The sensor-based structural health monitoring (SHM) systems are widely embedded in the new-constructed and rehabilitated dam. Due to the harsh working environment, poor installation, and sampling error, sensor fault often inevitably occurs. In this paper, rather than using conventional Gaussian process regression(GPR) to reconstruct missing data from multiple sensors independently, we propose a multi-task GPR (mGPR) paradigm for capturing the correlation among various sensors to reconstruct missing data from faulty sensors as a whole. In this framework, for a particular sensor, the missing data is reconstructed by the approach which not only learns other known data from this sensor but also learns the whole known measurements from other sensors. The proposed paradigm is quite beneficial for dam SHM systems since the missing data from the faulty sensor(s) can be efficiently and accurately learned by the whole historical data including both faulty and normal sensors. The usefulness of the proposed paradigm is demonstrated through three measurement items including air temperature, dam displacements, and crack opening displacements collected from two dams in long-term service. We investigate two missing data scenarios with distinct positions in sensors. The experimental results show our proposed mGPR has significantly better performance than conventional multiple GPR for all the tested measurement items, especially in the scenarios that the missing part occurs at the beginning or the end of the dataset. It is also shown the multi-task learning paradigm powered by mGPR is considerable to address missing data reconstruction for dam SHM systems.

基于传感器的结构健康监测 (SHM) 系统广泛嵌入新建和修复的大坝中。由于工作环境恶劣、安装不良、采样误差等原因，传感器故障往往不可避免。在本文中，我们不是使用传统的高斯过程回归 (GPR) 来独立地重建来自多个传感器的缺失数据，而是提出了一种多任务 GPR (mGPR) 范式，用于捕获各种传感器之间的相关性以重建来自故障传感器的缺失数据作为所有的。在此框架中，对于特定传感器，通过不仅从该传感器学习其他已知数据而且从其他传感器学习全部已知测量值的方法来重建丢失的数据。所提出的范例对大坝 SHM 系统非常有益，因为来自故障传感器的缺失数据可以通过包括故障传感器和正常传感器在内的整个历史数据有效而准确地学习。通过从长期服务的两座大坝收集的三个测量项目，包括气温、大坝位移和裂缝张开位移，证明了所提出的范式的有用性。我们调查了两个在传感器中具有不同位置的缺失数据场景。实验结果表明，对于所有测试的测量项目，我们提出的 mGPR 的性能明显优于传统的多重 GPR，尤其是在缺失部分发生在数据集的开头或结尾的情况下。