Condition Monitoring of Railway Crossing Geometry via Measured and Simulated Track Responses
Artikel i vetenskaplig tidskrift, 2022
This paper presents methods for continuous condition monitoring of railway switches and crossings (S&C, turnout) via sleeper-mounted accelerometers at the crossing transition. The methods are developed from concurrently measured sleeper accelerations and scanned crossing geometries from six in situ crossing panels. These measurements combined with a multi-body simulation (MBS) model with a structural track model and implemented scanned crossing geometries are used to derive the link between the crossing geometry condition and the resulting track excitation. From this analysis, a crossing condition indicator Cλ1-λ2,γ is proposed. The indicator is defined as the root mean square (RMS) of a track response signal γ that has been band-passed between frequencies corresponding to track deformation wavelength bounds of λ1 and λ2 for the vehicle passing speed (f = v/ λ). In this way, the indicator ignores the quasi-static track response with wavelengths pre-dominantly above λ1 and targets the dynamic track response caused by the kinematic wheel-cross-ing interaction governed by the crossing geometry. For the studied crossing panels, the indicator C1-0.2 m,γ (λ1 = 1 and λ2 = 0.2) was evaluated for γ = u, v, or a as in displacements, velocities, and accelerations, respectively. It is shown that this condition indicator has a strong correlation with vertical wheel–rail contact forces that is sustained for various track conditions. Further, model calibrations were performed to measured sleeper displacements for the six investigated crossing panels. The calibrated models show (1) a good agreement between measured and simulated sleeper displacements for the lower frequency quasi-static track response and (2) improved agreement for the dynamic track response at higher frequencies. The calibration also improved the agreement between measurements and simulation for the crossing condition indicator demonstrating the value of model calibration for condition monitoring purposes.