Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM
This study presents a novel fault diagnosis approach for rolling bearings that integrates the Improved Pelican Optimization Algorithm (IPOA) for optimizing Variational Mode Decomposition (VMD) and the Sparrow Search Algorithm (SSA) for optimizing the Hybrid Kernel Extreme Learning Machine (HKELM). T...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10792661/ |
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| author | Baoxian Chang Xing Zhao Dawei Guo Siyu Zhao Jiyou Fei Hua Li Xiaodong Liu |
| author_facet | Baoxian Chang Xing Zhao Dawei Guo Siyu Zhao Jiyou Fei Hua Li Xiaodong Liu |
| author_sort | Baoxian Chang |
| collection | DOAJ |
| description | This study presents a novel fault diagnosis approach for rolling bearings that integrates the Improved Pelican Optimization Algorithm (IPOA) for optimizing Variational Mode Decomposition (VMD) and the Sparrow Search Algorithm (SSA) for optimizing the Hybrid Kernel Extreme Learning Machine (HKELM). The method aims to overcome challenges such as weak early fault signals and the complexities in extracting fault characteristics that often result in subpar fault classification outcomes. A novel comprehensive indicator is introduced as the fitness function during the parameter selection phase of IPOA. By utilizing IPOA, the optimal combination of VMD’s parameters, including the mode component K and penalty factor <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, is determined. Signal decomposition via VMD yields a set of Intrinsic Mode Functions (IMF). The Kolmogorov-Smirnov Distance (KSD) is employed as a measure to assess the correlation between each IMF component and the original signal. Subsequently, the KSD values of the IMFs are calculated to identify the optimal IMF components, with their Multi-scale Range Entropy (MRE) computed as a distinguishing feature. Lastly, the HKELM, enhanced through SSA optimization, is employed for the training and classification of rolling bearing faults, with the reliability and efficacy of the proposed methodology validated through simulation and empirical data. |
| format | Article |
| id | doaj-art-3f839b5a0dad4a2da5a2ab6005e2d3a8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3f839b5a0dad4a2da5a2ab6005e2d3a82024-12-20T00:00:43ZengIEEEIEEE Access2169-35362024-01-011219029519030910.1109/ACCESS.2024.351547910792661Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELMBaoxian Chang0https://orcid.org/0009-0005-0430-5201Xing Zhao1https://orcid.org/0000-0002-3654-7134Dawei Guo2Siyu Zhao3Jiyou Fei4https://orcid.org/0000-0003-2547-8783Hua Li5Xiaodong Liu6College of Zhan Tianyou, Dalian Jiaotong University, Dalian, ChinaCollege of Zhan Tianyou, Dalian Jiaotong University, Dalian, ChinaCollege of Zhan Tianyou, Dalian Jiaotong University, Dalian, ChinaCollege of Zhan Tianyou, Dalian Jiaotong University, Dalian, ChinaCollege of Zhan Tianyou, Dalian Jiaotong University, Dalian, ChinaCollege of Zhan Tianyou, Dalian Jiaotong University, Dalian, ChinaCollege of Zhan Tianyou, Dalian Jiaotong University, Dalian, ChinaThis study presents a novel fault diagnosis approach for rolling bearings that integrates the Improved Pelican Optimization Algorithm (IPOA) for optimizing Variational Mode Decomposition (VMD) and the Sparrow Search Algorithm (SSA) for optimizing the Hybrid Kernel Extreme Learning Machine (HKELM). The method aims to overcome challenges such as weak early fault signals and the complexities in extracting fault characteristics that often result in subpar fault classification outcomes. A novel comprehensive indicator is introduced as the fitness function during the parameter selection phase of IPOA. By utilizing IPOA, the optimal combination of VMD’s parameters, including the mode component K and penalty factor <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, is determined. Signal decomposition via VMD yields a set of Intrinsic Mode Functions (IMF). The Kolmogorov-Smirnov Distance (KSD) is employed as a measure to assess the correlation between each IMF component and the original signal. Subsequently, the KSD values of the IMFs are calculated to identify the optimal IMF components, with their Multi-scale Range Entropy (MRE) computed as a distinguishing feature. Lastly, the HKELM, enhanced through SSA optimization, is employed for the training and classification of rolling bearing faults, with the reliability and efficacy of the proposed methodology validated through simulation and empirical data.https://ieeexplore.ieee.org/document/10792661/Pelican optimization algorithmvariational mode decompositionsparrow search algorithmhybrid kernel extreme learning machine |
| spellingShingle | Baoxian Chang Xing Zhao Dawei Guo Siyu Zhao Jiyou Fei Hua Li Xiaodong Liu Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM IEEE Access Pelican optimization algorithm variational mode decomposition sparrow search algorithm hybrid kernel extreme learning machine |
| title | Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM |
| title_full | Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM |
| title_fullStr | Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM |
| title_full_unstemmed | Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM |
| title_short | Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM |
| title_sort | bearing fault diagnosis based on ipoa vmd and ssa hkelm |
| topic | Pelican optimization algorithm variational mode decomposition sparrow search algorithm hybrid kernel extreme learning machine |
| url | https://ieeexplore.ieee.org/document/10792661/ |
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