Reconstruction for Rolling Bearing Vibration Signals Integrating 5G-NR-LDPC Codes and Weighted Compressed Sensing
Accurate reconstruction of vibration signals is essential for effective fault diagnosis of rolling bearings. However, existing methods often struggle to achieve a balance between high compression and effective signal reconstruction. To tackle this challenge, we propose a novel algorithm known as the...
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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/10813338/ |
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| author | Chao Wang Hua Xu Guangxing Ni Wenjuan Shi |
| author_facet | Chao Wang Hua Xu Guangxing Ni Wenjuan Shi |
| author_sort | Chao Wang |
| collection | DOAJ |
| description | Accurate reconstruction of vibration signals is essential for effective fault diagnosis of rolling bearings. However, existing methods often struggle to achieve a balance between high compression and effective signal reconstruction. To tackle this challenge, we propose a novel algorithm known as the 5G-WCS algorithm, which integrates 5G New Radio Low-Density Parity-Check Codes (5G-NR-LDPC) with weighted compressed sensing (CS). In this study, a weighted matrix is constructed based on the sparsity coefficients of the signal. This weighted strategy significantly improves compressed sensing’s ability to capture critical information. During the signal observation stage, we use the parity-check matrix of the 5G-NR-LDPC code for efficient sampling and compression, leading to effective signal compression and hardware implementation. Simulation results validate the effectiveness of the proposed 5G-WCS algorithm, demonstrating its capability to achieve desirable quality of signal reconstruction while maintaining high compression of rolling bearing vibration signals. This hardware-friendly scheme presents an efficient solution for industrial signal processing and mechanical fault diagnosis, showcasing significant potential for real-world applications. |
| format | Article |
| id | doaj-art-c2342b5dfd8d46ea83e2e1797ff432dc |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c2342b5dfd8d46ea83e2e1797ff432dc2025-08-20T02:58:18ZengIEEEIEEE Access2169-35362024-01-011219732219733410.1109/ACCESS.2024.352195710813338Reconstruction for Rolling Bearing Vibration Signals Integrating 5G-NR-LDPC Codes and Weighted Compressed SensingChao Wang0https://orcid.org/0009-0008-8972-8490Hua Xu1https://orcid.org/0000-0001-6653-7140Guangxing Ni2https://orcid.org/0009-0000-6798-5439Wenjuan Shi3College of Information Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, ChinaCollege of Physics and Electronic Engineering, Yancheng Teachers University, Yancheng, ChinaSchool of Digital and Design, Shanghai Sibo Vocational and Technical College, Shanghai, ChinaCollege of Physics and Electronic Engineering, Yancheng Teachers University, Yancheng, ChinaAccurate reconstruction of vibration signals is essential for effective fault diagnosis of rolling bearings. However, existing methods often struggle to achieve a balance between high compression and effective signal reconstruction. To tackle this challenge, we propose a novel algorithm known as the 5G-WCS algorithm, which integrates 5G New Radio Low-Density Parity-Check Codes (5G-NR-LDPC) with weighted compressed sensing (CS). In this study, a weighted matrix is constructed based on the sparsity coefficients of the signal. This weighted strategy significantly improves compressed sensing’s ability to capture critical information. During the signal observation stage, we use the parity-check matrix of the 5G-NR-LDPC code for efficient sampling and compression, leading to effective signal compression and hardware implementation. Simulation results validate the effectiveness of the proposed 5G-WCS algorithm, demonstrating its capability to achieve desirable quality of signal reconstruction while maintaining high compression of rolling bearing vibration signals. This hardware-friendly scheme presents an efficient solution for industrial signal processing and mechanical fault diagnosis, showcasing significant potential for real-world applications.https://ieeexplore.ieee.org/document/10813338/Signal reconstructioncompressed sensing5G-NR-LDPC codesrolling bearing |
| spellingShingle | Chao Wang Hua Xu Guangxing Ni Wenjuan Shi Reconstruction for Rolling Bearing Vibration Signals Integrating 5G-NR-LDPC Codes and Weighted Compressed Sensing IEEE Access Signal reconstruction compressed sensing 5G-NR-LDPC codes rolling bearing |
| title | Reconstruction for Rolling Bearing Vibration Signals Integrating 5G-NR-LDPC Codes and Weighted Compressed Sensing |
| title_full | Reconstruction for Rolling Bearing Vibration Signals Integrating 5G-NR-LDPC Codes and Weighted Compressed Sensing |
| title_fullStr | Reconstruction for Rolling Bearing Vibration Signals Integrating 5G-NR-LDPC Codes and Weighted Compressed Sensing |
| title_full_unstemmed | Reconstruction for Rolling Bearing Vibration Signals Integrating 5G-NR-LDPC Codes and Weighted Compressed Sensing |
| title_short | Reconstruction for Rolling Bearing Vibration Signals Integrating 5G-NR-LDPC Codes and Weighted Compressed Sensing |
| title_sort | reconstruction for rolling bearing vibration signals integrating 5g nr ldpc codes and weighted compressed sensing |
| topic | Signal reconstruction compressed sensing 5G-NR-LDPC codes rolling bearing |
| url | https://ieeexplore.ieee.org/document/10813338/ |
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