Wang Qi-Ang, Ni Yi-Qing
State Key Laboratory for Geomechanics and Deep Underground Engineering & School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221008, China.
National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
Sensors (Basel). 2019 Jul 27;19(15):3311. doi: 10.3390/s19153311.
Uncertainty in sensor data complicates the construction of baseline models for the measurement and forecasting (M&F) of high-speed rail (HSR) track slab deformation. Standard Gaussian process (GP) assumes a uniform noise throughout the input space. However, in the application to modelling of HSR structural health monitoring (SHM) data, this assumption can be unrealistic, because of its unique heteroscedastic uncertainty that is induced by dynamic train loading, electromagnetic interference, large temperature variation, and daily maintenance actions of railway track infrastructure. Therefore, this study firstly develops a novel online SHM system enabled by fiber Bragg grating (FBG) technology to eliminate electromagnetic interference on SHM data for continuous and long-term monitoring of track slab deformation, with the capacity of temperature self-compensation. To deal with different sources of uncertainty, the study explores Variational Heteroscedastic Gaussian Process (VHGP) approach while using variational Bayesian and Gaussian approximation for data modelling, estimation of the monitoring data uncertainty, and further data forecasting. The results demonstrate that the VHGP framework yields more robust regression results and the estimated confidence level can better depict the heteroscedastic variances of the noise in HSR data. Higher accuracy for both regression and forecasting is gained through VHGP and the position with maximum noise can be more accurately forecasted with a smooth varying confidence interval. Based on in-situ measurement data, the uncertainty levels for all sensors are estimated together with corresponding deformation profiles for the instrumented segment and three typical types of uncertainty are summarized during the M&F process of HSR track slab deformation.
传感器数据中的不确定性使得构建高速铁路（HSR）轨道板变形测量与预测（M&F）的基线模型变得复杂。标准高斯过程（GP）假设在整个输入空间中噪声是均匀的。然而，在应用于高速铁路结构健康监测（SHM）数据建模时，这一假设可能不现实，因为其存在由动态列车荷载、电磁干扰、大温度变化以及铁路轨道基础设施的日常维护活动所引发的独特异方差不确定性。因此，本研究首先开发了一种基于光纤布拉格光栅（FBG）技术的新型在线SHM系统，以消除SHM数据上的电磁干扰，用于轨道板变形的连续长期监测，并具备温度自补偿能力。为了应对不同的不确定性来源，该研究探索了变分异方差高斯过程（VHGP）方法，同时使用变分贝叶斯和高斯近似进行数据建模、监测数据不确定性估计以及进一步的数据预测。结果表明，VHGP框架产生了更稳健的回归结果，并且估计的置信水平能够更好地描述高速铁路数据中噪声的异方差。通过VHGP在回归和预测方面都获得了更高的精度，并且可以通过平滑变化的置信区间更准确地预测最大噪声位置。基于现场测量数据，在高速铁路轨道板变形的测量与预测过程中，一起估计了所有传感器的不确定性水平以及仪器化路段的相应变形剖面，并总结了三种典型的不确定性类型。
