Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence

Abstract

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.

Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science

“…This means that a certain tested section of the track is considered to have reached or exceeded the alert level, with an uncertainty of 1%. The value of 1% is also reported by Silva et al [24] and Mosleh et al [70]. The CB, in this case, is the mean of the estimated features, plus and minus the variance (standard deviation) of these estimated features.…”

“…The Mahalanobis distance is a metric distance or similarity measure, used in machine learning to distinguish structural conditions. It is also understood as a fusion of multivariate data into a single DI, as referred to in Silva et al [24], and Meixedo et al [25]. The DI calculates the distance (i.e., dissimilarity or difference) between the damage and the baseline scenarios to state how severe each tested track section is compared to the normal condition.…”

“…Huang et al [23] developed an algorithm based on genetic evaluation theory for damage detection, considering the effects of temperature variation and vibration measurements, with acceptable accuracy. To detect unbalanced loads on a freight railway vehicle, and guarantee the comfort and safety of goods, Silva et al [24] implemented an artificial intelligence algorithm, which recorded vibrations data using a virtual wayside monitoring system. For the extraction of the most sensitive features for damage detection and feature dimensionality reduction, Silva et al [24] considered (linear) autoregressive models with exogenous input (ARX) for feature extraction from accelerations and strain dynamic responses.…”

“…To detect unbalanced loads on a freight railway vehicle, and guarantee the comfort and safety of goods, Silva et al [24] implemented an artificial intelligence algorithm, which recorded vibrations data using a virtual wayside monitoring system. For the extraction of the most sensitive features for damage detection and feature dimensionality reduction, Silva et al [24] considered (linear) autoregressive models with exogenous input (ARX) for feature extraction from accelerations and strain dynamic responses. The Mahalanobis distance was considered for data fusion and features discrimination, and outlier analysis was used to distinguish in which scenarios the vertical and lateral vehicle loads were not balanced.…”

Wheel Out-of-Roundness Detection Using an Envelope Spectrum Analysis

“…There are several research studies regarding the development of wheel condition monitoring technologies [ 31 , 32 , 49 , 50 ]; however, to the knowledge of the authors, detecting defective wheels with envelope spectrum analysis has been limited so far. In this research study, an envelope spectrum analysis is utilized to distinguish a defective wheel from a healthy one.…”