Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage
The potential application of a quantum-inspired adaptive wavelet shrinkage (QAWS) technique to mechanical vibration signals with a focus on noise reduction is studied in this paper. This quantum-inspired shrinkage algorithm combines three elements: an adaptive non-Gaussian statistical model of dual-...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2014/848097 |
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| _version_ | 1832562517242544128 |
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| author | Yan-long Chen Pei-lin Zhang Bing Li Ding-hai Wu |
| author_facet | Yan-long Chen Pei-lin Zhang Bing Li Ding-hai Wu |
| author_sort | Yan-long Chen |
| collection | DOAJ |
| description | The potential application of a quantum-inspired adaptive wavelet shrinkage (QAWS) technique to mechanical vibration signals with a focus on noise reduction is studied in this paper. This quantum-inspired shrinkage algorithm combines three elements: an adaptive non-Gaussian statistical model of dual-tree complex wavelet transform (DTCWT) coefficients proposed to improve practicability of prior information, the quantum superposition introduced to describe the interscale dependencies of DTCWT coefficients, and the quantum-inspired probability of noise defined to shrink wavelet coefficients in a Bayesian framework. By combining all these elements, this signal processing scheme incorporating the DTCWT with quantum theory can both reduce noise and preserve signal details. A practical vibration signal measured from a power-shift steering transmission is utilized to evaluate the denoising ability of QAWS. Application results demonstrate the effectiveness of the proposed method. Moreover, it achieves better performance than hard and soft thresholding. |
| format | Article |
| id | doaj-art-253afd9de37c4706bb2fee2e504d43c2 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-253afd9de37c4706bb2fee2e504d43c22025-02-03T01:22:20ZengWileyShock and Vibration1070-96221875-92032014-01-01201410.1155/2014/848097848097Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet ShrinkageYan-long Chen0Pei-lin Zhang1Bing Li2Ding-hai Wu37th Department, Ordnance Engineering College, Shijiazhuang, China7th Department, Ordnance Engineering College, Shijiazhuang, China4th Department, Ordnance Engineering College, Shijiazhuang, China7th Department, Ordnance Engineering College, Shijiazhuang, ChinaThe potential application of a quantum-inspired adaptive wavelet shrinkage (QAWS) technique to mechanical vibration signals with a focus on noise reduction is studied in this paper. This quantum-inspired shrinkage algorithm combines three elements: an adaptive non-Gaussian statistical model of dual-tree complex wavelet transform (DTCWT) coefficients proposed to improve practicability of prior information, the quantum superposition introduced to describe the interscale dependencies of DTCWT coefficients, and the quantum-inspired probability of noise defined to shrink wavelet coefficients in a Bayesian framework. By combining all these elements, this signal processing scheme incorporating the DTCWT with quantum theory can both reduce noise and preserve signal details. A practical vibration signal measured from a power-shift steering transmission is utilized to evaluate the denoising ability of QAWS. Application results demonstrate the effectiveness of the proposed method. Moreover, it achieves better performance than hard and soft thresholding.http://dx.doi.org/10.1155/2014/848097 |
| spellingShingle | Yan-long Chen Pei-lin Zhang Bing Li Ding-hai Wu Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage Shock and Vibration |
| title | Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage |
| title_full | Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage |
| title_fullStr | Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage |
| title_full_unstemmed | Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage |
| title_short | Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage |
| title_sort | denoising of mechanical vibration signals using quantum inspired adaptive wavelet shrinkage |
| url | http://dx.doi.org/10.1155/2014/848097 |
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