This article presents an efficient methodology for the calibration of a numerical model of a Sgnss freight railway wagon based on experimental modal parameters, namely natural frequencies and mode shapes. Dynamic tests were performed for two distinct static loading configurations, tare weight and current operational overload, under demanding test conditions, particularly during an unloading operation of the train and without disturbing its tight operational schedule. These conditions impose restrictions to the tests, especially regarding the test duration, sensor positioning and system excitation. The experimental setups involve the use of several high-sensitivity accelerometers strategically distributed along with the vehicle platform and bogies in the vertical direction. The modal identification was performed with the application of the enhanced frequency-domain decomposition (EFDD) method, allowing the estimation of 10 natural frequencies and mode shapes associated with structural movements of the wagon platform, which in some cases are coupled with rigid body movements. A detailed 3D FE model of the freight wagon was developed including the platform, bogies, wheelsets, primary suspensions and wheel–rail interface. The model calibration was performed sequentially, first with the unloaded wagon model and then with the loaded wagon model, resorting to an iterative method based on a genetic algorithm. The calibration process allowed the obtainment of the optimal values of eight numerical parameters, including a double estimation of the vertical stiffness of the primary suspensions under the unloaded and loaded static configurations. The results demonstrate that the primary suspensions present an elastic/almost elastic behaviour. The comparison of experimental and numerical responses before and after calibration revealed significant improvements in the numerical models and a very good correlation between the experimental and numerical responses after calibration.

This research presents an approach based on artificial intelligence techniques for wheel polygonization detection. The proposed methodology is tested with dynamic responses induced on the track by passing a Laagrss-type rail vehicle. The dynamic response is attained considering the application of a train-track interaction model that simulates the passage of the train over a set of accelerometers installed on the rail and sleepers. This study, which considers an unsupervised methodology, aims to compare the performance of two feature extraction techniques, namely the Autoregressive Exogenous (ARX) model and Continuous Wavelets Transform (CWT). The extracted features are then submitted to data normalization considering the Principal Component Analysis (PCA) applied to suppress environmental and operational effects. Next to data normalization, data fusion using Mahalanobis distance is performed to enhance the sensitivity to the recognition of defective wheels. Finally, an outlier analysis is employed to distinguish a healthy wheel from a defective one. Moreover, sensitivity analysis is performed to analyze the influence of the number of sensors and their location on the accuracy of the wheel defect detection system.

The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with high financial costs. This study aims to develop a methodology capable of automatically identifying unbalanced vertical loads considering the limits proposed by the reference guidelines. The research relies on a 3D numerical simulation of the train–track dynamic response to the presence of longitudinal and transverse scenarios of unbalanced vertical loads and resorting to a virtual wayside monitoring system. This methodology is based on measured data from accelerometers and strain gauges installed on the rail and involves the following steps: (i) feature extraction, (ii) features normalization based on a latent variable method, (iii) data fusion, and (iv) feature discrimination based on an outlier and a cluster analysis. Regarding feature extraction, the performance of ARX and PCA models is compared. The results prove that the methodology is able to accurately detect and classify longitudinal and transverse unbalanced loads with a reduced number of sensors.