System and method for railway track monitoring and defect detection

Abstract

Systems and techniques for railway track monitoring and defect detection. In embodiments, a system captures and processes rail profile data for detecting defects on a railroad track. An on-board rail profile capture system, mounted to a railroad vehicle, captures rail profile data at regular intervals as the vehicle traverses the track. The system includes profile capture sensors and an inertial measurement unit to capture rates and angles of the vehicle's orientation. A main computer receives the rail profile data and generates track geometry data. A backend rail profile processing system receives the track geometry data, processes it to determine rail defects, and generates notifications and alerts based on the determined defects. The system utilizes machine learning algorithms and models for defect detection and validation.

Description

SYSTEM AND METHOD FOR RAIL WA Y TRACK MONITORING AND DEFECT DETECTIONCROSS-REFERENCE TO RELATED APPLICATION

[0001] The present application claims the benefit of United States Provisional Patent Application Serial No. 63 / 636,004, filed April 18, 2024, which is incorporated herein by refemece for all purposes.TECHNICAL FIELD

[0002] The present disclosure relates generally to railway track monitoring technology, and more particularly to systems and methods for capturing rail profile data and calculating track geometry data for the detection of defects on railway tracks.BACKGROUND

[0003] Railway tracks are a cornerstone of the global transportation infrastructure, enabling the efficient and reliable movement of goods and passengers over long distances. The structural integrity and operational reliability of these tracks are of paramount concern to railway operators, as the safety of millions of passengers and the timely delivery of cargo hinge upon their condition. Over time, tracks are susceptible to a multitude of defects arising from wear, environmental factors such as temperature fluctuations and corrosion, and the stress of bearing the weight and vibrations of passing trains. Left undetected or unaddressed, these defects can escalate into serious failures, including derailments, which not only endanger public safety but also inflict severe economic damage through service disruptions and infrastructure repair costs.

[0004] The traditional approach to railway track inspection has been a manual one, with track inspectors employing a variety of specialized tools to measure track parameters such as alignment, gauge, and elevation. This manual inspection process, while thorough, is fraughtwith challenges. It is inherently slow and labor-intensive, making it difficult to cover extensive track networks comprehensively. The precision of these inspections is also limited by human factors, including variability in measurement techniques and the potential for error. The vastness of railway networks exacerbates these issues, rendering it nearly impractical to manually inspect every segment of track with the frequency and detail that would be ideal for ensuring safety and preventing failures.

[0005] Inspectors face additional risks due to the nature of their work environment. Conducting inspections on or near active tracks exposes them to potential accidents, while the physical demands of the job and exposure to adverse weather conditions can affect their wellbeing and the accuracy of their assessments. As rail traffic increases and trains become faster and heavier, the pressure on the tracks — and the demand for more frequent and detailed inspections — grows. The railway industry is acutely aware of the pressing demand for more efficient, accurate, and safer track inspection methods.

[0006] The limitations of manual inspections have prompted the industry to explore automated solutions that can offer continuous, precise, and non-intrusive monitoring of track conditions. Such automated systems promise to revolutionize track maintenance by enabling early detection of potential issues, thereby allowing for timely interventions that can prevent accidents and reduce maintenance costs. However, the adoption of these advanced technologies has been a complex process, with technical, financial, and operational challenges to address.

[0007] While some automated systems for track inspection have been developed, they often fall short in terms of robustness and transparency. Many of these systems are proprietary, which poses a challenge for railway operators who are ultimately responsible for the condition of the tracks. Without a clear understanding of how these systems operate and themethodologies they employ for inspection, operators cannot fully assess the effectiveness or reliability of the inspections. This lack of transparency and control is a major concern, as operators are tasked with ensuring the safety and integrity of the railway infrastructure.

[0008] The proprietary nature of existing systems can also lead to vendor lock-in, where operators are dependent on a single supplier for updates, maintenance, and support, which can be both costly and limiting in terms of flexibility to adapt to new requirements or integrate with other systems. Despite these challenges, the pursuit of improved track inspection methods remains a high priority, driven by the imperative to enhance the safety and efficiency of rail transport in an increasingly fast-paced and interconnected world.SUMMARY

[0009] The present disclosure achieves technical advantages as systems, methods, and computer-readable storage media for railway track monitoring and defect detection. In embodiments, an innovative system is configured to enhance the safety and reliability of railway transportation by providing a comprehensive solution for railway track monitoring and defect detection. The system of embodiments is configured to capture the profile of the rails on a train track with high precision, utilize the captured profile information to calculate the geometry of the train track, and employ these geometry calculations to identify potential defects on the track. The system’s capabilities extend to generating detailed reports and alerts based on the identified defects, facilitating timely and informed maintenance decisions.

[0010] At the core of the system of embodiments is the on-board rail profile capture system, which is mounted to a railroad vehicle. As the vehicle traverses the track, the system continuously captures the rail profile data at predetermined intervals. This rail profile data serves as the foundation for calculating various aspects of track geometry over several channels, which are indicative of the track’s condition. The system’s ability to operate at high speeds without compromising on data accuracy or resolution represents a substantial improvement over traditional manual inspection methods.

[0011] The calculated track geometry data is then analyzed to detect any irregularities or deviations from predefined standards that may signal the presence of defects. This analysis is not a trivial task, as it involves the processing of large volumes of data to distinguish between normal variations in track geometry and actual defects that could compromise the safety of rail operations. To achieve this, the system employs sophisticated algorithms, including machine learning techniques, which are adept at identifying patterns and anomalies within complex datasets.

[0012] Once potential defects are identified, the system generates alerts and reports that provide detailed information about the nature and location of the defects. These reports are instrumental for railway maintenance crews, enabling them to prioritize and carry out repair work effectively. The system’s ability to generate real-time alerts ensures that any immediate risks to rail safety are communicated promptly, allowing for swift action to prevent accidents.

