Abstract:Contact and friction between wheel and rail during train operation is the main cause of the rolling noise for which railways are known. Therefore, it is necessary to accurately measure the surface roughness of wheels and rails to monitor railway noise and predict noise around tracks. Conventional systems developed to measure surface roughness have large deviations in measured values or low repeatability. The recently developed automatic mobile measurement platform known as Auto Rail Checker (ARCer) uses three … Show more
“…The treated blade has an RMS roughness of ∼850 nm (measured with an optical profilometer), close to the railhead’s typical roughness of 1μm. 19 Figure 1.Schematic of the laboratory apparatus. The wheel represents the train wheel, and the blade represents the rail.…”
Section: Methodsmentioning
confidence: 94%
“…The treated blade has an RMS roughness of ∼850 nm (measured with an optical profilometer), close to the railhead's typical roughness of 1 μm. 19 The wheel design was based on the work conducted by Naeimi et al, 20 where they showed that a 1/5-scale train wheel under scaled loading could produce a contact pressure comparable to that at the wheel-rail interface. The wheel diameter is 0.2 m (roughly 1/5 of the actual wheel diameter).…”
A small-scale laboratory apparatus was built to study liquid friction modifier (LFM) behavior in a top-of-rail application. A field experiment was also carried out to complement the laboratory findings. KELTRACK® (a water-based LFM) was used as the test fluid. Laser-induced fluorescence served to measure the LFM thickness left on the track after the passage of the wheel. The lab experiments show that the LFM cannot withstand the high wheel-rail contact pressure in the nip present in cargo rail situations. As a result, the liquid is squeezed out laterally and attaches to the edges of the wheel contact band. This “edge liquid” is then carried down the track on the wheel. Gravimetric measurements of the wheel contact band confirm this observation, and show that only a minute amount of liquid is carried through the nip in the valleys between surface roughness features. In the field experiment, the LFM is applied from a trackside unit on a tangent section of the track. About 500 m downstream of the application point, the track has a curved section. LFM cannot be detected anywhere on the track a short distance (∼200 m) past the application unit. However, LFM is detectable on the curved sections of the track up to approximately 2 km from the application unit. This LFM on the curved track is believed to be due to transfer of the “edge liquid” from the wheel to the track, caused by the movement of the contact band as the train rounds a curve. The presence of LFM on the curved track far downstream is consistent with prior measurements of reduced lateral force on the curved track downstream of LFM application sites.
“…The treated blade has an RMS roughness of ∼850 nm (measured with an optical profilometer), close to the railhead’s typical roughness of 1μm. 19 Figure 1.Schematic of the laboratory apparatus. The wheel represents the train wheel, and the blade represents the rail.…”
Section: Methodsmentioning
confidence: 94%
“…The treated blade has an RMS roughness of ∼850 nm (measured with an optical profilometer), close to the railhead's typical roughness of 1 μm. 19 The wheel design was based on the work conducted by Naeimi et al, 20 where they showed that a 1/5-scale train wheel under scaled loading could produce a contact pressure comparable to that at the wheel-rail interface. The wheel diameter is 0.2 m (roughly 1/5 of the actual wheel diameter).…”
A small-scale laboratory apparatus was built to study liquid friction modifier (LFM) behavior in a top-of-rail application. A field experiment was also carried out to complement the laboratory findings. KELTRACK® (a water-based LFM) was used as the test fluid. Laser-induced fluorescence served to measure the LFM thickness left on the track after the passage of the wheel. The lab experiments show that the LFM cannot withstand the high wheel-rail contact pressure in the nip present in cargo rail situations. As a result, the liquid is squeezed out laterally and attaches to the edges of the wheel contact band. This “edge liquid” is then carried down the track on the wheel. Gravimetric measurements of the wheel contact band confirm this observation, and show that only a minute amount of liquid is carried through the nip in the valleys between surface roughness features. In the field experiment, the LFM is applied from a trackside unit on a tangent section of the track. About 500 m downstream of the application point, the track has a curved section. LFM cannot be detected anywhere on the track a short distance (∼200 m) past the application unit. However, LFM is detectable on the curved sections of the track up to approximately 2 km from the application unit. This LFM on the curved track is believed to be due to transfer of the “edge liquid” from the wheel to the track, caused by the movement of the contact band as the train rounds a curve. The presence of LFM on the curved track far downstream is consistent with prior measurements of reduced lateral force on the curved track downstream of LFM application sites.
“…Kampczyk [ 11 ] presents the analysis and evaluation of the turnout geometry conditions, also describing the causes of turnout deformations. Researchers such as Wootae Jeong and Dahae Jeong [ 12 ] present a method for accurately measuring the roughness of wheels and rails, considered the main cause of producing the noise during trains operation. They propose enhancing the chord offset synchronization algorithm applied to the existing ARCer for high measurement precision with only two displacement sensors.…”
This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maintenance of complex railways, or subway networks with long operating times is a difficult process and intensive resources consuming. The proposed solution delivers human operators in the fault management service and operations from the time-consuming task of railway inspection and measurements, by integrating several sensors and collecting most relevant information on railway, associated automation equipment and infrastructure on a single intelligent platform. The robotic cart integrates autonomy, remote sensing, artificial intelligence, and ability to detect even infrastructural anomalies. Moreover, via a future process of complex statistical filtering of data, it is foreseen that the solution might be configured to offer second-order information about infrastructure changes, such as land sliding, water flooding, or similar modifications. Results of simulations and field tests show the ability of the platform to integrate several fault management operations in a single process, useful in increasing railway capacity and resilience.
“…Experiments were performed to find the influence of temperature on modal properties used for damage diagnosis. Jeong and Jeong [12] proposed an optimization method for sensor batch design to measure roughness on railhead surfaces. This information allowed the estimation of corrugations and contact between wheel and rail during train operation to analyze rolling noise.…”
Section: Sensors Parameter Identification and Signal Processingmentioning
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