Adam Bevan

This study investigated the influence of rail steel metallurgy, chemical composition, and mechanical properties on wear and rolling contact fatigue (RCF) performance. Five commercially relevant rail steels were evaluated, including the as-rolled carbon-manganese pearlitic grades R200 and R260, the heat-treated grades R350HT and R350LHT, and the as-rolled hypereutectoid grade HP335. Each steel was characterised through comprehensive metallurgical, mechanical, and compositional analyses, and their wear and RCF performance was assessed using controlled twin-disc experiments conducted under identical test conditions.,Heat-treated steel grades achieved wear rates as low as 1.0 μm³/cycle and fatigue lives of approximately 60,000 cycles, compared with 20.3 μm³/cycle and 10,000 cycles for the softer R200 grade, and 6.4 μm³/cycle and 30,000 cycles for R260. Despite an as-rolled steel, HP335 achieved a fatigue life of approximately 60,000 cycles, comparable to the heat-treated grades, while maintaining a similarly low wear rate of 1.6 μm³/cycle.,Regression analysis identified mechanical strength as the dominant factor governing both wear and RCF resistance. Hardness, ultimate tensile strength, fracture stress, ferrite lamellar thickness, and cementite lamellar thickness all exhibited very strong correlations with wear rate (R² = 0.99), while ultimate tensile strength (R² = 0.99), hardness (R² = 0.97), fracture stress (R² = 0.97), and proof stress (R² = 0.93) showed the strongest relationships with cycles to RCF initiation. Finer pearlitic microstructures were also strongly associated with improved performance, with interlamellar spacing demonstrating correlations of R² = 0.95 for wear resistance and R² = 0.72 for RCF resistance.,The findings establish practical design principles for future rail steel development, demonstrating that increased mechanical strength, refined pearlitic microstructures, and higher cementite fractions offer the greatest potential for improving rail durability and service life.

A condition-based monitoring (CBM) system provides the possibility for the railway industry to guarantee reliability by executing prompt and low-cost maintenance. In this study, a simple model-based condition monitoring strategy for the railway vehicle suspension system is demonstrated. The method is based on a recursive least-square (RLS) algorithm regarding a deterministic parametric model. The fault detection approach for the locomotive suspension system is illustrated with three diagnostic modules. Multi-body simulation data are employed to validate the feasibility of this CBM strategy. The designed diagnostic model reveals that the suspension parameter estimates are consistent with the reference values. The corresponding demonstrator provides evidence that the monitoring system has potential applications and is suitable for further development.

Diesel engine failures are one reason for delays and breakdowns on the UK rail network, resulting in significant fines and related financial penalties for a train operating company. Preventing such failures is the ultimate goal, but forecasting or predicting future failures before they occur would be highly desirable. In this study, we take real world Diesel Multiple Unit sensor data, recorded in the form of event data, and repurpose it for the remote condition monitoring of critical diesel engine operations. A methodology based on windowing of data is proposed that demonstrates the effective processing of event data for predictive modelling. This study specifically looks at predicting engine failures, and through this methodology, models trained on the processed data resulted in accuracies of 88%. Explainable AI methods are then utilised to provide feature importance explanations for the model’s performance. This information helps the end user understand specifically which sensor data from the larger dataset is most relevant for predicting engine failures. The work presented is useful to the railway industry, but more specifically to train operator companies who ideally want to foresee failures before they occur to avoid significant financial costs. The methodology proposed is applicable for the predictive maintenance of many systems, not just railway diesel engines.

The railway industry has focused on the improvement of maintenance through the use of novel technologies. Recently, the utilisation of repair welding to restore the worn area on wheels has been investigated, as it can bring significant savings in wheelset maintenance. Under an Innovate UK AURORA project, a worn wheel that previously operated on London Underground (LUL) was restored using this process. To test its performance and compare with a new standard steel grade wheel, the HAROLD full scale test rig was used in which an LUL vehicle bogie equipped with both the restored and R9 grade wheels was operated under the representative lateral and yaw displacements computed from vehicle dynamics simulations. The wear measurements carried out at the end of test cycles showed that although the restored wheel suffered from initial higher wear, the levels reduced and became similar to the R9 grade wheel. Furthermore, the full-scale testing provided an opportunity to validate the wear model predictions which were conducted using the vehicle dynamics simulations utilised in testing inputs. It was found that while the flange wear predictions were higher, the tread wear estimations were smaller than the measurements on the R9 grade wheel.

A cab-based track monitoring system has been developed which makes use of the existing on-board GSM-R cab radio present in the majority of trains operating in the UK. With the addition of a low-cost sensor, type, location and severity of the track defects are reported using the system. The system improves safety and network performance by efficiently directing maintenance crews to the location of defects, minimising time spent on maintenance and inspection. Initially, vehicle dynamic simulation was used to test the feasibility of the system for defect monitoring and to develop compensation factors for vehicle type and operating speed. Novel on-board signal processing techniques are also presented through comparison of vibration response from sites with known defects and outputs from Network Rail’s (NR) New Measurement Train (NMT). Good agreement was reported for track faults in relation to vertical and lateral alignment and dip faults. Statistically, good agreement has been demonstrated, suggesting that the data acquired could be used to provide an indication of track quality thereby improving network performance, reducing rough ride and leading to improved passenger comfort. Improvements in the measured and statistical correlation are anticipated through the use, of multi-train / multi-journey and machine learning methods..

London Underground is facing the challenge of increasing timetables against spending cuts across renewals and maintenance in all assets. In order to meet this challenge, it is reviewing all maintenance practices to make sure that they are appropriate for the current asset conditions. Management of the wheel–rail interface is critical to maximising the life of wheels and rails through preventative maintenance regimes that ensure all activities offer value for money and safe operation. Detailed monitoring of the asset condition using novel non-destructive techniques has allowed the identification of the problems which currently occur at the wheel–rail interface on the London Underground network. These problems are discussed in this paper along with some of the solutions proposed to manage them. Site observations from a range of rail rolling contact fatigue monitoring sites have also been compared to the outputs from vehicle dynamic simulations. These outputs were post-processed using a circle plotting technique, which illustrates the location, direction and severity of the forces, and the Whole Life Rail Model to predict the susceptibility to rail damage for two rail steel grades. The outputs from these comparisons have helped to illustrate the wheel–rail contact conditions and forces which are driving the observed damage and potential future enhancements to improve the accuracy of the models for predicting the observed rolling contact fatigue damage.

The rate of rail degradation and hence its expected life is not uniform throughout any railway network and is governed by a combination of track, traffic and operating characteristics in addition to the metallurgical attributes of the rail steel. Consequently, it is suggested that any route or network is not a single linear asset but is a compilation of individual segments with different track characteristics, degradation rates and expected life spans. Thus, the choice of rail steel grade to maximise life (and minimise life-cycle costs) needs to combine knowledge of the metallurgical attributes of the available rail steels with the conditions prevailing at the wheel–rail and vehicle–track interfaces, whilst also considering the economic costs and benefits of the different options. This paper focuses on the classification of the susceptibility to rail degradation in various parts of a mixed-traffic network using vehicle dynamics simulation. The metallurgical attributes of the currently available rail steels are summarised along with an assessment of the life-cycle costs and wider economic implications associated with selection of a rail steel which provides improved resistance to the key degradation mechanisms of rolling contact fatigue and wear. Overall, the proposed methodology, which incorporates engineering, metallurgical and economic assessments, provides guidance on the circumstances in which the introduction of alternative rail steels make sense (or not) from an economic perspective.