[0013] As such, the present disclosure provides for a system integrated into a practical application with meaningful limitations as that represent an improved system for railway track monitoring, combining advanced data capture technology with powerful analytical tools to ensure the integrity of railway infrastructure. The advantageous result of the features of the system described herein provides several technical advantages that collectively enhance railway track monitoring, offering increased safety, reduced maintenance costs, and a streamlined, non-intrusive inspection process in line with modem operational demands.

[0014] For example, a system implemented in accordance with the present disclosure may provide automated high-speed data capture through an on-board rail profile capture system. Configured for efficiency, this component captures rail profile data at high velocities, enabling continuous track monitoring without impeding rail traffic. This capability facilitates more frequent and thorough inspections, which aids in the early detection of potential track defects.

[0015] Another significant technical improvement is the system’s precise interval data collection. Leveraging one or more wheel encoders, the system is designed to capture rail profile data at exact, predetermined intervals, such as every foot of track traversed. This tehnical approach yields a detailed, continuous profile of the railway track, affording a granular view of its condition and contributing to enhanced maintenance strategies.

[0016] Further augmenting the system’s improved performance is the inclusion of an inertial measurement unit (IMU), which captures the orientation of the railroad vehicle. This orientation data is crucial for refining rail profile information, adjusting for the vehicle’s movement and alignment to ensure that the TGD accurately represents the track’s actual condition.

[0017] The system is also designed with robust environmental adaptability. For example, the main enclosure is constructed from materials that are both lightweight and resilient, capable of enduring high-speed travel, environmental elements, and debris impacts. Strategic placement of sun shields mitigates interference from direct sunlight, maintaining data quality under varied lighting conditions.

[0018] In terms of data processing and analysis, the main computer plays a pivotal role, processing captured rail profile data to generate comprehensive TGD. This data spans multiple channels, including gauge, crosslevel, alignment, surface, and curvature, derived from both static and chordal measurements. This extensive analysis provides a thorough examination of the track’s geometry, enhancing the monitoring process.

[0019] The backend rail profile processing system employs sophisticated machine learning algorithms and models to analyze TGD, identifying patterns indicative of defects. This distinction between normal track geometry variations and actual defects significantly improves defect detection accuracy. Moreover, the system features an intelligent TGD file transmission manager, which optimizes the timing and conditions for data transmission to the backend system, ensuring reliable data transfer and timely processing.

[0020] As if the above improvements were not enough, an automated maintenance workflow integration capability enables the backend system to generate signals that actuate maintenance equipment in response to detected defects. This functionality facilitatesautomated corrective actions, streamlining the maintenance process and underscoring the system’s comprehensive approach to modem railway track monitoring.

[0021] Thus, it will be appreciated that the technological solutions provided herein, and missing from conventional systems, are more than a mere application of a manual process to a computerized environment, but rather include functionality to implement a technical process to replace or supplement current manual solutions or non-existing solutions for railway track monitoring. In doing so, the present disclosure goes well beyond a mere application the manual process to a computer. Accordingly, the claims herein necessarily provide a technological solution that overcomes a technological problem.

[0022] In embodiments, the present disclosure includes techniques for training models (e.g., machine-learning models, artificial intelligence models, algorithmic constructs, etc.) for performing or executing a designated task or a series of tasks (e.g., one or more features for TGD generation, defect detection, and / or defect validation in accordance with embodiments of the present disclosure). The disclosed techniques provide a systematic approach for the training of such models to enhance performance, accuracy, and efficiency in their respective applications. In embodiments, the techniques for training the models may include collecting a set of data from a database, conditioning the set of data to generate a set of conditioned data, and / or generating a set of training data including the collected set of data and / or the conditioned set of data. In embodiments, that model may undergo a training phase wherein the model may be exposed to the set of training data, such as through an iterative processes of learning in which the model adjusts and optimizes its parameters and algorithms to improve its performance on the designated task or series of tasks. This training phase may configure the model to develop the capability to perform its intended function with a high degree of accuracy and efficiency. In embodiments, the conditioning of the set of data may include modification,transformation, and / or the application of targeted algorithms to prepare the data for training.The conditioning step may be configured to ensure that the set of data is in an optimal state for training the model, resulting in an enhancement of the effectiveness of the model’s learning process. These features and techniques not only qualify as patent-eligible features but also introduce substantial improvements to the field of computational modeling. These features are not merely theoretical but represent an integration of a concepts into a practical applications that significantly enhance the functionality, reliability, and efficiency of the models developed through these processes.

[0023] In embodiments, the present disclosure includes techniques for generating a notification of an event (e.g., a defect notification, a maintenance required notification, a false positive notification, etc.) includes generating an alert that includes information specifying the location of a source of data associated with the event, formatting the alert into data structured according to an information format; and transmitting the formatted alert over a network to a device associated with a receiver based upon a destination address and a transmission schedule. In embodiments, receiving the alert enables a connection from the device associated with the receiver to the data source over the network when the device is connected to the source to retrieve the data associated with the event and causes a viewer application (e.g., a graphical user interface (GUI)) to be activated to display the data associated with the event. These features represent patent eligible features, as these features amount to significantly more than an abstract idea. These features, when considered as an ordered combination, amount to significantly more than simply organizing and comparing data. The features address the Internet-centric challenge of alerting a receiver with time sensitive information. This is addressed by transmitting the alert over a network to activate the viewer application, which enables the connection of the device of the receiver to the source over the network to retrieve the data associated with the event. These are meaningful limitations that add more thangenerally linking the use of an abstract idea (e.g., the general concept of organizing and comparing data) to the Internet, because they solve an Internet-centric problem with a solution that is necessarily rooted in computer technology. These features, when taken as an ordered combination, provide unconventional steps that confine the abstract idea to a particular useful application. Therefore, these features represent patent eligible subject matter.

