Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning
Addressing the problem that it is difficult to extract the features of vibration signal and diagnose the fault of rolling bearing, we propose a novel diagnosis method combining multisynchrosqueezing S transform and faster dictionary learning (MSSST-FDL). Firstly, MSSST is adopted to transform vibrat...
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| Vydané v: | Shock and vibration Ročník 2021; číslo 1 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Cairo
Hindawi
2021
John Wiley & Sons, Inc Wiley |
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| ISSN: | 1070-9622, 1875-9203 |
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| Abstract | Addressing the problem that it is difficult to extract the features of vibration signal and diagnose the fault of rolling bearing, we propose a novel diagnosis method combining multisynchrosqueezing S transform and faster dictionary learning (MSSST-FDL). Firstly, MSSST is adopted to transform vibration signals into high-resolution time-frequency images. Then, the local binary pattern (LBP) operator is introduced to extract the low-dimensional texture features of time-frequency images, which improves the speed of fault recognition. Finally, nonnegative matrix factorization (NMF) with only one hyperparameter and nonnegative linear equation are used to solve the dictionary learning and feature coding, respectively. The feature coding is input into the classifier for training and recognition. Experiments show that our method performs well on the rolling bearing dataset of Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT). Further, the proposed method is applied to the loudspeaker pure-tone detection dataset, and the loudspeaker anomaly diagnosis is achieved. The diagnosis results verify that our method can meet the needs of practical engineering. |
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| AbstractList | Addressing the problem that it is difficult to extract the features of vibration signal and diagnose the fault of rolling bearing, we propose a novel diagnosis method combining multisynchrosqueezing S transform and faster dictionary learning (MSSST-FDL). Firstly, MSSST is adopted to transform vibration signals into high-resolution time-frequency images. Then, the local binary pattern (LBP) operator is introduced to extract the low-dimensional texture features of time-frequency images, which improves the speed of fault recognition. Finally, nonnegative matrix factorization (NMF) with only one hyperparameter and nonnegative linear equation are used to solve the dictionary learning and feature coding, respectively. The feature coding is input into the classifier for training and recognition. Experiments show that our method performs well on the rolling bearing dataset of Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT). Further, the proposed method is applied to the loudspeaker pure-tone detection dataset, and the loudspeaker anomaly diagnosis is achieved. The diagnosis results verify that our method can meet the needs of practical engineering. |
| Audience | Academic |
| Author | Wu, Bo Sun, Guodong Zhou, Hongyu Hu, Ye |
| Author_xml | – sequence: 1 givenname: Guodong orcidid: 0000-0002-6756-7007 surname: Sun fullname: Sun, Guodong organization: School of Mechanical EngineeringHubei University of TechnologyWuhanHubeiChinahbut.edu.cn – sequence: 2 givenname: Ye orcidid: 0000-0002-4927-8367 surname: Hu fullname: Hu, Ye organization: School of Mechanical EngineeringHubei University of TechnologyWuhanHubeiChinahbut.edu.cn – sequence: 3 givenname: Bo orcidid: 0000-0001-7362-1607 surname: Wu fullname: Wu, Bo organization: Shanghai Advanced Research InstituteChinese Academy of SciencesShanghaiChinacas.cn – sequence: 4 givenname: Hongyu orcidid: 0000-0003-0359-0803 surname: Zhou fullname: Zhou, Hongyu organization: School of Mechanical EngineeringHubei University of TechnologyWuhanHubeiChinahbut.edu.cn |
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| Cites_doi | 10.1038/44565 10.1016/j.measurement.2020.108392 10.1155/2019/1531079 10.1109/JSEN.2020.3042182 10.1016/j.jsv.2010.11.019 10.1155/2019/9375437 10.1016/j.ymssp.2019.106587 10.1109/78.492555 10.1109/18.57199 10.1016/j.eswa.2011.02.008 10.1109/TII.2020.3030186 10.3390/s18051429 10.1002/env.3170050203 10.1016/j.knosys.2020.106507 10.1109/tpami.2002.1017623 10.1016/j.knosys.2019.105313 10.1109/tassp.1977.1162950 10.1016/j.ymssp.2013.09.003 10.1155/2017/5067651 10.1016/j.knosys.2019.07.008 10.1109/jsen.2019.2913006 10.1016/0031-3203(95)00067-4 10.1109/tie.2017.2745473 10.1109/18.923723 10.1109/tie.2018.2868296 10.1109/lsp.2004.839696 10.1109/tgrs.2015.2466660 10.1049/ji-3-2.1946.0074 10.1109/PHM-Chongqing.2018.00056 10.1190/1.1441328 10.1109/msp.2013.2265316 10.1109/jsen.2019.2910868 |
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| Copyright | Copyright © 2021 Guodong Sun et al. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Guodong Sun et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Algorithms Analysis Bearings Coding Datasets Dictionaries Energy Failure prevention Fault diagnosis Feature extraction Image resolution Learning Linear equations Loudspeakers Machine learning Machinery Mathematical analysis Matrix methods Methods Neural networks Object recognition Roller bearings Texture recognition Transformations (mathematics) Vibration Wavelet transforms |
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| Title | Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning |
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