Probability of instant rail break induced by wheel–rail impact loading using field test data

The probability of an instant rail break, initiated at a single pre-existing rail foot crack due to a severe wheel impact loading, is predicted using statistical methods and a time-domain model for the simulation of dynamic vehicle–track interaction. A linear elastic fracture mechanics approach is employed to calculate the stress intensity at the crack in a continuously welded rail subjected to combined bending and temperature loading. Based on long-term field measurements in a wayside wheel load detector, a three-parameter probability distribution of the dynamic wheel load is determined. For a faster numerical assessment of the probability of failure, a thin plate spline regression is implemented to develop a meta-model of the performance function quantifying the stress intensity at the crack. The methodology is demonstrated by investigating the influence of initial crack length, fracture toughness and rail temperature difference on the risk for an instant rail break

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Vibration-Based Machine Learning Models for Condition Monitoring of Railroad Rolling Stock

One of the primary causes of rail rolling stock derailments is attributed to bearing and wheel axle failures. The health of train bearings is primarily monitored at target locations through wayside detection systems. This practice is susceptible to bearing failure and potential derailments at points in between these wayside systems. To remedy this, the University Transportation Center for Railway Safety (UTCRS) has developed a wireless onboard monitoring system that can continuously monitor the vibration response, which directly correlates to the health of bearings. This data is used to train regression-based machine learning algorithms and long-term prediction neural networks to predict bearing health. The models are intended to work in tandem with the onboard monitoring sensors as a means of two-way practical validation. The models tested were the Gradient Boosting Machine architecture for scheduled predictions and the Informer neural network architecture for long-term predictions of ongoing routes. The dataset for these models comes from the expansive laboratory record data available at the UTCRS. Ultimately, these machine learning algorithms will enhance freight railcars safety and save rail companies money by allowing for predictive maintenance practices

WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)

Track loading limits and cross-acceptance of vehicle approvals

The requirements for track loading limits are one of the main barriers to simple cross-acceptance of vehicles where rolling stock that is already operating successfully in one (or more) networks has to be retested before it can be approved for operation on another network. DynoTRAIN Work Package 4 studied this area in order to determine whether the additional requirements were justified, or if the process could be made much cheaper and simpler without increasing the risk of track deterioration for the networks. The review of national requirements identified modified criteria and limit values for track forces in some member states; however, these can be obtained from additional analysis of the normal test results with no new tests required. The influence of design rail inclination has also been found not to be significant, provided a realistic range of wheel–rail contact conditions are included in the tests. For line speeds greater than or equal to 160 km/h, the current standards for track construction across the member states appear to be similar. On lower speed lines in some countries, a ‘weaker’ track condition may require a lower limit on one of the vehicle assessment parameters. Track dynamics modelling has shown that the vehicle assessment parameters used in international standards are suitable for use in cross-acceptance for track forces. The use of multiple regression analysis allows the estimated maximum value for relevant parameters to be evaluated for different target conditions and then compared with the appropriate limit value, or with values for existing, comparable vehicles. Guidance has also been provided on the relevant parameters to consider when developing operating controls for different types of track deterioration

Predicting the Remaining Service Life of Railroad Bearings: Leveraging Machine Learning and Onboard Sensor Data

By continuously monitoring train bearing health in terms of temperature and vibration levels of bearings tested in a laboratory setting, statistical regression models have been developed to establish relationships between the sensor-acquired bearing health data with several explanatory factors that potentially influence the bearing deterioration. Despite their merits, statistical models fall short of reliable prediction accuracy levels since they entail restrictive assumptions, such as a priori known functional relationship between the response and input variables. A data-driven machine learning algorithm is presented, which can unravel the nonlinear deterioration model purely based on the bearing health data, even when the structure is not apparent. More specifically, a Gradient Boosting Machine is trained using vast amounts of laboratory data collected over the course of over a decade. This will help predict bearing failure, thus, providing railroads and railcar owners the opportunity to schedule preventive maintenance cycles rather than costly reactive ones

Track-Bridge Interaction Effects in Heavy Haul Railway Viaducts

When continuously welded rails are placed over a bridge, the track and the bridge interact via the ballast in the case of ballasted track or track slab in the case of non-ballasted track. This interaction, commonly referred to as track-bridge interaction results in force transfer between the track and the bridge. With the demand to increase freight haulage on heavy haul railway lines intensifying to meet mineral export demands, there is a need to understand the manifestation of rail bridge interactions in heavy haul railway bridges. Understanding the manifestation of these forces is critical for the management of the infrastructure during operation. Whilst track-bridge interactions effects design limits in high-speed rail have been documented, to the author's knowledge there has been no documented report that addresses track bridge interactions in the design of new heavy haul railway bridges and the management old heavy haul railway bridges. Resultantly, this study explored the observed patterns of rail forces, longitudinal deck displacements, ambient temperature, concrete temperature and rail temperature on the Olifants River Railway Bridge. Thereafter, the observed patterns were used investigate the effect of rail temperature variation on rail forces and the longitudinal displacement of the deck. Examine the effect of variation in concrete temperature on the longitudinal deck displacement, rail forces and variation in rail temperature as well as the effect of longitudinal deck displacement on rail forces. The effects of the presence of a train on the longitudinal displacement of the deck, rail forces and concrete temperature will also be investigated. Finally, this study developed a predictive multiple linear regression model that will assist in the management and maintenance heavy haul railway bridges. This study demonstrated that rail temperature variation is inversely proportion to the rail forces in the rail, longitudinal deck displacement is directly proportional to concrete temperature variation and that longitudinal deck displacement of the bridge deck and rail forces in the rail are inversely proportional. However, the correlation between the longitudinal deck displacement and the rail temperature, rail temperature in the track and concrete temperature in the deck, concrete temperature in the deck and the rail forces in the track could not be established conclusively. The effect of the presence of the train on the longitudinal displacement of the deck, rail forces and concrete temperature could also not be established conclusivel