A railroad track inspection system has multiple track scanning sensors, a data store, and a scan data processor. The scan data processor provides automatic analysis of the track scan data to detect track components within the scan data from a predetermined list of component types according to features identified in said scan data. The track scanning sensors, data store and scan data processor are attached to a common support structure for mounting the system to a railway vehicle in use. An inertia sensor and common master clock are used to make corrections to the output of the track scanning sensors to accommodate dynamic forces in use. The inspection system may be provided in a single housing for mounting to a conventional passenger/freight rail vehicles and may operate automatically in an unattended mode. The location of track components and/or defects may be logged.

Methods and systems for detecting and measuring physical conditions of a railroad track based on image-based distance measurements are provided. In embodiments, at least one object associated with a condition of a railroad track is detected in at least one image. A first point and a second point on the at least one object is detected. A pixel distance between the first point and the second point is measured, and a physical distance-based measurement of the condition of the railroad track is determined, using a conversion model, based on the pixel distance between the first point and the second point. An alert is generated when the physical distance-based measurement of the condition of the railroad track exceeds a predetermined threshold.

A rail vehicle having rail wheels accommodated to guide the rail vehicle along a railway track. Each of the wheels is connected to the vehicle by an intermediate axle box that includes at least one accelerometer. A measurement system includes a receiver for receiving signals from the at least one accelerometer, and a vehicle-railway track interaction model that estimates the expected signals from the at least one accelerometer. The receiver and the vehicle-railway track interaction model connect to a comparator to compare the measured signals and the expected signals from the at least one accelerometer. The comparator connects to a tuning portion of the measurement system, which tuning portion is arranged to adjust parameters of the vehicle-railway track interaction model so as to provide a closer fit of the estimation of the expected signals from the at least one accelerometer with the measured signals from the at least one accelerometer.

A system for modeling risk of rail buckling in railroad infrastructure is presented. The system can receive a myriad of data related to railroad tracks and/or railroad operations, and weight the data using specially-designed weighting factors that can be unique to each data type. The weighted data can be transformed via specialized algorithms to generate location scores reflective of a risk isolated to a particular area. The system can further utilize additional specialized algorithms to elucidate how such isolated risk can be extrapolated from one location to another. The system can implement a multilayer approach, formulating one or more layers of risk models and aggregating such models into an overarching risk model that can more-accurately forecast risk of rail buckling in a railroad track.

A system and method for inspecting a railway track using sensors oriented at an oblique angle relative to a rail vehicle on which the system is traveling. The orientation of the sensors allows for different data to be gathered regarding a particular rail including rail design specifications (gathered based on manufacturer markings detected and analyzed by the system), rail seat abrasion values based on direct measurement of rails from the oblique angle, and other analysis of rail features including joint bars, rail welds, bond wires, rail holes, and broken rails. The use of an air blower, ducts, and one or more air distribution lids over the sensors helps remove debris from blocking the sensors and structured light generators.

A system for analyzing a railroad track comprises a transport device, a camera coupled to the transport device, an electronic display device, a memory device, and one or more processors. The camera is disposed adjacent to a rail of the railroad track and generates image data reproducible as one or more images of at least a portion of a surface of the rail. The processors can produce an image of the rail surface, which includes a plurality of elongated portions. The image is analyzed to identify any defects that exist within each elongated portion of the rail surface. The processors determine a value of a metric for each elongated portion of the rail surface. The metric is associated with the identified defects. The electronic display device displays a graph indicative of the metric for each elongated portion, the image of the rail surface, or both.

A system and method is disclosed for predicting and comparing wear scenarios in a rail system. The method can include generating and running a contact model of the interaction between a rail and a train car to produce a simulated loading on the rail for a predetermined time period; generating and running a wear model based on the material properties and/or friction modifier properties of the rail using the simulated loading to produce a simulated wear profile of the rail for the predetermined time period; obtaining a grinding profile to be performed on the rail during the predetermined time period; and generating an updated rail profile by modifying the rail profile by the simulated wear profile and the grinding profile. The method can predict and compare crack growth over time, and provide a financial model and comparison of costs and benefits for different maintenance protocols for the rail system.

The present disclosure includes systems, devices, and methods for identifying, detecting, and/or tracking rail features. In some aspects, a system includes a camera and a computer having at least one memory, at least one processor configured to receive a plurality of images from the camera, and for each of the images: assigning a location identifier and identifying one or more rail features that correspond to one of a plurality of predetermined rail features. In some systems, the at least one processor is configured to determine a location of each of the one or more identified rail features.

Systems for examining a route inject one or more electrical examination signals into a conductive route from onboard a vehicle system traveling along the route, detect one or more electrical characteristics of the route based on the one or more electrical examination signals, and detect a break in conductivity of the route responsive to the one or more electrical characteristics decreasing by more than a designated drop threshold for a time period within a designated drop time period. Feature vectors may be determined for the electrical characteristics and compared to one or more patterns in order to distinguish between breaks in the conductivity of the route and other causes for changes in the electrical characteristics.

A system and method for inspecting a railway track. The system preferably includes: a processor; at least one sensor oriented to capture data of the railway track; a data storage device in electronic communication with the processor; and computer executable instructions stored on a computer readable storage medium in communication with the processor operable to: determine an elevation of a surface of a rail head of a rail located on the railway track based on a distance to the rail head from the at least one sensor, determine an elevation of a surface of a crosstie of the railway track based on a distance to a top surface of the crosstie from the at least one sensor, estimate a total rail height and underlying rail support, and calculate a crosstie wear value based on the determined rail head surface elevation, crosstie surface elevation, and estimated total rail height and underlying rail support.