Incremental Bayesian tensor learning for structural monitoring data imputation and response forecasting

• URL: http://arxiv.org/abs/2007.00790v3
• Date: Fri, 17 Jul 2020 01:12:38 GMT
• Title: Incremental Bayesian tensor learning for structural monitoring data imputation and response forecasting
• Authors: Pu Ren and Xinyu Chen and Lijun Sun and Hao Sun
• Abstract summary: This paper presents an Bayesian tensor learning method for reconstruction of missing sensor data intemporal and structural response. The performance of the proposed approach is validated on continuous field-sensing data of a concrete bridge. The results indicate that the proposed tensor learning approach is accurate and robust even in the presence of large rates of random missing, structured missing and their combination.
• Score: 18.919194955756396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There has been increased interest in missing sensor data imputation, which is ubiquitous in the field of structural health monitoring (SHM) due to discontinuous sensing caused by sensor malfunction. To address this fundamental issue, this paper presents an incremental Bayesian tensor learning method for reconstruction of spatiotemporal missing data in SHM and forecasting of structural response. In particular, a spatiotemporal tensor is first constructed followed by Bayesian tensor factorization that extracts latent features for missing data imputation. To enable structural response forecasting based on incomplete sensing data, the tensor decomposition is further integrated with vector autoregression in an incremental learning scheme. The performance of the proposed approach is validated on continuous field-sensing data (including strain and temperature records) of a concrete bridge, based on the assumption that strain time histories are highly correlated to temperature recordings. The results indicate that the proposed probabilistic tensor learning approach is accurate and robust even in the presence of large rates of random missing, structured missing and their combination. The effect of rank selection on the imputation and prediction performance is also investigated. The results show that a better estimation accuracy can be achieved with a higher rank for random missing whereas a lower rank for structured missing.

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