[0024] It is an object of the disclosure to provide a method of capturing and processing rail profile data. It is a further object of the disclosure to provide a system for capturing and processing rail profile data, and a computer-based tool for capturing and processing rail profile data. These and other objects are provided by the present disclosure, including at least the following embodiments.

[0025] In one particular embodiment, a method of capturing and processing rail profile data is provided. The method includes capturing, by one or more profile capture sensors of an on-board rail profile capture system mounted to a railroad vehicle, rail profile data at predetermined intervals as the railroad vehicle traverses a railroad track. In embodiments, the rail profile data is associated with one or more rails of the railroad track. The method also includes determining, based on a global positioning system (GPS) module, a GPS location of the railroad vehicle at one or more of the predetermined intervals and calculating, based on the captured rail profile data, a plurality of data channels related to the geometry of the railroad track. In embodiments, each data channel of the plurality of data channels is associated with a different feature of the geometry of the railroad track. In embodiments, at least one of the plurality of data channels is calculated based on the determined GPS location of the railroad vehicle. The method further includes generating TGD based on the data calculated for the plurality of data channels and transmitting the TGD from the on-board rail profile capture system to a backend rail profile processing system via a network. In embodiments, the TGDis processed at the backend rail profile processing system to determine defects on the railroad track and to generate track defect reports and notifications based on the determined defects.

[0026] In another embodiment, a system for capturing and processing rail profile data is provided. The system comprises at least one processor and a memory operably coupled to the at least one processor and storing processor-readable code that, when executed by the at least one processor, is configured to perform operations. The operations include capturing, by one or more profile capture sensors of an on-board rail profile capture system mounted to a railroad vehicle, rail profile data at predetermined intervals as the railroad vehicle traverses a railroad track. In embodiments, the rail profile data is associated with one or more rails of the railroad track. The operations also include determining, based on a global positioning system (GPS) module, a GPS location of the railroad vehicle at one or more of the predetermined intervals and calculating, based on the captured rail profile data, a plurality of data channels related to the geometry of the railroad track. In embodiments, each data channel of the plurality of data channels is associated with a different feature of the geometry of the railroad track. In embodiments, at least one of the plurality of data channels is calculated based on the determined GPS location of the railroad vehicle. The operations further include generating TGD based on the data calculated for the plurality of data channels and transmitting the TGD from the onboard rail profile capture system to a backend rail profile processing system via a network. In embodiments, the TGD is processed at the backend rail profile processing system to determine defects on the railroad track and to generate track defect reports and notifications based on the determined defects.

[0027] In yet another embodiment, a computer-based tool for capturing and processing rail profile data is provided. The computer-based tool including non-transitory computer readable media having stored thereon computer code which, when executed by a processor,causes a computing device to perform operations. The operations capturing, by one or more profile capture sensors of an on-board rail profile capture system mounted to a railroad vehicle, rail profile data at predetermined intervals as the railroad vehicle traverses a railroad track. In embodiments, the rail profile data is associated with one or more rails of the railroad track. The operations also include determining, based on a global positioning system (GPS) module, a GPS location of the railroad vehicle at one or more of the predetermined intervals and calculating, based on the captured rail profile data, a plurality of data channels related to the geometry of the railroad track. In embodiments, each data channel of the plurality of data channels is associated with a different feature of the geometry of the railroad track. In embodiments, at least one of the plurality of data channels is calculated based on the determined GPS location of the railroad vehicle. The operations further include generating TGD based on the data calculated for the plurality of data channels and transmitting the TGD from the onboard rail profde capture system to a backend rail profde processing system via a network. In embodiments, the TGD is processed at the backend rail profde processing system to determine defects on the railroad track and to generate track defect reports and notifications based on the determined defects.

[0028] The foregoing has outlined rather broadly the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. Additional features and advantages of the disclosure will be described hereinafter which form the subject of the claims of the disclosure. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the disclosure as set forth in the appended claims. The novel features which are believed to be characteristic of thedisclosure, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0029] For a more complete understanding of the present disclosure, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

[0030] FIG. 1 is a block diagram of an exemplary system configured with capabilities and functionality for railway track monitoring and defect detection in accordance with embodiments of the present disclosure.

[0031] FIG. 2 is a block diagram illustrating an example of operations of an on-board rail profile capture system configured with capabilities and functionality for capturing rail profile data and generating track geometry data in accordance with embodiments of the present disclosure.

[0032] FIG. 3A show an example configuration of an on-board rail profile capture system mounted to the underside of a railroad vehicle in accordance with embodiments of the present disclosure.

[0033] FIG. 3B shows the example configuration of the on-board system mounted to the underside of a railroad vehicle with the railroad vehicle removed to offer a clearer perspective of the system’s components and their arrangement in accordance with embodiments of the present disclosure.

[0034] FIG. 3C shows a perspective view of amain enclosure assembly of the on-board rail profile capture system configured in accordance with embodiments of the present disclosure.

[0035] FIG. 3D presents an exploded view of the main enclosure assembly configured with a modular configuration in accordance with embodiments of the present disclosure.

[0036] FIG. 4 is a flowchart illustrating operations for railway track monitoring and defect detection in accordance with embodiments of the present disclosure.

