of each defective category is different. In addition, each descriptor extracts spatial features. Some features are weak and others are strong. In this article, the method of removing additional key points of SIFT (RKEM SIFT) is used. In addition, for each descriptor, the best features are selected using the non-linear principal component analysis method. Finally, the selected features are combined and four classification methods are applied to achieve the best classification performance and after comparison, the best classification method is selected. The performance of the proposed algorithm is evaluated on the standard bearing data set of Case Western Reserve University. The simulation results show that the proposed method performs better than other methods of fault finding of rolling bearings.https://sanad.iau.ir/journal/jce/Article/869969diagnosing bearing defectsrkem sift methodkernel principal component analysissignal-to-image conversion |
| spellingShingle | Zohreh Hashempour Hamed Agahi Azar Mahmoodzadeh Diagnosis of Bearing Defects based on the Analysis of Vibration Images Using the RKEM SIFT Descriptor Method مهندسی مخابرات جنوب diagnosing bearing defects rkem sift method kernel principal component analysis signal-to-image conversion |
| title | Diagnosis of Bearing Defects based on the Analysis of Vibration Images Using the RKEM SIFT Descriptor Method |
| title_full | Diagnosis of Bearing Defects based on the Analysis of Vibration Images Using the RKEM SIFT Descriptor Method |
| title_fullStr | Diagnosis of Bearing Defects based on the Analysis of Vibration Images Using the RKEM SIFT Descriptor Method |
| title_full_unstemmed | Diagnosis of Bearing Defects based on the Analysis of Vibration Images Using the RKEM SIFT Descriptor Method |
| title_short | Diagnosis of Bearing Defects based on the Analysis of Vibration Images Using the RKEM SIFT Descriptor Method |
| title_sort | diagnosis of bearing defects based on the analysis of vibration images using the rkem sift descriptor method |
| topic | diagnosing bearing defects rkem sift method kernel principal component analysis signal-to-image conversion |
| url | https://sanad.iau.ir/journal/jce/Article/869969 |
| work_keys_str_mv | AT zohrehhashempour diagnosisofbearingdefectsbasedontheanalysisofvibrationimagesusingtherkemsiftdescriptormethod AT hamedagahi diagnosisofbearingdefectsbasedontheanalysisofvibrationimagesusingtherkemsiftdescriptormethod AT azarmahmoodzadeh diagnosisofbearingdefectsbasedontheanalysisofvibrationimagesusingtherkemsiftdescriptormethod |