State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future Perspectives
This paper presents a comprehensive review of automated modal identification techniques, focusing on various established and emerging methods, particularly Stochastic Subspace Identification (SSI). Automated modal identification plays a crucial role in structural health monitoring (SHM) by extractin...
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MDPI AG
2025-01-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/39 |
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| author | Hasan Mostafaei Mahdi Ghamami |
| author_facet | Hasan Mostafaei Mahdi Ghamami |
| author_sort | Hasan Mostafaei |
| collection | DOAJ |
| description | This paper presents a comprehensive review of automated modal identification techniques, focusing on various established and emerging methods, particularly Stochastic Subspace Identification (SSI). Automated modal identification plays a crucial role in structural health monitoring (SHM) by extracting key modal parameters such as natural frequencies, damping ratios, and mode shapes from vibration data. To address the limitations of traditional manual methods, several approaches have been developed to automate this process. Among these, SSI stands out as one of the most effective time-domain methods due to its robustness in handling noisy environments and closely spaced modes. This review examines SSI-based algorithms, covering essential components such as system identification, noise mode elimination, stabilization diagram interpretation, and clustering techniques for mode identification. Advanced SSI implementations that incorporate real-time recursive estimation, adaptive stabilization criteria, and automated mode selection are also discussed. Additionally, the review covers frequency-domain methods like Frequency Domain Decomposition (FDD) and Enhanced Frequency Domain Decomposition (EFDD), highlighting their application in spectral analysis and modal parameter extraction. Techniques based on machine learning (ML), deep learning (DL), and artificial intelligence (AI) are explored for their ability to automate feature extraction, classification, and decision making in large-scale SHM systems. This review concludes by highlighting the current challenges, such as computational demands and data management, and proposing future directions for research in automated modal analysis to support resilient, sustainable infrastructure. |
| format | Article |
| id | doaj-art-9741c7de77d54c22a2f4e1baea28b7cf |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-9741c7de77d54c22a2f4e1baea28b7cf2025-01-24T13:39:13ZengMDPI AGMachines2075-17022025-01-011313910.3390/machines13010039State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future PerspectivesHasan Mostafaei0Mahdi Ghamami1School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, AustraliaDepartment of Mechanical Engineering, Isfahan University of Technology (IUT), Isfahan 84156-83111, IranThis paper presents a comprehensive review of automated modal identification techniques, focusing on various established and emerging methods, particularly Stochastic Subspace Identification (SSI). Automated modal identification plays a crucial role in structural health monitoring (SHM) by extracting key modal parameters such as natural frequencies, damping ratios, and mode shapes from vibration data. To address the limitations of traditional manual methods, several approaches have been developed to automate this process. Among these, SSI stands out as one of the most effective time-domain methods due to its robustness in handling noisy environments and closely spaced modes. This review examines SSI-based algorithms, covering essential components such as system identification, noise mode elimination, stabilization diagram interpretation, and clustering techniques for mode identification. Advanced SSI implementations that incorporate real-time recursive estimation, adaptive stabilization criteria, and automated mode selection are also discussed. Additionally, the review covers frequency-domain methods like Frequency Domain Decomposition (FDD) and Enhanced Frequency Domain Decomposition (EFDD), highlighting their application in spectral analysis and modal parameter extraction. Techniques based on machine learning (ML), deep learning (DL), and artificial intelligence (AI) are explored for their ability to automate feature extraction, classification, and decision making in large-scale SHM systems. This review concludes by highlighting the current challenges, such as computational demands and data management, and proposing future directions for research in automated modal analysis to support resilient, sustainable infrastructure.https://www.mdpi.com/2075-1702/13/1/39automated modal identificationmachine learningdeep learningclustering analysisstochastic subspace identificationstructural health monitoring |
| spellingShingle | Hasan Mostafaei Mahdi Ghamami State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future Perspectives Machines automated modal identification machine learning deep learning clustering analysis stochastic subspace identification structural health monitoring |
| title | State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future Perspectives |
| title_full | State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future Perspectives |
| title_fullStr | State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future Perspectives |
| title_full_unstemmed | State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future Perspectives |
| title_short | State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future Perspectives |
| title_sort | state of the art in automated operational modal identification algorithms applications and future perspectives |
| topic | automated modal identification machine learning deep learning clustering analysis stochastic subspace identification structural health monitoring |
| url | https://www.mdpi.com/2075-1702/13/1/39 |
| work_keys_str_mv | AT hasanmostafaei stateoftheartinautomatedoperationalmodalidentificationalgorithmsapplicationsandfutureperspectives AT mahdighamami stateoftheartinautomatedoperationalmodalidentificationalgorithmsapplicationsandfutureperspectives |