Loram Enhances Track Maintenance Analytics with TRACE Methodology
Loram has developed TRACE, an advanced analytics methodology for track condition diagnostics and predictive maintenance. This innovation is crucial for heavy haul railways in Australia, where effective maintenance planning can significantly reduce recurring track defects.
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Loram has introduced TRACE (Track Root-cause Analytics and Condition Evaluation) to enhance its track condition analytics capabilities for heavy haul railways. This methodology integrates Ground Penetrating Radar (GPR), LiDAR, and historical track geometry data to provide comprehensive diagnostics and predictive maintenance planning.
TRACE correlates defect behavior with ballast condition and moisture distribution, allowing asset managers to prioritize maintenance based on structural conditions. The approach has shown potential to reduce recurring defects by up to 80% over several years. As railways transition to data-driven asset management, this system aims to improve reliability and optimize maintenance investments.
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