Reconstruction to Sensor Measurements Based on a Correlation Model of Monitoring Data
AbstractA sensor failure will lead to sensor measurement distortion, and may reduce the reliability of the whole structure analysis. This paper studies the method of monitoring information reconstruction based on the correlation degree. For the faulty sensor, the correlation degree of the normal response of this sensor and the measurements of the other sensors is calculated, which is also called the correlation degree of reconstructed variables and response variables. By comparing the correlation degrees, the response variables, which are needed to establish the correlation model, are determined. The correlation model between the reconstructed variables and the response variables is established by the partial least square method. The value of the correlation degrees between the reconstructed variables and the response variables, the amount of the monitoring data which is used to determine the coefficients of the correlation model, and the number of the response variables are used to discuss the influence factors of the reconstruction error. The stress measurements of structural health monitoring system of Shenzhen Bay Stadium is taken as an example, and the effectiveness of the method is verified and the practicability of the method is illustrated. View Full-Text
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Lu, W.; Teng, J.; Li, C.; Cui, Y. Reconstruction to Sensor Measurements Based on a Correlation Model of Monitoring Data. Appl. Sci. 2017, 7, 243.
Lu W, Teng J, Li C, Cui Y. Reconstruction to Sensor Measurements Based on a Correlation Model of Monitoring Data. Applied Sciences. 2017; 7(3):243.Chicago/Turabian Style
Lu, Wei; Teng, Jun; Li, Chao; Cui, Yan. 2017. "Reconstruction to Sensor Measurements Based on a Correlation Model of Monitoring Data." Appl. Sci. 7, no. 3: 243.
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