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RISS 인기검색어
- 검색키워드
- 저자 : Zhanjun Wu (검색결과 4 건)
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Probabilistic-based damage identification based on error functions with an autofocusing feature
Rahim Gorgin,Yunlong Ma,Zhanjun Wu,Dongyue Gao,Yishou Wang 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.15 No.4
This study presents probabilistic-based damage identification technique for highlightingdamage in metallic structures. This technique utilizes distributed piezoelectric transducers to generate andmonitor the ultrasonic Lamb wave with narrowband frequency. Diagnostic signals were used to define thescatter signals of different paths. The energy of scatter signals till different times were calculated by takingroot mean square of the scatter signals. For each pair of parallel paths an error function based on the energyof scatter signals is introduced. The resultant error function then is used to estimate the probability of thepresence of damage in the monitoring area. The presented method with an autofocusing feature is applied toaluminum plates for method verification. The results identified using both simulation and experimentalLamb wave signals at different central frequencies agreed well with the actual situations, demonstrating thepotential of the presented algorithm for identification of damage in metallic structures. An obvious merit ofthe presented technique is that in addition to damages located inside the region between transducers; thosewho are outside this region can also be monitored without any interpretation of signals. This noveltyqualifies this method for online structural health monitoring.
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Probabilistic-based damage identification based on error functions with an autofocusing feature
Gorgin, Rahim,Ma, Yunlong,Wu, Zhanjun,Gao, Dongyue,Wang, Yishou Techno-Press 2015 Smart Structures and Systems, An International Jou Vol.15 No.4
This study presents probabilistic-based damage identification technique for highlighting damage in metallic structures. This technique utilizes distributed piezoelectric transducers to generate and monitor the ultrasonic Lamb wave with narrowband frequency. Diagnostic signals were used to define the scatter signals of different paths. The energy of scatter signals till different times were calculated by taking root mean square of the scatter signals. For each pair of parallel paths an error function based on the energy of scatter signals is introduced. The resultant error function then is used to estimate the probability of the presence of damage in the monitoring area. The presented method with an autofocusing feature is applied to aluminum plates for method verification. The results identified using both simulation and experimental Lamb wave signals at different central frequencies agreed well with the actual situations, demonstrating the potential of the presented algorithm for identification of damage in metallic structures. An obvious merit of the presented technique is that in addition to damages located inside the region between transducers; those who are outside this region can also be monitored without any interpretation of signals. This novelty qualifies this method for online structural health monitoring.
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Ting-Hua Yi,Dong-Hui Yang,Hai-Lun Gu,Zhanjun Wu 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.30 No.6
Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the <i>k</i>-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.
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