Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Laboratory for Nondestructive Evaluation and Structural Health Monitoring Studies, Department of Civil and Environmental Engineering, University of Pittsburgh, 3700 O'Hara Street, 724 Benedum Hall, Pittsburgh, PA 15261, USA.
Sensors (Basel). 2022 Sep 30;22(19):7447. doi: 10.3390/s22197447.
This paper describes a non-invasive inspection technique for the estimation of longitudinal stress in continuous welded rails (CWR) to infer the rail neutral temperature (RNT), i.e., the temperature at which the net longitudinal force in the rail is zero. The technique is based on the use of finite element method (FEM), vibration measurements, and machine learning (ML). FEM is used to model the relationship between the boundary conditions and the longitudinal stress of any given CWR to the vibration characteristics (mode shapes and frequencies) of the rail. The results of the numerical analysis are used to train a ML algorithm that is then tested using field data obtained by an array of accelerometers polled on the track of interest. In the study presented in this article, the proposed technique was proven in the field during an experimental campaign conducted in Colorado. A commercial FEM software was used to model the rail track as a short rail segment repeated indefinitely and under varying boundary conditions and stress. Three datasets were prepared and fed to ML models developed using hyperparameter search optimization techniques and k-fold cross validation to infer the stress or the RNT. The frequencies of vibration were extracted from the time waveforms obtained from two accelerometers temporarily attached to the rail. The results of the experiments demonstrated that the success of the technique is dependent on the accuracy of the model and the ability to properly identify the modeshapes. The results also proved that the ML was also able to predict successfully the neutral temperature of the tested rail by using only a limited number of experimental data for the training.
本文介绍了一种非侵入式检测技术，用于估算连续焊接钢轨（CWR）中的纵向应力，以推断钢轨中性温度（RNT），即钢轨中净纵向力为零的温度。该技术基于使用有限元方法（FEM）、振动测量和机器学习（ML）。FEM 用于建模任何给定 CWR 的边界条件与纵向应力之间的关系，以及钢轨的振动特性（模态形状和频率）。数值分析的结果用于训练机器学习算法，然后使用在感兴趣的轨道上布置的加速度计数组获得的现场数据进行测试。在本文介绍的研究中，该技术在科罗拉多州进行的实验活动中得到了现场验证。使用商业 FEM 软件将轨道模型模拟为无限重复的短轨段，并在变化的边界条件和应力下进行建模。准备了三个数据集，并将其馈送到使用超参数搜索优化技术和 k 折交叉验证开发的 ML 模型中，以推断应力或 RNT。振动频率是从暂时附着在钢轨上的两个加速度计获得的时间波形中提取的。实验结果表明，该技术的成功取决于模型的准确性和正确识别模态形状的能力。结果还证明，ML 仅使用有限数量的实验数据进行训练，也能够成功预测测试钢轨的中性温度。