[0037] It should be understood that the drawings are not necessarily to scale and that the disclosed embodiments are sometimes illustrated diagrammatically and in partial views. In certain instances, details which are not necessary for an understanding of the disclosed methods and apparatuses or which render other details difficult to perceive may have been omitted. It should be understood, of course, that this disclosure is not limited to the particular embodiments illustrated herein.DETAILED DESCRIPTION

[0038] The disclosure presented in the following written description and the various features and advantageous details thereof, are explained more fully with reference to the nonlimiting examples included in the accompanying drawings and as detailed in the description. Descriptions of well-known components have been omitted to not unnecessarily obscure the principal features described herein. The examples used in the following description are intended to facilitate an understanding of the ways in which the disclosure can be implemented and practiced. A person of ordinary skill in the art would read this disclosure to mean that any suitable combination of the functionality or exemplary embodiments below could be combined to achieve the subject matter claimed. The disclosure includes either a representative number of species falling within the scope of the genus or structural features common to the members of the genus so that one of ordinary skill in the art can recognize the members of the genus. Accordingly, these examples should not be construed as limiting the scope of the claims.

[0039] A person of ordinary skill in the art would understand that any system claims presented herein encompass all of the elements and limitations disclosed therein, and as such, require that each system claim be viewed as a whole. Any reasonably foreseeable items functionally related to the claims are also relevant. The Examiner, after having obtained a thorough understanding of the disclosure and claims of the present application has searched the prior art as disclosed in patents and other published documents, i.e., nonpatent literature. Therefore, the issuance of this patent is evidence that: the elements and limitations presented in the claims are enabled by the specification and drawings, the issued claims are directed toward patent-eligible subject matter, and the prior art fails to disclose or teach the claims as a whole, such that the issued claims of this patent are patentable under the applicable laws and rules of this country.

[0040] Various embodiments of the present disclosure are directed to systems and techniques that provide functionality for railway track monitoring and defect detection. In embodiments, the functionality for railway track monitoring and defect detection may include functionality to capture (e.g., using an on-board rail profile capture system mounted to a railroad vehicle) rail profile data at regular intervals as the railroad vehicle traverses a railroad track. The captured rail profile data may be used to generate track geometry data (TGD), which may include a plurality of data channels that may be indicative of the condition of the railroad track. The TGD may be sent to a backend rail profile processing system, which may be configured to process the TGD and to determine the presence of defects on the railroad track and to generate track defect reports and notifications. These track defect reports and notifications may be used by railway operators to take appropriate maintenance actions to address any identified issues, and in this manner enhance the safety and efficiency of the railway infrastructure.

[0041] The functionality of a system implemented in accordance with the present disclosure may enable a system to overcome the limitations of manual inspections and proprietary automated systems by providing a robust, non-proprietary solution that offers continuous, precise, and non-intrusive monitoring of track conditions. By utilizing advanced technologies such as machine learning algorithms and models for defect detection and validation, the system 100 ensures a high level of accuracy and transparency in the inspection process, enabling railway operators to maintain the integrity of their tracks with confidence.

[0042] FIG. 1 is a block diagram of an exemplary system 100 configured with capabilities and functionality for railway track monitoring and defect detection in accordance with embodiments of the present disclosure. As shown in FIG. 1, system 100 may include an on-board rail profile capture system 110, a backend rail profile processing system 150, userterminal 130, and network 145. These components, and their individual components, may cooperatively operate to provide functionality in accordance with the discussion herein. In particular, on-board rail profde capture system 110 may be configured to capture rail profile data at regular intervals as the railroad vehicle traverses a railroad track. The captured rail profile data may be used to generate track geometry data (TGD) 111, which may include a plurality of data channels and may be indicative of the condition of the railroad track 115. Onboard rail profile capture system 110 may be in communication with backend rail profile processing system 150 (e.g., via a network 145) and may send the TGD 111 to backend rail profile processing system 150. Backend rail profile processing system 150 may process the TGD 111 to determine the presence of defects on the rail and to generate track defect reports and notifications 151. These track reports and notifications 151 may be used by railway operators to take appropriate maintenance actions to address any identified issues, and in this manner enhance the safety and efficiency of the railway infrastructure.

[0043] It is noted that the functional blocks, and components thereof, of system 100 of embodiments of the present disclosure may be implemented using processors, electronics devices, hardware devices, electronics components, logical circuits, memories, software codes, firmware codes, etc., or any combination thereof. For example, one or more functional blocks, or some portion thereof, may be implemented as discrete gate or transistor logic, discrete hardware components, or combinations thereof configured to provide logic for performing the functions described herein. Additionally, or alternatively, when implemented in software, one or more of the functional blocks, or some portion thereof, may comprise code segments operable upon a processor to provide logic for performing the functions described herein.

[0044] It is also noted that various components of system 100 are illustrated as single and separate components. However, it will be appreciated that each of the various illustratedcomponents may be implemented as a single component (e.g., a single application, server module, etc.), may be functional components of a single component, or the functionality of these various components may be distributed over multiple devices / components. In such embodiments, the functionality of each respective component may be aggregated from the functionality of multiple modules residing in a single, or in multiple devices.

[0045] It is further noted that functionalities described with reference to each of the different functional blocks of system 100 described herein is provided for purposes of illustration, rather than by way of limitation and that functionalities described as being provided by different functional blocks may be combined into a single component or may be provided via computing resources disposed in a cloud-based environment accessible over a network, such as one of network 145.

[0046] User terminal 130 may include a mobile device, a smartphone, a tablet computing device, a personal computing device, a laptop computing device, a desktop computing device, a computer system of a vehicle, a personal digital assistant (PDA), a smart watch, another type of wired and / or wireless computing device, or any part thereof. In embodiments, user terminal 130 may provide a user interface that may be configured to provide an interface (e.g., a graphical user interface (GUI)) structured to facilitate an operator interacting with system 100, e.g., via network 145, to execute and leverage the features provided by the cooperative operations of system 100. In embodiments, the operator may be enabled, e.g., through the functionality of user terminal 130, to provide functionality for managing railway track monitoring and defect detection in accordance with embodiments of the present disclosure. In embodiments, the operator may receive track condition reports, track defect reports, notifications, alerts, etc. via the GUI in accordance with embodiments of thepresent disclosure. In embodiments, user terminal 130 may be configured to communicate with other components of system 100.

