LRAIL™ Measurement Capabilities
Track Geometry
Measuring track geometry is fundamental to understanding track health. Gauge, alignment, profile and cross-level measurements are used to monitor and model track integrity for all track gauges under varying load conditions, and curvatures
While track geometry measurements typically require a separate dedicated inspection system, Railmetrics™ LRAIL™ performs these measurements simultaneous with other important inspections such as tie grading, fastener inspection, ballast profiling, turnout inspection, and more.
The LRAIL uses Artificial Intelligence to first correct 3D scans for measurement vehicle motion using built-in IMUs. Then 3D data is analyzed in order to detect rails, locate the top of rails for cross-level measurements and the gauge measurement point (5/8″below top of rail) for both gauge and rail alignment measurements. Chord lengths are customizable in order to match the customer’s specific standard.
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Rail Head Wear
Rail heads are regularly exposed to heavy loads from long train consists as well as localized stress associated with both acceleration and braking. These stresses deform the surface of the rails resulting in head and side wear.
While rail head wear inspection typically requires a separate dedicated inspection system, Railmetrics’ LRAIL is able to perform this task simultaneous with other important tasks such as tie grading, fastener inspection, ballast profiling, turnout inspection, and more.
The LRAIL algorithms analyze 3D data in order to measure top, side and corner rail wear. Rail wear measurements can be made for both standard rail profile as well as grooved(metro) rail.
Additionally, through the LRAIL’s Change Detection algorithms, changes in wear can be automatically detected and reported, pointing track maintenance crews to the portions of the network that are most in need of attention.
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Tie (Sleeper) Condition Assessment
Railroad ties (sleepers) play an important role in the function of a railroad by transferring loads to the track ballast and sub-grade, by holding rails upright, and by helping to maintain proper gauge thus preventing derailments.
However, wooden (timber) and concrete tie systems degrade over time and thus require efficient and objective inspections in order to ensure their integrity. Wooden ties can develop increasingly large splits, decay, become excessively plate cut and fail to properly hold gauge and surface. Concrete ties can develop both longitudinal and transverse cracking, and chipping from derailments and tamping operations. As well, both wooden and concrete ties can become skewed (no longer perpendicular to the rails) such that they may be widen gauge or making it difficult for tamping operations.
However, tie condition assessment can be a challenging task for traditional 2D machine vision systems as they are limited to assessment based on merely the appearance of the tie condition as opposed to 3D measurements of the length, width and depth of splits, cracks and chips. Additionally, the surface of ties can become discolored over time reducing the already limited condition information available from 2D system seven further.
Railmetrics’ LRAIL simplifies the tie inspection task by using Artificial Intelligence to analyze both 2D and 3D data in order to more accurately detect and report tie count, position, material, surface cover, tie skew, plate cut, wooden tie grade and concrete tie grade.
Additionally, through the LRAIL’s Change Detection algorithms, changes in tie grading and skew between repeat runs can be automatically detected and reported pointing track maintenance crews to the portions of the network that are most in need of attention.
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Spike and Anchors
Spikes play a crucial part in the safe and proper functioning of railway fastening systems as they are used to secure rails and base-plates to cross ties (sleepers). Anchors also play a role in mitigating longitudinal movement of the rail Spikes come in a variety of shapes and types with some of the most common being cut spikes, lag spikes and screw spikes.
Missing and high spikes degrade the overall performance of the fastening system and thus their detection is an important railway inspection task.
When anchors are missing or are not properly positioned next to a tie, then they are not able to perform their intended task of resisting longitudinal movement of the rails and thus identifying and inspecting anchor position is also an important railway inspection task.
However, spike and anchor inspection can be a challenging task for traditional 2D machine vision systems due to their lack of 3D data. 3D data is needed to detect high spikes and often to correctly identify spike heads which are difficult to separate from ballast particles due to their similar size and shape in 2D images.
Railmetrics’ LRAIL simplifies the spike inspection task by using Artificial Intelligence to analyze both 2D and 3D data in order to more accurately detect present and missing spikes, spiking pattern, high spikes and broken spikes.
Additionally, through the LRAIL’s Change Detection algorithms, changes in spike inventory and condition can be automatically detected and reported pointing track maintenance crews to the portions of the network that are most in need of attention.
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Elastic Fasteners
Elastic fastening systems play a crucial part in attaching ties to rail in a way that limits longitudinal and lateral movements of the tie. Missing, loose and damaged clips directly affect the tie’s performance in holding gauge and surface. However, elastic fastener inspection can be a challenging task for traditional 2D machine vision systems as they come in a wide variety of shapes, sizes and designs thus making them difficult to detect from 2D data alone.
