Dynamic generalized principal component analysis with applications to fault subspace modeling
In order to solve the problem of inaccurate modeling of fault subspace, traditional fault subspace modeling method did not consider the fact that fault data contain both normal and fault condition information, or did not consider the dynamic factors in the fault data, these flaws may lead to the cas...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2022-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022091/ |
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| Summary: | In order to solve the problem of inaccurate modeling of fault subspace, traditional fault subspace modeling method did not consider the fact that fault data contain both normal and fault condition information, or did not consider the dynamic factors in the fault data, these flaws may lead to the case that the fault subspace cannot be extracted accurately, a dynamic generalized principal component analysis (DGPCA) method was proposed.By reorganizing the lagged input data, the dynamic characteristics between normal and fault data were extracted by the proposed DGPCA method, and then the fault subspaces could be modeled for further fault diagnosis.Finally, simulation results confirm the availability of the proposed method for fault subspace modeling and fault diagnosis. |
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| ISSN: | 1000-436X |