标题:贝叶斯张量学习用于结构监测数据的插补和响应预测

摘要】由于缺少由传感器故障引起的不连续感测,缺失的传感器数据归因引起了人们的越来越多的兴趣,在结构健康监测(SHM)领域中普遍存在这种缺失。为了解决这个基本问题,本文提出了一种贝叶斯张量学习方法,用于重建SHM中的时空缺失数据并预测结构响应。特别是,首先构建时空张量,然后进行贝叶斯张量分解,以提取潜在特征以缺失数据。为了基于不完整的传感数据进行结构响应预测,将张量分解与矢量自回归进一步集成。基于应变时间历史与温度记录高度相关的假设,该方法的性能在混凝土桥梁的连续现场传感数据(包括应变和温度记录)上得到了验证。结果表明,即使存在大量随机缺失,结构性缺失及其组合的情况下,所提出的概率张量学习方法也是准确而稳健的。还研究了等级选择对插补和预测性能的影响。结果表明,较高的随机缺失等级可以实现更好的估计精度,而对于结构化缺失则等级更低。

Title: Bayesian tensor learning for structural monitoring data imputation and response forecasting

[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 a 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. 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.

【作者】Pu Ren, Hao Sun

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