Fusing frequent low-accuracy on-board vibration measurements with a track geometry degradation model via Kalman filtering substantially narrows credible prediction intervals for Top and Alignment indicators.
Intelligent and adaptive asset management model for railway sections using the iPN method
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Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering
Fusing frequent low-accuracy on-board vibration measurements with a track geometry degradation model via Kalman filtering substantially narrows credible prediction intervals for Top and Alignment indicators.