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• W3208714512 abstract "Accurate estimation of cable tension is crucial for the structural health monitoring of cable-supported structures. Identifying the cable's force from its vibration data is probably the most widely adopted method of cable tension estimation. According to string theory, the accuracy of estimated cable tension is highly related to identified modal parameters including natural frequencies and frequency order. To alleviate the factors that impact the accuracy of modal parameters when using the peak-picking method in wireless sensor networks, a fully automated and robust identifying method is proposed in this paper. This novel method was implemented on the Xnode wireless sensor system and validated with the data obtained from Jindo Bridge. The experiment results indicate that, through this method, the wireless sensor is able to distinguish the cognizable power spectrum, extract the peaks, eliminate false frequencies and determine frequency orders automatically to estimate cable tension force without any manual intervention or preprocessing. Meanwhile, the results of natural frequencies, corresponding orders and cable tension force obtained from the Xnode system show excellent agreement with the results obtained using the Matlab program method. This demonstrates the effectiveness and reliability of the Xnode estimation system. Furthermore, this method is also appropriate for other high-performance wireless sensor network systems to realize self-identification of cable in long-term monitoring." @default.
• W3208714512 created "2021-11-08" @default.
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• W3208714512 date "2021-10-30" @default.
• W3208714512 modified "2023-10-15" @default.
• W3208714512 title "Fully Automated and Robust Cable Tension Estimation of Wireless Sensor Networks System" @default.
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• W3208714512 doi "https://doi.org/10.3390/s21217229" @default.
• W3208714512 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8587797" @default.
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