[0047] In embodiments, network 145 may facilitate communications between the various components of system 100 (e.g., on-board rail profde capture system 110, backend rail profde processing system 150, and / or user terminal 130). Network 145 may include a wired network, a wireless communication network, a cellular network, a cable transmission system, a Local Area Network (LAN), a Wireless LAN (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), the Internet, the Public Switched Telephone Network (PSTN), etc.

[0048] System 100 is meticulously architected and / or configured to ensure the integrity and safety of railway infrastructure through advanced monitoring and defect detection capabilities. In embodiments, and for the purposes of the present disclosure, the functionality of system 100 may be bifurcated into two main components, each serving a distinct yet interrelated function within the overall system. The first main component includes on-board rail profile capture system 110, which is primarily responsible for the direct acquisition of rail profile data associated with the rails of the railroad track as the railroad vehicle moves along the railroad track. On-board rail profile capture system 110 may be configured to be mounted on the railroad vehicle (e.g., to the underside of the railroad vehicle with line of sight to the rails of the railroad track) and includes the hardware and software elements that enable the capture, initial processing of the rail profile data to generate TGD 111, storage of the rail profile data and TGD 111, and transmission of TGD 111 to backend rail profile processing system 150.

[0049] The second main component of system 100 includes backend rail profile processing system 150, which may serve as the analytical and reporting hub for the datacollected by the on-board rail profile capture system 110. Backend system 150 may be tasked with the more complex processing of TGD 111, including the application of machine learning algorithms and models to accurately identify and classify defects on the railroad track. Additionally, backend rail profile processing system 150 may generate the track defect reports and notifications 151 that are instrumental for railway operators in making informed decisions regarding track maintenance and safety measures, and may be used to automatically trigger the actuation of equipment to address the findings in the track defect reports and notifications 151.

[0050] Together, the two main components of system 100 form a cohesive system that enhances the predictive maintenance capabilities of railway operators and contributes to the prevention of track-related incidents.

[0051] As noted above, on-board rail profile capture system 110 may be configured to capture precise rail profile data as a railroad vehicle traverses the tracks. This data forms the basis for the subsequent analysis and assessment of the condition of the railway track, enabling the identification of any potential defects that may be present.

[0052] In embodiments, the configuration of on-board rail profile capture system 110 is optimized for high-speed operation, ensuring that rail profile data can be effectively captured even when the railroad vehicle is moving at high speeds. This is a particularly valuable feature, as it allows for the continuous monitoring of the tracks without disrupting regular railway operations or requiring the vehicle to slow down or stop for inspections.

[0053] In embodiments, on-board rail profile capture system 110 may be configured to withstand a wide range of environmental conditions. Whether it’s operating under the scorching sun, in heavy rain, or in freezing temperatures, on-board rail profile capture system 110 may be configured to function reliably and deliver accurate data. This robustness ensures that on-board rail profile capture system 110 can be used for track monitoring in variousgeographical locations and climates, making it a versatile solution for railway operators worldwide.

[0054] On-board rail profile capture system 110 represents a sophisticated piece of technology that plays a pivotal role in the railway track monitoring process. On-board rail profile capture system 110’s ability to capture precise rail profile data at high speeds and under various environmental conditions ensures that railway operators have access to reliable and timely information about the condition of their tracks, enabling them to detect and address potential defects promptly and effectively.

[0055] In embodiments, on-board rail profile capture system 110 is configured to capture detailed rail profile data as a railroad vehicle moves along the track. In embodiments, the captured rail profile data includes rail profile data for each rail — left and right — of the track. The rail profile data provides insights into the shape and profile of each rail of the track. As the railroad vehicle travels along the track, on-board rail profile capture system 110 employs its one or more profile capture sensors (e.g., comprising laser modules in some embodiments), to project laser beams onto the rails (e.g., one laser module per rail). These beams are reflected back and captured by cameras within the one or more profile capture sensors, translating into a series of reflection points that collectively define the rail’s profile. Each reflection point is recorded as an x-y coordinate, relative to the sensor’s position, effectively mapping out the rail’s contour and dimensions at precise intervals. In embodiments, the rail profile data capture may be performed at periodic intervals (e.g., every foot of track traversed).

[0056] The rail profile data, once captured, is more than just a static representation of the track’s condition. The rail profile data associated with the rails of the track serves as the foundation for a series of complex mathematical and machine learning algorithms applied by on-board rail profile capture system 110 that process and analyze the data to generate TGD111. TGD 111 may include data related to various channels, each representing a specific aspect of the track’s geometry, such as gauge, crosslevel, alignment, surface, and curvature. The data for the TGD channels may be compiled into a TGD file, and may organized according to predefined parameters, such as distance traveled or file size.

[0057] On-board rail profile capture system 110 may intelligently determine when to transmit the TGD file including TGD 111 to backend rail profile processing system 150. In this manner, on-board rail profile capture system 110 may not simply be a data collector, but rather an intelligent decision-maker. On-board rail profile capture system 110 determines the opportune moments to transmit the TGD file to the backend rail profile processing system 150. On-board rail profile capture system 110 assesses factors such as the file’s size, the distance covered by the vehicle, and the availability of a robust network signal to decide when to send the file. This ensures that backend rail profile processing system 150 receives the TGD file in a timely and efficient manner, allowing for the prompt identification of potential track defects and the initiation of maintenance actions. Backend rail profile processing system 150, equipped with its own suite of advanced algorithms, further scrutinizes TGD 111 to confirm the presence of defects and to generate the requisite notifications and alerts for railway operators.

