An Improved Kernel Entropy Component Analysis for Damage Detection Under Environmental and Operational Variations
Environmental effects often trigger false alarms in vibration-based damage detection methods used for structural health monitoring (SHM). While conventional techniques like Principal Component Analysis (PCA) and cointegration have been somewhat effective in addressing this issue, challenges such as...
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1332 |
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| author | Shuigen Hu Jian Yang Jiezhong Huang Dongsheng Li Cheng Li |
| author_facet | Shuigen Hu Jian Yang Jiezhong Huang Dongsheng Li Cheng Li |
| author_sort | Shuigen Hu |
| collection | DOAJ |
| description | Environmental effects often trigger false alarms in vibration-based damage detection methods used for structural health monitoring (SHM). While conventional techniques like Principal Component Analysis (PCA) and cointegration have been somewhat effective in addressing this issue, challenges such as measurement noise, nonlinear behavior, and non-Gaussian data distribution continue to affect their performance. To address these limitations, a novel damage detection method combining Variational Mode Decomposition (VMD) and Dynamic Kernel Entropy Component Analysis (DKECA) is proposed. The proposed method initially uses the VMD technique to remove seasonal patterns and noise from the modal frequencies. Subsequently, a DKECA model is constructed based on a time-delay data matrix, and the principal components that maximize the Rényi entropy in the high-dimensional space are selected. Using these principal components, a damage detector developed from the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>T</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> statistic is used to determine damage indices for SHM. The effectiveness of the proposed method is verified through both a simulated 7-DOF model and real-world data from the Z24 bridge, with comparative studies highlighting its advantages over existing techniques. |
| format | Article |
| id | doaj-art-e2551afa36624301b8807d3d3af6b75b |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e2551afa36624301b8807d3d3af6b75b2025-08-20T02:52:45ZengMDPI AGSensors1424-82202025-02-01255133210.3390/s25051332An Improved Kernel Entropy Component Analysis for Damage Detection Under Environmental and Operational VariationsShuigen Hu0Jian Yang1Jiezhong Huang2Dongsheng Li3Cheng Li4Anhui Provincial International Joint Research Center of Data Diagnosis and Smart Maintenance on Bridge Structures, Chuzhou 239099, ChinaDepartment of Civil and Intelligent Construction Engineering, Shantou University, Shantou 515063, ChinaDepartment of Civil and Intelligent Construction Engineering, Shantou University, Shantou 515063, ChinaDepartment of Civil and Intelligent Construction Engineering, Shantou University, Shantou 515063, ChinaKey Laboratory for Health and Safety of Bridge Structures, Wuhan 430034, ChinaEnvironmental effects often trigger false alarms in vibration-based damage detection methods used for structural health monitoring (SHM). While conventional techniques like Principal Component Analysis (PCA) and cointegration have been somewhat effective in addressing this issue, challenges such as measurement noise, nonlinear behavior, and non-Gaussian data distribution continue to affect their performance. To address these limitations, a novel damage detection method combining Variational Mode Decomposition (VMD) and Dynamic Kernel Entropy Component Analysis (DKECA) is proposed. The proposed method initially uses the VMD technique to remove seasonal patterns and noise from the modal frequencies. Subsequently, a DKECA model is constructed based on a time-delay data matrix, and the principal components that maximize the Rényi entropy in the high-dimensional space are selected. Using these principal components, a damage detector developed from the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>T</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> statistic is used to determine damage indices for SHM. The effectiveness of the proposed method is verified through both a simulated 7-DOF model and real-world data from the Z24 bridge, with comparative studies highlighting its advantages over existing techniques.https://www.mdpi.com/1424-8220/25/5/1332structural health monitoringdamage detectionenvironmental and operational variationvariational mode decompositionprincipal component analysisgaussian process regression |
| spellingShingle | Shuigen Hu Jian Yang Jiezhong Huang Dongsheng Li Cheng Li An Improved Kernel Entropy Component Analysis for Damage Detection Under Environmental and Operational Variations Sensors structural health monitoring damage detection environmental and operational variation variational mode decomposition principal component analysis gaussian process regression |
| title | An Improved Kernel Entropy Component Analysis for Damage Detection Under Environmental and Operational Variations |
| title_full | An Improved Kernel Entropy Component Analysis for Damage Detection Under Environmental and Operational Variations |
| title_fullStr | An Improved Kernel Entropy Component Analysis for Damage Detection Under Environmental and Operational Variations |
| title_full_unstemmed | An Improved Kernel Entropy Component Analysis for Damage Detection Under Environmental and Operational Variations |
| title_short | An Improved Kernel Entropy Component Analysis for Damage Detection Under Environmental and Operational Variations |
| title_sort | improved kernel entropy component analysis for damage detection under environmental and operational variations |
| topic | structural health monitoring damage detection environmental and operational variation variational mode decomposition principal component analysis gaussian process regression |
| url | https://www.mdpi.com/1424-8220/25/5/1332 |
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