A missing sensor measurement data reconstruction framework powered by multi-task Gaussian process regression for dam structural health monitoring systems

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.