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Structural Damage Identification Based on Convolutional Neural Network Group Considering the Sensor Fault
This article proposes a structural damage identification method based on one-dimensional convolutional neural network group considering sensor faults. The method aims to reduce the damage misjudgment caused by sensor faults. In the proposed method, according to the sensor layout, some convolutional neural network sub-models are established to extract the features from raw vibration data for sensor fault diagnosis and structural damage identification; then two convolutional neural networks groups, namely the sensor fault diagnosis group and the damage identification group are designed on the basis of the functions of each sub-model. The sensor fault diagnosis group determines whether the sensor data is abnormal and truncates the abnormal signal. The remaining normal signal are entered into the damage identification group and the final damage identification results are calculated according to the statistical decision module. The effectiveness of the devised method is verified by the IASC-ASCE benchmark structure and laboratory experiments. The results demonstrate that the sensor fault diagnosis and damage identification accuracy of each sub-model ranges from 98.54% to 99.77% and from 87.21% to 91.74% respectively at different noise levels; the damage identification group can reduce the impact of sub-model misjudgment on the structural damage identification. The accuracy of the final damage identification results is 100%. The identification time of all samples in the test set is 53.09 s and 22.93 s, respectively, for SHM benchmark and Laboratory experiment cases. And the average judgment time of each submodel in the sensor fault diagnosis group was 278 and 94 ms, and that of each submodel in the damage identification group was 294 and 105 ms, respectively, for a single test sample, which fulfills the requirements of online damage identification for structural health monitoring.
Structural Damage Identification Based on Convolutional Neural Network Group Considering the Sensor Fault
This article proposes a structural damage identification method based on one-dimensional convolutional neural network group considering sensor faults. The method aims to reduce the damage misjudgment caused by sensor faults. In the proposed method, according to the sensor layout, some convolutional neural network sub-models are established to extract the features from raw vibration data for sensor fault diagnosis and structural damage identification; then two convolutional neural networks groups, namely the sensor fault diagnosis group and the damage identification group are designed on the basis of the functions of each sub-model. The sensor fault diagnosis group determines whether the sensor data is abnormal and truncates the abnormal signal. The remaining normal signal are entered into the damage identification group and the final damage identification results are calculated according to the statistical decision module. The effectiveness of the devised method is verified by the IASC-ASCE benchmark structure and laboratory experiments. The results demonstrate that the sensor fault diagnosis and damage identification accuracy of each sub-model ranges from 98.54% to 99.77% and from 87.21% to 91.74% respectively at different noise levels; the damage identification group can reduce the impact of sub-model misjudgment on the structural damage identification. The accuracy of the final damage identification results is 100%. The identification time of all samples in the test set is 53.09 s and 22.93 s, respectively, for SHM benchmark and Laboratory experiment cases. And the average judgment time of each submodel in the sensor fault diagnosis group was 278 and 94 ms, and that of each submodel in the damage identification group was 294 and 105 ms, respectively, for a single test sample, which fulfills the requirements of online damage identification for structural health monitoring.
Structural Damage Identification Based on Convolutional Neural Network Group Considering the Sensor Fault
KSCE J Civ Eng
Luo, Yongpeng (author) / Wang, Linkun (author) / Guo, Xu (author) / Zheng, Jinlin (author) / Liao, Feiyu (author) / Guo, Zixiong (author)
KSCE Journal of Civil Engineering ; 27 ; 3403-3417
2023-08-01
15 pages
Article (Journal)
Electronic Resource
English
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