2DUMAP: Two-Dimensional Uniform Manifold Approximation and Projection for Fault Diagnosis
With the continuous development of industry, the operational data of mechanical equipment has grown exponentially. High-dimensional fault data often contain a significant amount of noise signals and redundant information, severely impacting the performance of fault diagnosis methods. The currently p...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10845752/ |
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| author | Benchao Li Yuanyuan Zheng Ruisheng Ran |
| author_facet | Benchao Li Yuanyuan Zheng Ruisheng Ran |
| author_sort | Benchao Li |
| collection | DOAJ |
| description | With the continuous development of industry, the operational data of mechanical equipment has grown exponentially. High-dimensional fault data often contain a significant amount of noise signals and redundant information, severely impacting the performance of fault diagnosis methods. The currently popular manifold learning dimensionality reduction algorithm, Uniform Manifold Approximation and Projection (UMAP), effectively extract complex nonlinear relationships in fault diagnosis data while preserving both local and global structures. However, the UMAP has Out-of-Sample Problem, making it unable to directly map new samples to the learned low-dimensional space. To address this problem, a mapping matrix is learned to directly extend Out-of-Sample data, but this approach still faces the issue of the mapping matrix being too large. To tackle this challenge, the Two-Dimensional UMAP method is proposed, which transforms one-dimensional vector inputs into two-dimensional matrix representations. Subsequently, considering discriminant information, a Supervised Two-dimensional UMAP algorithm (2DSUMAP) is proposed. This study conducts fault diagnosis experiments on 7 datasets of bearings and gears to comprehensively evaluate the proposed methods. The experiments demonstrate that these methods solve the inherent Out-of-Sample Problem of the UMAP, effectively reduce algorithm complexity, and have superior classification performance and strong robustness in practical fault diagnosis applications. |
| format | Article |
| id | doaj-art-10f5ac7d334d4feba56bb1638b4fe704 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-10f5ac7d334d4feba56bb1638b4fe7042025-01-28T00:01:07ZengIEEEIEEE Access2169-35362025-01-0113128191283110.1109/ACCESS.2025.3531712108457522DUMAP: Two-Dimensional Uniform Manifold Approximation and Projection for Fault DiagnosisBenchao Li0https://orcid.org/0009-0003-6239-2230Yuanyuan Zheng1https://orcid.org/0009-0005-5828-9892Ruisheng Ran2https://orcid.org/0000-0002-0785-2703College of Computer and Information Science, Chongqing Normal University, Chongqing, ChinaCollege of Computer and Information Science, Chongqing Normal University, Chongqing, ChinaCollege of Computer and Information Science, Chongqing Normal University, Chongqing, ChinaWith the continuous development of industry, the operational data of mechanical equipment has grown exponentially. High-dimensional fault data often contain a significant amount of noise signals and redundant information, severely impacting the performance of fault diagnosis methods. The currently popular manifold learning dimensionality reduction algorithm, Uniform Manifold Approximation and Projection (UMAP), effectively extract complex nonlinear relationships in fault diagnosis data while preserving both local and global structures. However, the UMAP has Out-of-Sample Problem, making it unable to directly map new samples to the learned low-dimensional space. To address this problem, a mapping matrix is learned to directly extend Out-of-Sample data, but this approach still faces the issue of the mapping matrix being too large. To tackle this challenge, the Two-Dimensional UMAP method is proposed, which transforms one-dimensional vector inputs into two-dimensional matrix representations. Subsequently, considering discriminant information, a Supervised Two-dimensional UMAP algorithm (2DSUMAP) is proposed. This study conducts fault diagnosis experiments on 7 datasets of bearings and gears to comprehensively evaluate the proposed methods. The experiments demonstrate that these methods solve the inherent Out-of-Sample Problem of the UMAP, effectively reduce algorithm complexity, and have superior classification performance and strong robustness in practical fault diagnosis applications.https://ieeexplore.ieee.org/document/10845752/UMAPout-of-sample problemfault diagnosismanifold learning |
| spellingShingle | Benchao Li Yuanyuan Zheng Ruisheng Ran 2DUMAP: Two-Dimensional Uniform Manifold Approximation and Projection for Fault Diagnosis IEEE Access UMAP out-of-sample problem fault diagnosis manifold learning |
| title | 2DUMAP: Two-Dimensional Uniform Manifold Approximation and Projection for Fault Diagnosis |
| title_full | 2DUMAP: Two-Dimensional Uniform Manifold Approximation and Projection for Fault Diagnosis |
| title_fullStr | 2DUMAP: Two-Dimensional Uniform Manifold Approximation and Projection for Fault Diagnosis |
| title_full_unstemmed | 2DUMAP: Two-Dimensional Uniform Manifold Approximation and Projection for Fault Diagnosis |
| title_short | 2DUMAP: Two-Dimensional Uniform Manifold Approximation and Projection for Fault Diagnosis |
| title_sort | 2dumap two dimensional uniform manifold approximation and projection for fault diagnosis |
| topic | UMAP out-of-sample problem fault diagnosis manifold learning |
| url | https://ieeexplore.ieee.org/document/10845752/ |
| work_keys_str_mv | AT benchaoli 2dumaptwodimensionaluniformmanifoldapproximationandprojectionforfaultdiagnosis AT yuanyuanzheng 2dumaptwodimensionaluniformmanifoldapproximationandprojectionforfaultdiagnosis AT ruishengran 2dumaptwodimensionaluniformmanifoldapproximationandprojectionforfaultdiagnosis |