Uncertainty-Based Fusion Method for Structural Modal Parameter Identification
The structural modal parameter identification method can be classified into time-domain and frequency-domain methods. Practically, two types of methods are characterized by different advantages, and the estimated modal parameters are always subjected to statistical uncertainties due to measurement n...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4397 |
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| author | Xiaoteng Liu Zirui Dong Hongxia Ji Zhenjiang Yue Jie Kang |
| author_facet | Xiaoteng Liu Zirui Dong Hongxia Ji Zhenjiang Yue Jie Kang |
| author_sort | Xiaoteng Liu |
| collection | DOAJ |
| description | The structural modal parameter identification method can be classified into time-domain and frequency-domain methods. Practically, two types of methods are characterized by different advantages, and the estimated modal parameters are always subjected to statistical uncertainties due to measurement noise. In this work, an uncertainty-based fusion method for structural mode identification is proposed to merge the advantages of different methods. The extensively applied time-domain AutoRegressive (AR) and frequency-domain Left-Matrix Fraction (LMF) models are expressed in a unified parametric model. With this unified model, a generalized framework is developed to identify the modal parameters of structures and compute variances associated with modal parameter estimates. The final modal parameter estimates are computed as the inverse-variance weighted sum of the results identified from different methods. A numerical and an experimental example demonstrate that the proposed method can obtain reliable modal parameter estimates, substantially mitigating the occurrence of extremely large estimation errors. Furthermore, the fusion method demonstrates enhanced identification capabilities, effectively reducing the likelihood of missing structural modes. |
| format | Article |
| id | doaj-art-dc5704f218b241788ad7bbfe16bfd52a |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-dc5704f218b241788ad7bbfe16bfd52a2025-08-20T03:32:28ZengMDPI AGSensors1424-82202025-07-012514439710.3390/s25144397Uncertainty-Based Fusion Method for Structural Modal Parameter IdentificationXiaoteng Liu0Zirui Dong1Hongxia Ji2Zhenjiang Yue3Jie Kang4College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaShanghai Institute of Spacecraft Equipment, Shanghai 201109, ChinaShanghai Institute of Spacecraft Equipment, Shanghai 201109, ChinaIntelligent Science and Technology Academy of CASIC, Beijing 100043, ChinaCollege of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaThe structural modal parameter identification method can be classified into time-domain and frequency-domain methods. Practically, two types of methods are characterized by different advantages, and the estimated modal parameters are always subjected to statistical uncertainties due to measurement noise. In this work, an uncertainty-based fusion method for structural mode identification is proposed to merge the advantages of different methods. The extensively applied time-domain AutoRegressive (AR) and frequency-domain Left-Matrix Fraction (LMF) models are expressed in a unified parametric model. With this unified model, a generalized framework is developed to identify the modal parameters of structures and compute variances associated with modal parameter estimates. The final modal parameter estimates are computed as the inverse-variance weighted sum of the results identified from different methods. A numerical and an experimental example demonstrate that the proposed method can obtain reliable modal parameter estimates, substantially mitigating the occurrence of extremely large estimation errors. Furthermore, the fusion method demonstrates enhanced identification capabilities, effectively reducing the likelihood of missing structural modes.https://www.mdpi.com/1424-8220/25/14/4397operational modal analysisuncertainty quantificationdata fusionAutoRegressive modelLeft-Matrix Fraction model |
| spellingShingle | Xiaoteng Liu Zirui Dong Hongxia Ji Zhenjiang Yue Jie Kang Uncertainty-Based Fusion Method for Structural Modal Parameter Identification Sensors operational modal analysis uncertainty quantification data fusion AutoRegressive model Left-Matrix Fraction model |
| title | Uncertainty-Based Fusion Method for Structural Modal Parameter Identification |
| title_full | Uncertainty-Based Fusion Method for Structural Modal Parameter Identification |
| title_fullStr | Uncertainty-Based Fusion Method for Structural Modal Parameter Identification |
| title_full_unstemmed | Uncertainty-Based Fusion Method for Structural Modal Parameter Identification |
| title_short | Uncertainty-Based Fusion Method for Structural Modal Parameter Identification |
| title_sort | uncertainty based fusion method for structural modal parameter identification |
| topic | operational modal analysis uncertainty quantification data fusion AutoRegressive model Left-Matrix Fraction model |
| url | https://www.mdpi.com/1424-8220/25/14/4397 |
| work_keys_str_mv | AT xiaotengliu uncertaintybasedfusionmethodforstructuralmodalparameteridentification AT ziruidong uncertaintybasedfusionmethodforstructuralmodalparameteridentification AT hongxiaji uncertaintybasedfusionmethodforstructuralmodalparameteridentification AT zhenjiangyue uncertaintybasedfusionmethodforstructuralmodalparameteridentification AT jiekang uncertaintybasedfusionmethodforstructuralmodalparameteridentification |