Sparse Reconstruction Based on the ADMM and Lasso-LSQR for Bearings Vibration Signals
In this paper, we introduce a novel method for reconstructing roller bearings vibration signals. As well as the sparse reconstruction algorithm, our approach is based on the Lasso via the alternate direction multiplier method (ADMM) and optimized by least square QR-factorization (LSQR), which takes...
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2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8052110/ |
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doaj-43e380bac3d84c7686c7a7b32256ad1c2021-03-29T20:12:56ZengIEEEIEEE Access2169-35362017-01-015200832008810.1109/ACCESS.2017.27570268052110Sparse Reconstruction Based on the ADMM and Lasso-LSQR for Bearings Vibration SignalsWanqing Song0https://orcid.org/0000-0002-0561-3258Maria N. Nazarova1Yujin Zhang2Ting Zhang3Ming Li4https://orcid.org/0000-0002-2725-353XSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, ChinaTransportation and Storage of Oil and Gas Department, Saint Petersburg Mining University, Saint Petersburg, RussiaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Machine Engineering, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Information Science and Technology, East China Normal University, Shanghai, ChinaIn this paper, we introduce a novel method for reconstructing roller bearings vibration signals. As well as the sparse reconstruction algorithm, our approach is based on the Lasso via the alternate direction multiplier method (ADMM) and optimized by least square QR-factorization (LSQR), which takes the priority over the Basis Pursuit and Lasso in iterations and errors. First, we use the discrete cosine transformation to achieve sparse signals, then we compress signals by using the Gaussian random matrix, and, finally, we reconstruct the original signals with the Lasso-LSQR by using the ADMM. According to the results, vibration signals can keep sufficient reconstruction accuracy with high compressive ratio, which validates the effectiveness of the method for vibration signals.https://ieeexplore.ieee.org/document/8052110/Sparse reconstructionADMMbearing vibrationLasso-LSQR |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Wanqing Song Maria N. Nazarova Yujin Zhang Ting Zhang Ming Li |
| spellingShingle |
Wanqing Song Maria N. Nazarova Yujin Zhang Ting Zhang Ming Li Sparse Reconstruction Based on the ADMM and Lasso-LSQR for Bearings Vibration Signals IEEE Access Sparse reconstruction ADMM bearing vibration Lasso-LSQR |
| author_facet |
Wanqing Song Maria N. Nazarova Yujin Zhang Ting Zhang Ming Li |
| author_sort |
Wanqing Song |
| title |
Sparse Reconstruction Based on the ADMM and Lasso-LSQR for Bearings Vibration Signals |
| title_short |
Sparse Reconstruction Based on the ADMM and Lasso-LSQR for Bearings Vibration Signals |
| title_full |
Sparse Reconstruction Based on the ADMM and Lasso-LSQR for Bearings Vibration Signals |
| title_fullStr |
Sparse Reconstruction Based on the ADMM and Lasso-LSQR for Bearings Vibration Signals |
| title_full_unstemmed |
Sparse Reconstruction Based on the ADMM and Lasso-LSQR for Bearings Vibration Signals |
| title_sort |
sparse reconstruction based on the admm and lasso-lsqr for bearings vibration signals |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2017-01-01 |
| description |
In this paper, we introduce a novel method for reconstructing roller bearings vibration signals. As well as the sparse reconstruction algorithm, our approach is based on the Lasso via the alternate direction multiplier method (ADMM) and optimized by least square QR-factorization (LSQR), which takes the priority over the Basis Pursuit and Lasso in iterations and errors. First, we use the discrete cosine transformation to achieve sparse signals, then we compress signals by using the Gaussian random matrix, and, finally, we reconstruct the original signals with the Lasso-LSQR by using the ADMM. According to the results, vibration signals can keep sufficient reconstruction accuracy with high compressive ratio, which validates the effectiveness of the method for vibration signals. |
| topic |
Sparse reconstruction ADMM bearing vibration Lasso-LSQR |
| url |
https://ieeexplore.ieee.org/document/8052110/ |
| work_keys_str_mv |
AT wanqingsong sparsereconstructionbasedontheadmmandlassolsqrforbearingsvibrationsignals AT mariannazarova sparsereconstructionbasedontheadmmandlassolsqrforbearingsvibrationsignals AT yujinzhang sparsereconstructionbasedontheadmmandlassolsqrforbearingsvibrationsignals AT tingzhang sparsereconstructionbasedontheadmmandlassolsqrforbearingsvibrationsignals AT mingli sparsereconstructionbasedontheadmmandlassolsqrforbearingsvibrationsignals |
| _version_ |
1724195080841986048 |