[0058] Backend rail profile processing system 150 may be configured to provide functionality as the analytical and reporting hub for the data collected by the on-board rail profile capture system 110. Backend system 150 may be configured to process TGD 111 received from on-board rail profile capture system 110 by applying mathematical and machine learning algorithms and models to accurately identify and classify defects on the track.Additionally, backend rail profile processing system 150 generates the track defect reports andnotifications 151 that are instrumental for railway operators in making informed decisions regarding track maintenance and safety measures.

[0059] For example, upon receiving the TGD file including TGD 111 from on-board rail profile capture system 110, backend rail profile processing system 150 may engage in a thorough analysis of TGD 111 across the various TGD channels. Backend rail profile processing system 150 may utilize sophisticated machine learning algorithms and models that have been trained to recognize patterns indicative of various types of track anomalies. These could range from minor irregularities that may require monitoring over time, to urgent defects that necessitate immediate intervention. Backend rail profile processing system 150’s ability to differentiate between normal variations in track geometry and genuine defects reflects the advanced nature of the algorithms provided by system 100.

[0060] By employing advanced machine learning algorithms and models, backend system 150 can detect a wide array of defect types, with the capability to pinpoint defects associated with each TGD channel of TGD 111. For example, the gauge channel of TGD 111 may provide insights into the lateral distance between the right and left rails. Analysis of this channel may reveal anomalies in the gauge measurements that may indicate a defect. A specific example could be a section of the track where the gauge measurement deviates from the standard track width, suggesting a potential narrowing or widening of the track at that particular location. Such a defect could compromise the stability of the trains running over it and requires immediate attention.

[0061] Similarly, the surface channel of TGD 111 may focus on the vertical alignment between the rails. Analysis of this channel may uncover irregularities in the surface profile, such as unevenness or dips along a particular section of the track. These surface defects could lead to a rough ride, potential wheel damage, or even derailment risks. By identifying theseissues, backend rail profile processing system 150 may alert maintenance crews to areas that require resurfacing or other corrective measures to restore the track to its proper condition.

[0062] In embodiments, backend rail profile processing system 150 may not just identify defects but may also categorize the defects them based on severity and location, enabling targeted maintenance efforts. Backend rail profile processing system 150 may also be configured to validate the detected defects to filter out false positives, ensuring that the maintenance resources are directed towards genuine concerns. For example, backend rail profile processing system 150 may be equipped with validation mechanisms to ensure the reliability of the defect detection process. Backend rail profile processing system 150 may be configured to apply mathematical and machine learning algorithms to the detected defects and TGD 111 to determine false positives — instances where the system might initially identify a defect that does not actually represent a threat to track integrity. By filtering out these false alarms, backend rail profile processing system 150 enhances the accuracy of its diagnostics, and optimizes maintenance operations and resource allocation.

[0063] In embodiments, once detected defects are confirmed and / or validated, backend rail profile processing system 150 may generate detailed track defect reports and notifications 151, which may include the type, location, and recommended actions for each defect. These reports and alerts may be communicated to railway operators, who can prioritize maintenance tasks and address the defects to maintain the safety and integrity of the railway infrastructure. In some embodiments, backend rail profile processing system 150 generate automatic signals to actuate equipment in response to the detection of defects. This proactive approach to track maintenance enhances the efficiency and responsiveness of railway operations.

[0064] For example, in response to identifying a defect within TGD 111, backend rail profile processing system 150 may categorize the defect based on type, severity, and location.If the defect is of a nature that can be addressed by automated maintenance equipment, backend rail profile processing system 150 may generate a signal that is transmitted to a controller of the appropriate equipment. For example, if the gauge channel analysis reveals a narrowing of the track width that can be corrected by track adjustment machinery, backend rail profile processing system 150 may send a signal to initiate the adjustment process automatically.

[0065] Similarly, if the surface channel analysis detects an uneven section of track that requires grinding or leveling, backend rail profile processing system 150 may automatically dispatch a signal to a track grinding machine to commence operations at the specified location. This automation of defect correction not just streamlines the maintenance process but also reduces the time lag between defect detection and remediation, minimizing the risk of accidents and service disruptions.

[0066] Backend rail profile processing system 150 may be integrated with a centralized control system that manages a fleet of maintenance machinery. Upon receiving the automatic signals, this control system can schedule and deploy the machinery to the exact locations where the defects have been identified. The integration of TGD analysis with automated maintenance operations represents a sophisticated approach to railway infrastructure management, ensuring that the tracks are kept in optimum condition with minimum human intervention and maximum precision.

[0067] Operations of on-board rail profile capture system 110 will now be discussed with respect to FIG. 2. FIG. 2 is a block diagram illustrating an example of operations of an on-board rail profile capture system 110 configured with capabilities and functionality for capturing rail profile data and generating track geometry data in accordance with embodiments of the present disclosure. In embodiments, functionality of on-board rail profile capture system110 for capturing rail profile data associated with the rails of track 115 and generating TGDmay be provided by the cooperative operation of the various components of on-board rail profde capture system 110, as will be described in more detail below.

[0068] It is noted that although FIG. 2 shows on-board rail profde capture system 110 as a single component, it will be appreciated that on-board rail profde capture system 110 (and the individual functional blocks of on-board rail profde capture system 110) may be implemented as separate devices and / or may be distributed over multiple devices having their own processing resources, whose aggregate functionality may be configured to perform operations in accordance with the present disclosure. Furthermore, those of skill in the art would recognize that although FIG. 2 illustrates components of on-board rail profde capture system 110 as single and separate blocks, each of the various components of on-board rail profde capture system 110 may be a single component (e.g., a single application, server module, etc.), may be functional components of a same component, or the functionality may be distributed over multiple devices / components. In such embodiments, the functionality of each respective component may be aggregated from the functionality of multiple modules residing in a single, or in multiple devices. In addition, particular functionality described for a particular component of on-board rail profde capture system 110 may actually be part of a different component of on-board rail profde capture system 110, and as such, the description of the particular functionality described for the particular component of on-board rail profde capture system 110 is for illustrative purposes and not limiting in any way.

