Bearing fault diagnosis based on spectrum image sparse representation of vibration signal
Bearings are crucial for industrial production and susceptible to malfunction in rotating machines. Image analysis can give a comprehensive description of vibration signal, thus, it has achieved much more attention recently in fault diagnosis field. However, it brings lots of redundant information f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2018-09-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/1687814018797788 |
| _version_ | 1811336471681957888 |
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| author | Zhe Tong Wei Li Fan Jiang Zhencai Zhu Gongbo Zhou |
| author_facet | Zhe Tong Wei Li Fan Jiang Zhencai Zhu Gongbo Zhou |
| author_sort | Zhe Tong |
| collection | DOAJ |
| description | Bearings are crucial for industrial production and susceptible to malfunction in rotating machines. Image analysis can give a comprehensive description of vibration signal, thus, it has achieved much more attention recently in fault diagnosis field. However, it brings lots of redundant information from a single spectrum image matrix behind rich fault information, and massive spectrum image samples lead to exacerbation of this situation, which readily results in the accuracy-dropping problem of multiple local defective bearings diagnosis. To solve this issue, a novel feature extraction method based on image sparse representation is proposed. Original spectrum images are acquired through fast Fourier transformation. Sparse coefficient that reveals the underlying structure of spectrum image based on raw signals is extracted as the feature by implementing the orthogonal matching pursuit and K-singular value decomposition algorithm strategically, and then two-dimensional principal component analysis is applied for further processing of these features. Finally, fault types are identified based on a minimum distance strategy. The experimental results are given to demonstrate the effectiveness of the proposed method. |
| first_indexed | 2024-04-13T17:40:00Z |
| format | Article |
| id | doaj.art-b48e28f03e544e1f8f8e534973c8d602 |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-04-13T17:40:00Z |
| publishDate | 2018-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-b48e28f03e544e1f8f8e534973c8d6022022-12-22T02:37:13ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402018-09-011010.1177/1687814018797788Bearing fault diagnosis based on spectrum image sparse representation of vibration signalZhe TongWei LiFan JiangZhencai ZhuGongbo ZhouBearings are crucial for industrial production and susceptible to malfunction in rotating machines. Image analysis can give a comprehensive description of vibration signal, thus, it has achieved much more attention recently in fault diagnosis field. However, it brings lots of redundant information from a single spectrum image matrix behind rich fault information, and massive spectrum image samples lead to exacerbation of this situation, which readily results in the accuracy-dropping problem of multiple local defective bearings diagnosis. To solve this issue, a novel feature extraction method based on image sparse representation is proposed. Original spectrum images are acquired through fast Fourier transformation. Sparse coefficient that reveals the underlying structure of spectrum image based on raw signals is extracted as the feature by implementing the orthogonal matching pursuit and K-singular value decomposition algorithm strategically, and then two-dimensional principal component analysis is applied for further processing of these features. Finally, fault types are identified based on a minimum distance strategy. The experimental results are given to demonstrate the effectiveness of the proposed method.https://doi.org/10.1177/1687814018797788 |
| spellingShingle | Zhe Tong Wei Li Fan Jiang Zhencai Zhu Gongbo Zhou Bearing fault diagnosis based on spectrum image sparse representation of vibration signal Advances in Mechanical Engineering |
| title | Bearing fault diagnosis based on spectrum image sparse representation of vibration signal |
| title_full | Bearing fault diagnosis based on spectrum image sparse representation of vibration signal |
| title_fullStr | Bearing fault diagnosis based on spectrum image sparse representation of vibration signal |
| title_full_unstemmed | Bearing fault diagnosis based on spectrum image sparse representation of vibration signal |
| title_short | Bearing fault diagnosis based on spectrum image sparse representation of vibration signal |
| title_sort | bearing fault diagnosis based on spectrum image sparse representation of vibration signal |
| url | https://doi.org/10.1177/1687814018797788 |
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