Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings
Abstract Sparse subspace clustering (SSC) is a spectral clustering methodology. Since high‐dimensional data are often dispersed over the union of many low‐dimensional subspaces, their representation in a suitable dictionary is sparse. Therefore, SSC is an effective technology for diagnosing mechanic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-12-01
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| Series: | International Journal of Mechanical System Dynamics |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/msd2.12019 |
| _version_ | 1828880279353163776 |
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| author | Le Zhao Shaopu Yang Yongqiang Liu |
| author_facet | Le Zhao Shaopu Yang Yongqiang Liu |
| author_sort | Le Zhao |
| collection | DOAJ |
| description | Abstract Sparse subspace clustering (SSC) is a spectral clustering methodology. Since high‐dimensional data are often dispersed over the union of many low‐dimensional subspaces, their representation in a suitable dictionary is sparse. Therefore, SSC is an effective technology for diagnosing mechanical system faults. Its main purpose is to create a representation model that can reveal the real subspace structure of high‐dimensional data, construct a similarity matrix by using the sparse representation coefficients of high‐dimensional data, and then cluster the obtained representation coefficients and similarity matrix in subspace. However, the design of SSC algorithm is based on global expression in which each data point is represented by all possible cluster data points. This leads to nonzero terms in nondiagonal blocks of similar matrices, which reduces the recognition performance of matrices. To improve the clustering ability of SSC for rolling bearing and the robustness of the algorithm in the presence of a large number of background noise, a simultaneous dimensionality reduction subspace clustering technology is provided in this work. Through the feature extraction of envelope signal, the dimension of the feature matrix is reduced by singular value decomposition, and the Euclidean distance between samples is replaced by correlation distance. A dimension reduction graph‐based SSC technology is established. Simulation and bearing data of Western Reserve University show that the proposed algorithm can improve the accuracy and compactness of clustering. |
| first_indexed | 2024-12-13T09:44:39Z |
| format | Article |
| id | doaj.art-f9767cb4fdeb4da99e5dedd46f5cd5aa |
| institution | Directory Open Access Journal |
| issn | 2767-1402 |
| language | English |
| last_indexed | 2024-12-13T09:44:39Z |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Mechanical System Dynamics |
| spelling | doaj.art-f9767cb4fdeb4da99e5dedd46f5cd5aa2022-12-21T23:52:06ZengWileyInternational Journal of Mechanical System Dynamics2767-14022021-12-011220721910.1002/msd2.12019Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearingsLe Zhao0Shaopu Yang1Yongqiang Liu2State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang Hebei ChinaState Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang Hebei ChinaState Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang Hebei ChinaAbstract Sparse subspace clustering (SSC) is a spectral clustering methodology. Since high‐dimensional data are often dispersed over the union of many low‐dimensional subspaces, their representation in a suitable dictionary is sparse. Therefore, SSC is an effective technology for diagnosing mechanical system faults. Its main purpose is to create a representation model that can reveal the real subspace structure of high‐dimensional data, construct a similarity matrix by using the sparse representation coefficients of high‐dimensional data, and then cluster the obtained representation coefficients and similarity matrix in subspace. However, the design of SSC algorithm is based on global expression in which each data point is represented by all possible cluster data points. This leads to nonzero terms in nondiagonal blocks of similar matrices, which reduces the recognition performance of matrices. To improve the clustering ability of SSC for rolling bearing and the robustness of the algorithm in the presence of a large number of background noise, a simultaneous dimensionality reduction subspace clustering technology is provided in this work. Through the feature extraction of envelope signal, the dimension of the feature matrix is reduced by singular value decomposition, and the Euclidean distance between samples is replaced by correlation distance. A dimension reduction graph‐based SSC technology is established. Simulation and bearing data of Western Reserve University show that the proposed algorithm can improve the accuracy and compactness of clustering.https://doi.org/10.1002/msd2.12019correlation distancedimension reductionsparse subspace clustering |
| spellingShingle | Le Zhao Shaopu Yang Yongqiang Liu Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings International Journal of Mechanical System Dynamics correlation distance dimension reduction sparse subspace clustering |
| title | Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings |
| title_full | Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings |
| title_fullStr | Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings |
| title_full_unstemmed | Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings |
| title_short | Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings |
| title_sort | dimension reduction graph based sparse subspace clustering for intelligent fault identification of rolling element bearings |
| topic | correlation distance dimension reduction sparse subspace clustering |
| url | https://doi.org/10.1002/msd2.12019 |
| work_keys_str_mv | AT lezhao dimensionreductiongraphbasedsparsesubspaceclusteringforintelligentfaultidentificationofrollingelementbearings AT shaopuyang dimensionreductiongraphbasedsparsesubspaceclusteringforintelligentfaultidentificationofrollingelementbearings AT yongqiangliu dimensionreductiongraphbasedsparsesubspaceclusteringforintelligentfaultidentificationofrollingelementbearings |