Automated harmonic signal removal technique using stochastic subspace-based image feature extraction
This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a useful output signal prior to system identifica...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI Multidisciplinary Digital Publishing Institute
2020
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| Subjects: | |
| Online Access: | http://eprints.utm.my/90287/1/ZairAsrarAhmad2020_AutomatedHarmonicSignalRemovalTechnique.pdf |
| _version_ | 1796864998931169280 |
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| author | Abu Hasan, Muhammad Danial Ahmad, Zair Asrar Leong, Mohd. Salman Lim, Meng Hee |
| author_facet | Abu Hasan, Muhammad Danial Ahmad, Zair Asrar Leong, Mohd. Salman Lim, Meng Hee |
| author_sort | Abu Hasan, Muhammad Danial |
| collection | ePrints |
| description | This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a useful output signal prior to system identification. Stochastic subspace-based algorithms (SSI) methods are the most practical tool due to the consistency in modal parameters estimation. However, it will be problematic when applied to structures with rotating machines and the presence of harmonic excitations. Difficulties arise when automating this procedure without any human interaction and the problem is still unresolved because stochastic subspace-based algorithms (SSI) methods still require parameters (the maximum within-cluster distance) that are compulsory to be defined at start-up for each analysis of the dataset. Thus, the use of image-based feature extraction for clustering and classification of harmonic components and structural poles directly from a stabilization diagram and for modal system identification is the focus of the present paper. As a fundamental necessary condition, the algorithm has been assessed first from computed numerical responses and then applied to the experimental dataset with the presence of harmonic excitation. Results of the proposed approach for estimating modal parameters demonstrated very high accuracy and exhibited consistent results before and after removing harmonic components from the response signal. |
| first_indexed | 2024-03-05T20:50:19Z |
| format | Article |
| id | utm.eprints-90287 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:50:19Z |
| publishDate | 2020 |
| publisher | MDPI Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | utm.eprints-902872021-04-30T14:30:42Z http://eprints.utm.my/90287/ Automated harmonic signal removal technique using stochastic subspace-based image feature extraction Abu Hasan, Muhammad Danial Ahmad, Zair Asrar Leong, Mohd. Salman Lim, Meng Hee TJ Mechanical engineering and machinery This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a useful output signal prior to system identification. Stochastic subspace-based algorithms (SSI) methods are the most practical tool due to the consistency in modal parameters estimation. However, it will be problematic when applied to structures with rotating machines and the presence of harmonic excitations. Difficulties arise when automating this procedure without any human interaction and the problem is still unresolved because stochastic subspace-based algorithms (SSI) methods still require parameters (the maximum within-cluster distance) that are compulsory to be defined at start-up for each analysis of the dataset. Thus, the use of image-based feature extraction for clustering and classification of harmonic components and structural poles directly from a stabilization diagram and for modal system identification is the focus of the present paper. As a fundamental necessary condition, the algorithm has been assessed first from computed numerical responses and then applied to the experimental dataset with the presence of harmonic excitation. Results of the proposed approach for estimating modal parameters demonstrated very high accuracy and exhibited consistent results before and after removing harmonic components from the response signal. MDPI Multidisciplinary Digital Publishing Institute 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/90287/1/ZairAsrarAhmad2020_AutomatedHarmonicSignalRemovalTechnique.pdf Abu Hasan, Muhammad Danial and Ahmad, Zair Asrar and Leong, Mohd. Salman and Lim, Meng Hee (2020) Automated harmonic signal removal technique using stochastic subspace-based image feature extraction. Journal of Imaging, 6 (3). 0010-0010. ISSN 2313-433X http://dx.doi.org/10.3390/jimaging6030010 |
| spellingShingle | TJ Mechanical engineering and machinery Abu Hasan, Muhammad Danial Ahmad, Zair Asrar Leong, Mohd. Salman Lim, Meng Hee Automated harmonic signal removal technique using stochastic subspace-based image feature extraction |
| title | Automated harmonic signal removal technique using stochastic subspace-based image feature extraction |
| title_full | Automated harmonic signal removal technique using stochastic subspace-based image feature extraction |
| title_fullStr | Automated harmonic signal removal technique using stochastic subspace-based image feature extraction |
| title_full_unstemmed | Automated harmonic signal removal technique using stochastic subspace-based image feature extraction |
| title_short | Automated harmonic signal removal technique using stochastic subspace-based image feature extraction |
| title_sort | automated harmonic signal removal technique using stochastic subspace based image feature extraction |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.utm.my/90287/1/ZairAsrarAhmad2020_AutomatedHarmonicSignalRemovalTechnique.pdf |
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