Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter
As sensor monitoring technology continues to evolve, structural online monitoring and health management have found numerous applications across various fields. However, challenges remain concerning the real-time diagnosis of structural damage and the accuracy of dynamic reliability predictions. In t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7582 |
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| author | Yan Zhang Yongbo Zhang Jinhui Yu Fei Zhao Shihao Zhu |
| author_facet | Yan Zhang Yongbo Zhang Jinhui Yu Fei Zhao Shihao Zhu |
| author_sort | Yan Zhang |
| collection | DOAJ |
| description | As sensor monitoring technology continues to evolve, structural online monitoring and health management have found numerous applications across various fields. However, challenges remain concerning the real-time diagnosis of structural damage and the accuracy of dynamic reliability predictions. In this paper, a structural online damage identification and dynamic reliability prediction method based on Unscented Kalman Filter (UKF) is presented. Specifically, in the Wiener degradation process with random effects on structural performance, the structural damage identification is initially realized using UKF. Following that, the EM algorithm is employed for estimating the performance model parameters. Eventually, dynamic reliability prediction is realized based on conditional probability. The simulation results indicate that the method effectively estimates the damage state during the structure’s use while providing accurate, real-time, and dynamic reliability predictions for the system. |
| format | Article |
| id | doaj-art-ea94b04c4dfc409282b17f8273f11414 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ea94b04c4dfc409282b17f8273f114142025-08-20T02:50:40ZengMDPI AGSensors1424-82202024-11-012423758210.3390/s24237582Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman FilterYan Zhang0Yongbo Zhang1Jinhui Yu2Fei Zhao3Shihao Zhu4School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaAs sensor monitoring technology continues to evolve, structural online monitoring and health management have found numerous applications across various fields. However, challenges remain concerning the real-time diagnosis of structural damage and the accuracy of dynamic reliability predictions. In this paper, a structural online damage identification and dynamic reliability prediction method based on Unscented Kalman Filter (UKF) is presented. Specifically, in the Wiener degradation process with random effects on structural performance, the structural damage identification is initially realized using UKF. Following that, the EM algorithm is employed for estimating the performance model parameters. Eventually, dynamic reliability prediction is realized based on conditional probability. The simulation results indicate that the method effectively estimates the damage state during the structure’s use while providing accurate, real-time, and dynamic reliability predictions for the system.https://www.mdpi.com/1424-8220/24/23/7582Unscented Kalman Filterstructural damage identificationdynamic reliability predictionperformance degradation process |
| spellingShingle | Yan Zhang Yongbo Zhang Jinhui Yu Fei Zhao Shihao Zhu Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter Sensors Unscented Kalman Filter structural damage identification dynamic reliability prediction performance degradation process |
| title | Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter |
| title_full | Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter |
| title_fullStr | Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter |
| title_full_unstemmed | Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter |
| title_short | Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter |
| title_sort | structural online damage identification and dynamic reliability prediction method based on unscented kalman filter |
| topic | Unscented Kalman Filter structural damage identification dynamic reliability prediction performance degradation process |
| url | https://www.mdpi.com/1424-8220/24/23/7582 |
| work_keys_str_mv | AT yanzhang structuralonlinedamageidentificationanddynamicreliabilitypredictionmethodbasedonunscentedkalmanfilter AT yongbozhang structuralonlinedamageidentificationanddynamicreliabilitypredictionmethodbasedonunscentedkalmanfilter AT jinhuiyu structuralonlinedamageidentificationanddynamicreliabilitypredictionmethodbasedonunscentedkalmanfilter AT feizhao structuralonlinedamageidentificationanddynamicreliabilitypredictionmethodbasedonunscentedkalmanfilter AT shihaozhu structuralonlinedamageidentificationanddynamicreliabilitypredictionmethodbasedonunscentedkalmanfilter |