[0069] As shown in FIG. 2, on-board rail profde capture system 110 includes processor 111, memory 112, data processor 120, TGD calculations manager 121, TGD fde generator 122, TGD fde transmission manager 123, one or more profde capture sensors 125, inertial measurement unit (IMU) 126, one or more wheel encoders 127, one or more sun shields 128, and database 114.

[0070] Processor 111 may comprise a processor, a microprocessor, a controller, a microcontroller, a plurality of microprocessors, an application-specific integrated circuit (ASIC), an application-specific standard product (ASSP), or any combination thereof, and may be configured to execute instructions to perform operations in accordance with the disclosure herein. In some embodiments, implementations of processor 111 may comprise code segments (e.g., software, firmware, and / or hardware logic) executable in hardware, such as a processor, to perform the tasks and functions described herein. In yet other embodiments, processor 111 may be implemented as a combination of hardware and software. Processor 111 may be communicatively coupled to memory 112.

[0071] Memory 112 may comprise one or more semiconductor memory devices, read only memory (ROM) devices, random access memory (RAM) devices, one or more hard disk drives (HDDs), flash memory devices, solid state drives (SSDs), erasable ROM (EROM), compact disk ROM (CD-ROM), optical disks, other devices configured to store data in a persistent or non-persistent state, network memory, cloud memory, local memory, or a combination of different memory devices. Memory 112 may comprise a processor readable medium configured to store one or more instruction sets (e.g., software, firmware, etc.) which, when executed by a processor (e.g., one or more processors of processor 111), perform tasks and functions as described herein.

[0072] Memory 112 may also be configured to facilitate storage operations. For example, memory 112 may comprise database 114 for storing various information related to operations of system 100. For example, database 114 may store configuration information related to operations of on-board rail profile capture system 110. In embodiments, database114 may store information related to various models used during operations of on-board rail profile capture system 110, such as a mathematical and / or machine learning algorithms usedto generate TGD data from rail profile data. Database 114 is illustrated as integrated into memory 112, but in some embodiments, database 114 may be provided as a separate storage module or may be provided as a cloud-based storage module. Additionally, or alternatively, database 114 may be a single database, or may be a distributed database implemented over a plurality of database modules.

[0073] In embodiments, on-board rail profile capture system 110 may be configured with a main enclosure assembly, such as enclosure assembly 350 depicted in FIGS. 3A-3D. This enclosure serves as a protective housing for several of the system’s core components, namely one or more profile capture sensors 125, which are instrumental in capturing the rail profile data, and IMU 126, which may be used to determine the railroad vehicle’s orientation in terms of roll, pitch, and yaw. Additionally, the main enclosure assembly may house a main computer assembly, where processor 111 and memory 112, including various subcomponents such as the data processor 120, TGD calculations manager 121, TGD file generator 122, and TGD file transmission manager 123.

[0074] While the main enclosure assembly is the central hub for data capture and initial processing, other components of the on-board rail profile capture system 110 are mounted externally to the railroad vehicle at specific locations to fulfill their respective functions. One or more wheel encoders 127 may be mounted directly to the wheels of the vehicle. The role of one or more wheel encoders 127 may include accurately measuring the distance traveled by the railroad vehicle along track 115, which may be a pivotal parameter for capturing rail profile data at precise intervals. One or more sun shields 128 may be strategically placed to prevent direct sunlight from interfering with the laser modules of the one or more profile capture sensors 125. These one or more sun shields 128 may be designed to mitigate the effects ofglare and ensure that the quality of the rail profile data remains uncompromised by variations in lighting conditions.

[0075] One or more profile capture sensors 125 represent a central component of onboard rail profile capture system 110, and may play a pivotal role in the acquisition of rail profile data as the railroad vehicle moves along track 115. One or more profile capture sensors 125 may be configured to capture detailed rail profile data, which may provide insights into the shape and profile of each rail — left and right — of the track. As the railroad vehicle on which on-board rail profile capture system 110 may be mounted travels or moves along track 115, one or more profile capture sensors 125 may capture the rail profile data of each of the left and right rails of track 115.

[0076] In embodiments, one or more profile capture sensors 125 may include one or more laser modules. For example, one or more profile capture sensors 125 may include a right laser module configured to capture rail profile data associated with the right rail of track 115 and a left laser module configured to capture rail profile data associated with the left rail of track 115. Right and left orientations may be with respect to the travel direction of the railroad vehicle. In embodiments, each laser module may include a laser beam projector configured to project a laser beam onto a corresponding rail and a camera configured to detect the reflections of the laser beam. For example, the right laser module may include a laser beam projector configured to project a laser beam onto the right rail of track 115 and a camera configured to detect the reflections of the laser beam, and the left laser module may include a laser beam projector configured to project a laser beam onto the left rail of track 115 and a camera configured to detect the reflections of the laser beam.

[0077] In embodiments, the laser module may translate the reflections of the laser beam captured by the camera into a series of reflection points that collectively define the rail’s profile.Each reflection point may be recorded as an x-y coordinate, relative to the laser module’s position. In this manner, each of the one or more profile capture sensors 125 effectively maps out the corresponding rail’s profile, contour, and dimensions at precise intervals along the length of track 115 (e.g., at each foot of track traversed).

[0078] IMU 126 may be configured to capture the dynamic orientation and movement of the railroad vehicle om which on-board rail profile capture system 110 may be mounted as the railroad vehicle travels along track 115. IMU 126 may be configured to measure and record the vehicle’s angular rates and orientation angles — specifically roll, pitch, and yaw — which are pivotal in understanding the vehicle’s positioning in three-dimensional space. This orientation information is particularly valuable when the railroad vehicle is navigating through varying terrains, such as inclines, declines, and curves along the railway track.

