Optimizing condition monitoring of ball bearings: An integrated approach using decision tree and extreme learning machine for effective decision-making
This article presents a study on condition monitoring and predictive maintenance, highlighting the importance of tracking ball bearing condition to estimate their Remaining Useful Life (RUL). The study proposes a methodology that combines three algorithms, namely Variational Mode Decomposition (VMD)...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2023-06-01
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| Series: | Open Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/phys-2022-0239 |
| _version_ | 1797800879340912640 |
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| author | Euldji Riadh Bouamhdi Mouloud Rebhi Redha Bachene Mourad Ikumapayi Omolayo M. Al-Dujaili Ayad Q. Abdulkareem Ahmed I. Humaidi Amjad J. Menni Younes |
| author_facet | Euldji Riadh Bouamhdi Mouloud Rebhi Redha Bachene Mourad Ikumapayi Omolayo M. Al-Dujaili Ayad Q. Abdulkareem Ahmed I. Humaidi Amjad J. Menni Younes |
| author_sort | Euldji Riadh |
| collection | DOAJ |
| description | This article presents a study on condition monitoring and predictive maintenance, highlighting the importance of tracking ball bearing condition to estimate their Remaining Useful Life (RUL). The study proposes a methodology that combines three algorithms, namely Variational Mode Decomposition (VMD), Decision Tree (DT), and Extreme Learning Machine (ELM), to extract pertinent features and estimate RUL using vibration signals. To improve the accuracy of the method, the VMD algorithm is used to reduce noise from the original vibration signals. The DT algorithm is then employed to extract relevant features, which are fed into the ELM algorithm to estimate the RUL of the ball bearings. The effectiveness of the proposed approach is evaluated using ball bearing data sets from the PRONOSTIA platform. Overall, the results demonstrate that the suggested methodology successfully tracks the ball bearing condition and estimates RUL using vibration signals. This study provides valuable insights into the development of predictive maintenance systems that can assist decision-makers in planning maintenance activities. Further research could explore the potential of this methodology in other industrial applications and under different operating conditions. |
| first_indexed | 2024-03-13T04:41:56Z |
| format | Article |
| id | doaj.art-f6045275097545379674d542bf19863a |
| institution | Directory Open Access Journal |
| issn | 2391-5471 |
| language | English |
| last_indexed | 2024-03-13T04:41:56Z |
| publishDate | 2023-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Physics |
| spelling | doaj.art-f6045275097545379674d542bf19863a2023-06-19T05:53:50ZengDe GruyterOpen Physics2391-54712023-06-01211148351010.1515/phys-2022-0239Optimizing condition monitoring of ball bearings: An integrated approach using decision tree and extreme learning machine for effective decision-makingEuldji Riadh0Bouamhdi Mouloud1Rebhi Redha2Bachene Mourad3Ikumapayi Omolayo M.4Al-Dujaili Ayad Q.5Abdulkareem Ahmed I.6Humaidi Amjad J.7Menni Younes8Laboratory of Automation and Industrial Diagnostic, University of Djelfa, Djelfa, AlgeriaLaboratory of Mechanics, Physics, Mathematical modeling (LMP2M), University of Medea, Medea, AlgeriaDepartment of Mechanical Engineering, Faculty of Technology, LERM, University of Medea, Medea, AlgeriaLaboratory of Mechanics, Physics, Mathematical modeling (LMP2M), University of Medea, Medea, AlgeriaDepartment of Mechanical and Mechatronics Engineering, Afe Babalola University, Ado Ekiti360101, NigeriaElectrical Engineering Technical College, Middle Technical University, Baghdad10001, IraqControl and Systems Engineering Department, University of Technology, Baghdad10066, IraqControl and Systems Engineering Department, University of Technology, Baghdad10066, IraqDepartment of Technology, University Center Salhi Ahmed Naama (Ctr. Univ. Naama), P.O. Box 66, Naama45000, AlgeriaThis article presents a study on condition monitoring and predictive maintenance, highlighting the importance of tracking ball bearing condition to estimate their Remaining Useful Life (RUL). The study proposes a methodology that combines three algorithms, namely Variational Mode Decomposition (VMD), Decision Tree (DT), and Extreme Learning Machine (ELM), to extract pertinent features and estimate RUL using vibration signals. To improve the accuracy of the method, the VMD algorithm is used to reduce noise from the original vibration signals. The DT algorithm is then employed to extract relevant features, which are fed into the ELM algorithm to estimate the RUL of the ball bearings. The effectiveness of the proposed approach is evaluated using ball bearing data sets from the PRONOSTIA platform. Overall, the results demonstrate that the suggested methodology successfully tracks the ball bearing condition and estimates RUL using vibration signals. This study provides valuable insights into the development of predictive maintenance systems that can assist decision-makers in planning maintenance activities. Further research could explore the potential of this methodology in other industrial applications and under different operating conditions.https://doi.org/10.1515/phys-2022-0239condition monitoringball bearingsvariational mode decompositiondecision treeextreme learning machines |
| spellingShingle | Euldji Riadh Bouamhdi Mouloud Rebhi Redha Bachene Mourad Ikumapayi Omolayo M. Al-Dujaili Ayad Q. Abdulkareem Ahmed I. Humaidi Amjad J. Menni Younes Optimizing condition monitoring of ball bearings: An integrated approach using decision tree and extreme learning machine for effective decision-making Open Physics condition monitoring ball bearings variational mode decomposition decision tree extreme learning machines |
| title | Optimizing condition monitoring of ball bearings: An integrated approach using decision tree and extreme learning machine for effective decision-making |
| title_full | Optimizing condition monitoring of ball bearings: An integrated approach using decision tree and extreme learning machine for effective decision-making |
| title_fullStr | Optimizing condition monitoring of ball bearings: An integrated approach using decision tree and extreme learning machine for effective decision-making |
| title_full_unstemmed | Optimizing condition monitoring of ball bearings: An integrated approach using decision tree and extreme learning machine for effective decision-making |
| title_short | Optimizing condition monitoring of ball bearings: An integrated approach using decision tree and extreme learning machine for effective decision-making |
| title_sort | optimizing condition monitoring of ball bearings an integrated approach using decision tree and extreme learning machine for effective decision making |
| topic | condition monitoring ball bearings variational mode decomposition decision tree extreme learning machines |
| url | https://doi.org/10.1515/phys-2022-0239 |
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