Unraveling Mixtures: A Novel Underdetermined Blind Source Separation Approach via Sparse Component Analysis
Underdetermined blind source separation (UBSS) is a critical technique in the field of intelligent mechanical operation and maintenance that allows for the disentanglement of source signals from their mixtures without the need for prior knowledge or extensive sensor information. The accuracy of sour...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10379675/ |
| _version_ | 1797335503193767936 |
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| author | Yanyang Li Jindong Wang Haiyang Zhao Chang Wang Zhichao Ma |
| author_facet | Yanyang Li Jindong Wang Haiyang Zhao Chang Wang Zhichao Ma |
| author_sort | Yanyang Li |
| collection | DOAJ |
| description | Underdetermined blind source separation (UBSS) is a critical technique in the field of intelligent mechanical operation and maintenance that allows for the disentanglement of source signals from their mixtures without the need for prior knowledge or extensive sensor information. The accuracy of source signal recovery depends on the estimation of the mixing matrix. To promote sparsity in source signals, we employed methods such as sparse representation and sparse component analysis. Traditional approaches, such as the Short-Time Fourier Transform (STFT), often suffer from limited time-frequency resolution, motivating the adoption of the Synchronous Extraction Transformation (SET) algorithm, which closely approximates the ideal time-frequency transform. SET significantly enhances the sparsity of the signals, thus facilitating the separation of the mixed signals. In the context of sparse component analysis, we introduce an improved density peaks clustering (DPC) method that successfully resolves source number estimation issues and robustly eliminates outliers. This improvement leads to a more accurate mixing matrix estimation. To determine the cluster centers, we employed K-means clustering, further refining our source separation process. In summary, our study presents an innovative approach that combines the Synchronous Extraction Transformation (SET) with ‘an improved density peaks clustering (DPC)’ method and K-means for mixing matrix estimation. Source signal recovery was achieved using the shortest-path algorithm. Extensive simulations and experiments validate the effectiveness of the method, outperforming the traditional techniques. When applied to rolling bearing fault diagnosis, the proposed approach effectively separates complex signals and accurately identifies the fault characteristic frequencies. |
| first_indexed | 2024-03-08T08:39:08Z |
| format | Article |
| id | doaj.art-480acd8378844d1dab03150ea05675a2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T08:39:08Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-480acd8378844d1dab03150ea05675a22024-02-02T00:02:43ZengIEEEIEEE Access2169-35362024-01-0112149491496310.1109/ACCESS.2024.334942710379675Unraveling Mixtures: A Novel Underdetermined Blind Source Separation Approach via Sparse Component AnalysisYanyang Li0https://orcid.org/0009-0009-4397-0297Jindong Wang1Haiyang Zhao2https://orcid.org/0000-0002-8007-4434Chang Wang3Zhichao Ma4College of Mechanical Science and Engineering, Northeast Petroleum University, Daqing, ChinaCollege of Mechanical Science and Engineering, Northeast Petroleum University, Daqing, ChinaCollege of Mechanical Science and Engineering, Northeast Petroleum University, Daqing, ChinaCollege of Mechanical Science and Engineering, Northeast Petroleum University, Daqing, ChinaCollege of Mechanical Science and Engineering, Northeast Petroleum University, Daqing, ChinaUnderdetermined blind source separation (UBSS) is a critical technique in the field of intelligent mechanical operation and maintenance that allows for the disentanglement of source signals from their mixtures without the need for prior knowledge or extensive sensor information. The accuracy of source signal recovery depends on the estimation of the mixing matrix. To promote sparsity in source signals, we employed methods such as sparse representation and sparse component analysis. Traditional approaches, such as the Short-Time Fourier Transform (STFT), often suffer from limited time-frequency resolution, motivating the adoption of the Synchronous Extraction Transformation (SET) algorithm, which closely approximates the ideal time-frequency transform. SET significantly enhances the sparsity of the signals, thus facilitating the separation of the mixed signals. In the context of sparse component analysis, we introduce an improved density peaks clustering (DPC) method that successfully resolves source number estimation issues and robustly eliminates outliers. This improvement leads to a more accurate mixing matrix estimation. To determine the cluster centers, we employed K-means clustering, further refining our source separation process. In summary, our study presents an innovative approach that combines the Synchronous Extraction Transformation (SET) with ‘an improved density peaks clustering (DPC)’ method and K-means for mixing matrix estimation. Source signal recovery was achieved using the shortest-path algorithm. Extensive simulations and experiments validate the effectiveness of the method, outperforming the traditional techniques. When applied to rolling bearing fault diagnosis, the proposed approach effectively separates complex signals and accurately identifies the fault characteristic frequencies.https://ieeexplore.ieee.org/document/10379675/Underdetermined blind source separationsynchronous extraction algorithmK-meanssparse component analysiscomplex mechanical signals |
| spellingShingle | Yanyang Li Jindong Wang Haiyang Zhao Chang Wang Zhichao Ma Unraveling Mixtures: A Novel Underdetermined Blind Source Separation Approach via Sparse Component Analysis IEEE Access Underdetermined blind source separation synchronous extraction algorithm K-means sparse component analysis complex mechanical signals |
| title | Unraveling Mixtures: A Novel Underdetermined Blind Source Separation Approach via Sparse Component Analysis |
| title_full | Unraveling Mixtures: A Novel Underdetermined Blind Source Separation Approach via Sparse Component Analysis |
| title_fullStr | Unraveling Mixtures: A Novel Underdetermined Blind Source Separation Approach via Sparse Component Analysis |
| title_full_unstemmed | Unraveling Mixtures: A Novel Underdetermined Blind Source Separation Approach via Sparse Component Analysis |
| title_short | Unraveling Mixtures: A Novel Underdetermined Blind Source Separation Approach via Sparse Component Analysis |
| title_sort | unraveling mixtures a novel underdetermined blind source separation approach via sparse component analysis |
| topic | Underdetermined blind source separation synchronous extraction algorithm K-means sparse component analysis complex mechanical signals |
| url | https://ieeexplore.ieee.org/document/10379675/ |
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