Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model
Aiming at the mechanical equipment in the fault diagnosis process, the traditional Shannon–Nyquist sampling theorem is used for data collection, which faces main problems of storage, transmission, and processing of mechanical vibration signals. This paper presents a novel method of compressed sensin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2020-03-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/0020294019898725 |
| _version_ | 1798030417948835840 |
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| author | Xiao Chaoang Tang Hesheng Ren Yan |
| author_facet | Xiao Chaoang Tang Hesheng Ren Yan |
| author_sort | Xiao Chaoang |
| collection | DOAJ |
| description | Aiming at the mechanical equipment in the fault diagnosis process, the traditional Shannon–Nyquist sampling theorem is used for data collection, which faces main problems of storage, transmission, and processing of mechanical vibration signals. This paper presents a novel method of compressed sensing reconstruction for axial piston pump bearing vibration signals based on the adaptive sparse dictionary model. First, vibration signals were divided into blocks, and an energy sequence was produced in accordance with the energy of each signal block. Second, the energy sequence of each signal block was classified by the quantum particle swarm optimization algorithm. Finally, the reconstruction of machinery vibration signals was carried out using the K-SVD dictionary algorithm. The average relative error of the reconstructed signal obtained by the proposed algorithm is 4.25%, and the reconstruction time decreases by 43.6% when the compression ratio is 1.6. |
| first_indexed | 2024-04-11T19:39:53Z |
| format | Article |
| id | doaj.art-c118e0daa9cb4d32a1a7f5d9108156ad |
| institution | Directory Open Access Journal |
| issn | 0020-2940 |
| language | English |
| last_indexed | 2024-04-11T19:39:53Z |
| publishDate | 2020-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj.art-c118e0daa9cb4d32a1a7f5d9108156ad2022-12-22T04:06:44ZengSAGE PublishingMeasurement + Control0020-29402020-03-015310.1177/0020294019898725Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary modelXiao Chaoang0Tang Hesheng1Ren Yan2College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, ChinaAiming at the mechanical equipment in the fault diagnosis process, the traditional Shannon–Nyquist sampling theorem is used for data collection, which faces main problems of storage, transmission, and processing of mechanical vibration signals. This paper presents a novel method of compressed sensing reconstruction for axial piston pump bearing vibration signals based on the adaptive sparse dictionary model. First, vibration signals were divided into blocks, and an energy sequence was produced in accordance with the energy of each signal block. Second, the energy sequence of each signal block was classified by the quantum particle swarm optimization algorithm. Finally, the reconstruction of machinery vibration signals was carried out using the K-SVD dictionary algorithm. The average relative error of the reconstructed signal obtained by the proposed algorithm is 4.25%, and the reconstruction time decreases by 43.6% when the compression ratio is 1.6.https://doi.org/10.1177/0020294019898725 |
| spellingShingle | Xiao Chaoang Tang Hesheng Ren Yan Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model Measurement + Control |
| title | Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model |
| title_full | Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model |
| title_fullStr | Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model |
| title_full_unstemmed | Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model |
| title_short | Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model |
| title_sort | compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model |
| url | https://doi.org/10.1177/0020294019898725 |
| work_keys_str_mv | AT xiaochaoang compressedsensingreconstructionforaxialpistonpumpbearingvibrationsignalsbasedonadaptivesparsedictionarymodel AT tanghesheng compressedsensingreconstructionforaxialpistonpumpbearingvibrationsignalsbasedonadaptivesparsedictionarymodel AT renyan compressedsensingreconstructionforaxialpistonpumpbearingvibrationsignalsbasedonadaptivesparsedictionarymodel |