Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE
The monitoring and fault diagnosis of axle-box bearings in high-speed trains is crucial for ensuring safe train operations. The vibration signals of these bearings exhibit non-stationary and non-linear characteristics. To further enhance the accuracy of identifying rolling bearing faults, a fault di...
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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/10494842/ |
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| author | Baoxian Chang Xing Zhao Dawei Guo Siyu Zhao Jiyou Fei |
| author_facet | Baoxian Chang Xing Zhao Dawei Guo Siyu Zhao Jiyou Fei |
| author_sort | Baoxian Chang |
| collection | DOAJ |
| description | The monitoring and fault diagnosis of axle-box bearings in high-speed trains is crucial for ensuring safe train operations. The vibration signals of these bearings exhibit non-stationary and non-linear characteristics. To further enhance the accuracy of identifying rolling bearing faults, a fault diagnosis method is proposed. This method is based on the improved Dung Beetle Optimization (DBO) algorithm for optimizing Variational Mode Decomposition (VMD) combined with Stacked Sparse Autoencoder (SSAE). Firstly, the DBO algorithm is enhanced to improve its optimization precision and global optimization capability. It is then utilized for the adaptive selection of two parameters: the number of decomposition modes and the penalty factor in VMD. These improvements address issues such as mode mixing, signal loss, and excessive decomposition, which arise from poor parameter selection in the traditional VMD method. Subsequently, components of Intrinsic Mode Functions (IMFs) that are highly correlated with the original signal are selected. The time-domain and frequency-domain features of these IMF components are used to construct the dataset. The feature set is then inputted into the deep machine learning model SSAE for training and testing. Through diagnostic experiments on various types and levels of rolling bearing faults, the model demonstrates a higher rate of fault diagnosis recognition. |
| format | Article |
| id | doaj-art-32acbd6ea9174f9b9521e2a0a6dffde3 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-32acbd6ea9174f9b9521e2a0a6dffde32025-08-20T01:54:57ZengIEEEIEEE Access2169-35362024-01-011213074613076210.1109/ACCESS.2024.338683510494842Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAEBaoxian Chang0https://orcid.org/0009-0005-0430-5201Xing Zhao1https://orcid.org/0000-0002-3654-7134Dawei Guo2Siyu Zhao3Jiyou Fei4https://orcid.org/0000-0003-2547-8783College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian, ChinaCollege of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian, ChinaCollege of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian, ChinaCollege of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian, ChinaCollege of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian, ChinaThe monitoring and fault diagnosis of axle-box bearings in high-speed trains is crucial for ensuring safe train operations. The vibration signals of these bearings exhibit non-stationary and non-linear characteristics. To further enhance the accuracy of identifying rolling bearing faults, a fault diagnosis method is proposed. This method is based on the improved Dung Beetle Optimization (DBO) algorithm for optimizing Variational Mode Decomposition (VMD) combined with Stacked Sparse Autoencoder (SSAE). Firstly, the DBO algorithm is enhanced to improve its optimization precision and global optimization capability. It is then utilized for the adaptive selection of two parameters: the number of decomposition modes and the penalty factor in VMD. These improvements address issues such as mode mixing, signal loss, and excessive decomposition, which arise from poor parameter selection in the traditional VMD method. Subsequently, components of Intrinsic Mode Functions (IMFs) that are highly correlated with the original signal are selected. The time-domain and frequency-domain features of these IMF components are used to construct the dataset. The feature set is then inputted into the deep machine learning model SSAE for training and testing. Through diagnostic experiments on various types and levels of rolling bearing faults, the model demonstrates a higher rate of fault diagnosis recognition.https://ieeexplore.ieee.org/document/10494842/Bearing fault diagnosisdung beetle optimization algorithmvariational modal decompositionstacked sparse autoencoders |
| spellingShingle | Baoxian Chang Xing Zhao Dawei Guo Siyu Zhao Jiyou Fei Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE IEEE Access Bearing fault diagnosis dung beetle optimization algorithm variational modal decomposition stacked sparse autoencoders |
| title | Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE |
| title_full | Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE |
| title_fullStr | Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE |
| title_full_unstemmed | Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE |
| title_short | Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE |
| title_sort | rolling bearing fault diagnosis based on optimized vmd and ssae |
| topic | Bearing fault diagnosis dung beetle optimization algorithm variational modal decomposition stacked sparse autoencoders |
| url | https://ieeexplore.ieee.org/document/10494842/ |
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