Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies
This review comprehensively encompasses a range of recent studies on journal bearings, emphasizing wear fault diagnostics, condition monitoring, and fault diagnosis methodologies. A significant finding reveals a shift back to the utilization of journal bearings in various rotating machinery such as...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Unviversity of Technology- Iraq
2025-01-01
|
| Series: | Engineering and Technology Journal |
| Subjects: | |
| Online Access: | https://etj.uotechnology.edu.iq/article_184650_e164d19b99b7d3d46399faa993dcc4ab.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832572782734475264 |
|---|---|
| author | Nazik Jebur Wafa Soud |
| author_facet | Nazik Jebur Wafa Soud |
| author_sort | Nazik Jebur |
| collection | DOAJ |
| description | This review comprehensively encompasses a range of recent studies on journal bearings, emphasizing wear fault diagnostics, condition monitoring, and fault diagnosis methodologies. A significant finding reveals a shift back to the utilization of journal bearings in various rotating machinery such as compressors, motors, turbines, and pumps. Various methodologies employed in these recent studies include vibration analysis, machine learning, deep learning, and both numerical and experimental simulations. Key findings indicate that ensemble models, such as the CNN and deep neural network (CNNEPDNN) model, significantly improve convergence speed, test accuracy, and F-Score in bearing fault diagnosis by 15-20% compared to individual models. Additionally, convolutional autoencoders have demonstrated impressive performance, achieving an average Pearson coefficient of 91% in wear estimation, underscoring the critical importance of predictive maintenance. Despite these remarkable advancements, challenges persist due to the lack of uniform evaluation criteria and the focus on specific error types under particular operating conditions. Collaborative efforts among researchers are essential for developing robust and broadly applicable diagnostic models. Addressing these ongoing issues will further enhance condition monitoring and defect detection, leading to more reliable and academically rigorous diagnostic methods applicable in diverse real-world scenarios. |
| format | Article |
| id | doaj-art-ff90fd993b0f4a8d8c7e206d59298170 |
| institution | Kabale University |
| issn | 1681-6900 2412-0758 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj-art-ff90fd993b0f4a8d8c7e206d592981702025-02-02T07:51:22ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582025-01-01431254110.30684/etj.2024.148997.1737184650Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologiesNazik Jebur0Wafa Soud1Mechanical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.Mechanical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.This review comprehensively encompasses a range of recent studies on journal bearings, emphasizing wear fault diagnostics, condition monitoring, and fault diagnosis methodologies. A significant finding reveals a shift back to the utilization of journal bearings in various rotating machinery such as compressors, motors, turbines, and pumps. Various methodologies employed in these recent studies include vibration analysis, machine learning, deep learning, and both numerical and experimental simulations. Key findings indicate that ensemble models, such as the CNN and deep neural network (CNNEPDNN) model, significantly improve convergence speed, test accuracy, and F-Score in bearing fault diagnosis by 15-20% compared to individual models. Additionally, convolutional autoencoders have demonstrated impressive performance, achieving an average Pearson coefficient of 91% in wear estimation, underscoring the critical importance of predictive maintenance. Despite these remarkable advancements, challenges persist due to the lack of uniform evaluation criteria and the focus on specific error types under particular operating conditions. Collaborative efforts among researchers are essential for developing robust and broadly applicable diagnostic models. Addressing these ongoing issues will further enhance condition monitoring and defect detection, leading to more reliable and academically rigorous diagnostic methods applicable in diverse real-world scenarios.https://etj.uotechnology.edu.iq/article_184650_e164d19b99b7d3d46399faa993dcc4ab.pdfjournal bearingsfault diagnosticsvibration analysisdeep learningbearing wear |
| spellingShingle | Nazik Jebur Wafa Soud Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies Engineering and Technology Journal journal bearings fault diagnostics vibration analysis deep learning bearing wear |
| title | Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies |
| title_full | Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies |
| title_fullStr | Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies |
| title_full_unstemmed | Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies |
| title_short | Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies |
| title_sort | recent advances in journal bearings wear fault diagnostics condition monitoring and fault diagnosis methodologies |
| topic | journal bearings fault diagnostics vibration analysis deep learning bearing wear |
| url | https://etj.uotechnology.edu.iq/article_184650_e164d19b99b7d3d46399faa993dcc4ab.pdf |
| work_keys_str_mv | AT nazikjebur recentadvancesinjournalbearingswearfaultdiagnosticsconditionmonitoringandfaultdiagnosismethodologies AT wafasoud recentadvancesinjournalbearingswearfaultdiagnosticsconditionmonitoringandfaultdiagnosismethodologies |