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An active learning-driven optimal sensor placement method considering sensor position distribution toward structural health monitoring
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An active learning-driven optimal sensor placement method considering sensor position distribution toward structural health monitoring
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Journal Article
An active learning-driven optimal sensor placement method considering sensor position distribution toward structural health monitoring
2024
Overview
Optimal sensor placement (OSP) is one of the essential factors affecting the accuracy of health management, particularly in health monitoring driven by mode information. A novel OSP method based on active learning is proposed to effectively capture modal shapes for Structural Health Monitoring (SHM). First, the optimal Latin Hypercube Sampling is carried out to generate initial sensor positions, and the corresponding amplitudes of modal shapes at these positions are obtained by a frequency response function. Subsequently, data-driven models are built to be treated as virtual sensors to reconstruct the integrated modal shapes of the structure, and the accuracies of the results are calculated. Then, considering the distribution of the input sensor position, an improved reliability-based expectation improvement function (IREIF2) is applied to find the optimal sensor positions by optimizing the parameters of the probability density function in IREIF2. Finally, the position and response of the optimal sensor are used to update the data-driven models for more accurate modal shape reconstruction, and the accuracies are calculated to determine whether the OSP process continues. Once the accuracies meet the desired criteria, the optimal sensor positions are also obtained. The superiority of the proposed method is verified by the comparisons with other OSP methods, and different case studies are also used to prove the proposed method can realize OSP for SHM.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
