Rail Geometry Measurement Alignment for Variable-Rate Data
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Solution Overview
Problem
Current data alignment methods for track geometry measurements face challenges such as handling highly segmented data without overlap, variable sample rates, spatially correlated and uncorrelated measurement errors, and ensuring consistent alignment over time, which are crucial for predictive maintenance in railway networks.
Innovation Solution
A novel algorithm that aligns rail parameter measurements by forming signals from measurement runs, finding pairwise relative offsets using cross correlation, and refining location estimates to ensure accurate mutual alignment, even with segmented and variable-rate data, while addressing measurement errors and maintaining consistency over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parameter-based alignment is used to find the closest match for each parameter value by incrementally shifting, then measurement precision is improved, but device complexity increases due to the need to process highly segmented data without overlap and handle variable sample rates
Solution Approach 1:
The patent divides the measurement data into discrete segments or windows along the track length. Each segment is processed independently to determine local alignment offsets, which are then integrated to produce the overall alignment. This segmentation approach handles highly segmented data without overlap by treating each segment as an independent unit that can be aligned locally, reducing the computational burden compared to processing the entire dataset at once.
Solution Approach 2:
The patent applies alignment adjustments selectively to specific segments where misalignment is detected, rather than applying a global adjustment to all data. By focusing computational effort only on segments that require correction, the system achieves accurate alignment without the excessive complexity of processing every data point uniformly, thus balancing precision with computational efficiency.
2Measurement precision
If cross correlation is used to find pairwise relative offsets between signals, then alignment accuracy is improved, but loss of time increases due to the computational intensity of processing multiple measurement runs
Solution Approach 1:
The patent divides the measurement data into discrete segments or windows along the track length. Each segment is processed independently to determine local alignment offsets, which are then integrated to produce the overall alignment. This segmentation approach handles highly segmented data without overlap by treating each segment as an independent unit that can be aligned locally, reducing the computational burden compared to processing the entire dataset at once.
Solution Approach 2:
The patent performs preliminary processing of measurement data by organizing it into segments and pre-calculating certain statistical properties or features before applying cross-correlation. This preliminary organization allows the cross-correlation algorithm to work more efficiently on structured data, reducing the time required for the intensive computation while maintaining alignment accuracy.
3Measurement precision
If data fusion between GNSS and INS is applied to improve position accuracy, then measurement precision is improved, but device complexity increases due to integration of multiple sensor systems
Solution Approach 1:
The patent introduces an intermediary alignment algorithm that processes measurement data from different sources (such as GNSS and INS) through a unified framework. Rather than directly integrating multiple sensor systems, the intermediary algorithm translates their outputs into a common reference frame and applies consistent alignment transformations. This mediator approach maintains position accuracy while reducing system complexity by providing a standardized interface between different measurement systems.
Data Source
AI summary
A method of aligning rail parameter measurements comprises receiving measurement data indicating variations in a parameter of the rail along the length of the rail derived from measurement runs performed at different points in time, each item of measurement data comprising a value for the parameter and an estimate of the location at which the parameter was measured. A signal is formed corresponding to each measurement run. Pairwise relative offsets are found between spatially overlapping segments in the signals corresponding to consecutive windows within the signals. For each signal and window the average relative offset to other signals is computed. Errors in the location estimates are determined based on the computed average relative offsets. The determined errors are used to refine the location estimates in the signal data.