Railmetrics’ LRAIL simplifies the fastener inspection task by using Artificial Intelligence to analyze both 2D and 3D data in order to more accurately detect elastic fastener type, both present and missing fasteners (and components), loose clips, damaged clips and obstructed clips.
Additionally, through the LRAIL’s Change Detection algorithms, changes in fastener inventory and conditions can be automatically detected and reported, pointing track maintenance crews to the portions of the network that are most in need of attention.
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Ballast Profile and Surface Fouling
Track ballast plays an important role in the proper functioning, and ultimately safety, of a railroad track. Ballast is used to transmit and distribute the load of the track and rolling equipment to the subgrade, restrain the track laterally, longitudinally and vertically, facilitate drainage of water, maintain proper cross level, surface, and elevation, as well as inhibit the growth of vegetation.
However, due to weather, train loading, and differences in ballast material quality, ballast can degrade in condition and become less effective over time. For example, heavy rains can result in ballast being washed away and leaving railway ties (sleepers) exposed and more likely to skew.
Ballast can also become fouled due to the fracture and abrasion of ballast particles, infiltration from underlying layers, and spillage from containers in transport. Fouling is often exacerbated by freeze-thaw cycles and fouled ballast can result in poor drainage and ultimately track surface deviation due to increased layer stiffness.
However ballast inspection is often a very subjective process due to the lack of a formal definition for when ballast is considered fouled and the often manual process of inspection.
Railmetrics’ LRAIL simplifies the task of ballast inspection by using Artificial Intelligence to analyze both 2D and 3D data in order to precisely and objectively measure ballast height and ballast fouling. Ballast height is automatically measured at the end of ties as well as in the crib area and locations with deficient or excessive ballast levels (based on user-definitions) can be flagged. Ballast fouling in the gauge side as well as in the field side can also be automatically detected and quantified objectively. Changes with regard to ballast height and fouling level between repeat runs can be automatically detected and reported.
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Turnout inspection
Track turnouts are designed to route trains from one track to another, allowing the trains to continue moving safely and efficiently without stopping. Turnout components are expensive, can wear quickly and are costly to repair. Proper maintenance of turnouts is vital to keep trains moving as safely and efficiently as possible, thus inspection of turnout components such as the switch points, flangeway clearances, frog flangeway depth, guard rails and wing rail clearances are vital in determining the condition of a turnout. Manually performing these inspections takes time and may delay train traffic. Additionally, manual inspections are limited to a small number of physical measurements and subjective observations that is variable dependent on the individual’s experience level. The LRAIL can identify major turnout components such as the switch points, switch heel blocks, the throat and nose of the frog, the wing rails, guard rails and switch rails and then will make key measurements every 2cm from beginning to end of the measurement region of interest.
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Road Crossing Inspection
As the LRAIL inspection system utilizes the same LCMS sensor technology as is used around the world for pavement condition inspection, the LRAIL inspection system can equally-well inspect the condition of pavement at road crossings. The LRAIL can identify cracks, potholes and other defects at road crossings. The crossing can then be assigned an overall condition grade indicating the severity of the defects identified.
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Change Detection
Until recently, the railway industry has tended to focus on the measurement of discrete performance parameters in order to determine if a maintenance or safety threshold has been met.
However, this approach is limited to flagging sections of track only once they meet a defined minimum threshold despite the fact that the same section of track may be steadily deteriorating up until that point.
Railmetrics’ LRAIL turns this process on its end by going beyond discrete measurements to the detection of change in key regions of interest along the track. This method allows track problems to be detected as they develop; well in advance of meeting maintenance or safety thresholds which could result in a closure or speed warning.
The LRAIL’s Artificial Intelligence automatically aligns repeat-runs and then analyzes both 2D and 3D data in order to detect changes between them.
Both positive changes, due to the performance of maintenance, as well as negative can be detected. Example changes that can be detected include:
Clip inventory changes including the number of missing or broken clips
Spike inventory changes including the number of missing spikes, the number of high spikes, the number of damaged spikes and changes in spike patterns
Wooden tie grading and skew angles
Concrete tie grading and skew angles
Ballast height and fouling level
Joint gap and joint bar bolting
Changes in gauge, cross level and alignment
Changes in rail surface condition
New features to come!
Want to find out more?
Get in touch with us to get further information about the variety of railway asset properties and defects our technology can automatically inspect.
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