[0079] In embodiment, the data captured by IMU 126 may be utilized by on-board rail profile capture system 110 to enhance the accuracy of the rail profile data collected by the one or more profile capture sensors 125. By factoring in the railroad vehicle ’ s orientation, on-board rail profile capture system 110 may adjust the rail profile data to account for the vehicle’s movement, ensuring that the TGD reflects the true condition of the track rather than artifacts introduced by the railroad vehicle’s motion. For example, if the vehicle is on an incline, IMU 126 data can be used to differentiate between actual track elevation changes and those perceived due to the angle of the vehicle.

[0080] In some embodiments, IMU 126 may facilitate filtering the rail profile data based on the frequency of the IMU data. High-frequency data may indicate transient vibrations or bumps that are not indicative of the track’s structural integrity, while low-frequency data may reveal long-term track deformations or curvatures. By analyzing the IMU data in conjunction with the rail profile data, on-board rail profile capture system 110 may moreaccurately identify areas of concern that may require further inspection or immediate maintenance.

[0081] In embodiments, IMU 126 may also contributes to on-board rail profde capture system 110’s ability to map the track’s geometry with precision. For example, the inclusion of global positioning system (GPS) data (e.g., within the IMU 126’s functionality or external to IMU 126) allows for the precise geolocation of any detected track defects, facilitating targeted maintenance efforts. This geospatial awareness is particularly beneficial for railway operators managing extensive track networks, enabling them to pinpoint the exact location of potential issues quickly and efficiently. In some embodiments, the GPS data may be provided by a component external and separate from IMU 126, even though the description herein may describe the GPS data as provided by IMU 126. As such, it should be appreciated that the description herein of GPS data as provided by IMU 126 is for illustrative purposes and should not be construed as limiting in any way.

[0082] One or more wheel encoders 127 may be configured to facilitate determining the distance traveled by the railroad vehicle on which on-board rail profile capture system 110 may be mounted, which represents a critical parameter for capturing rail profile data at specified intervals. The precision of one or more wheel encoders 127 ensures that the rail profile data corresponds accurately to each interval (e.g., each foot) of track traveled, providing a detailed and continuous profile of the railway track 115.

[0083] In embodiments, one or more wheel encoders 127 may be mounted onto one or more wheels of the railroad vehicle. One or more wheel encoders 127 may operate by generating a series of pulses as the wheels of the railroad vehicle rotate. Each pulse corresponds to a specific increment of distance traveled, allowing the system to calculate the total distance covered by the vehicle with a high degree of accuracy. This information is thenused by the on-board rail profile capture system 110 to synchronize the capture of rail profile data with the physical location along the track, ensuring that the data collected is spatially accurate and can be correlated with specific sections of the railway.

[0084] The robust design of one or more wheel encoders 127 is tailored to withstand the harsh conditions encountered during rail travel, including vibrations, temperature fluctuations, and exposure to contaminants such as dust and moisture. The reliability of one or more wheel encoders 127 is paramount, as any discrepancies in distance measurements could lead to inaccuracies in the rail profile data and, consequently, in the detection of track defects.

[0085] In addition to their primary function of distance measurement, one or more wheel encoders 127 may also play a role in the system’s overall data integrity. By providing a consistent and reliable metric for distance, one or more wheel encoders 127 enable the system to establish a baseline for the intervals at which rail profile data is captured. This consistency is especially valuable when analyzing long stretches of track, as it ensures that the data is evenly spaced and comprehensive, covering the full extent of the railway with no gaps in the information collected.

[0086] One or more sun shields 128 may be configured and positioned to mitigate the impact of direct sunlight on the rail, which can interfere with the accuracy and reliability of the one or more profile capture sensors 125. For example, one or more sun shields 128 may be strategically designed components of the on-board rail profile capture system 110, serving a specific purpose in ensuring the accuracy and reliability of the rail profile data capture process. One or more sun shields 128 may be positioned to protect the one or more profile capture sensors 125 from the direct interference of sunlight, which can introduce noise and errors into the data collection by causing glare or reflections on the rails of track 115. By effectively blocking these unwanted light sources, one or more sun shields 128 ensure that the profilecapture sensors can operate with maximum precision under a wide range of environmental lighting conditions.

[0087] Direct sunlight can have a detrimental effect on the performance of optical sensors, including the laser modules used in the one or more profde capture sensors 125. The intense light and heat from the sun can cause variations in the laser beam’s reflection, potentially leading to inaccuracies in the rail profde data. One or more sun shields 128 mitigate this risk by providing a physical barrier that blocks or diffuses direct sunlight, ensuring that the laser modules can function without the adverse effects of glare, which could otherwise compromise the quality and precision of the rail profde measurements.

[0088] The configuration of one or more sun shields 128 takes into account the various angles and intensities of sunlight that may be encountered throughout the day and across different seasons and geographical locations. The materials and shape of the sun shields are selected to provide maximum protection while minimizing any additional weight or aerodynamic drag that could affect the railroad vehicle’s performance. The placement of one or more sun shields 128 is carefully calculated to shield the sensors effectively throughout the operational hours, without obstructing one or more profde capture sensors 125’s field of view or interfering with the system’s other components.

[0089] In addition to enhancing the accuracy of the data capture process, one or more sun shields 128 may also contribute to the longevity and maintenance of the on-board rail profde capture system 110. By shielding sensitive optical components from direct sunlight, one or more sun shields 128 may help prevent thermal stress and material degradation, reducing the frequency of maintenance.

[0090] As mentioned above, on-board rail profde capture system 110 may be configured with capabilities and functionality for not only capturing rail profde data, but alsoprocessing the captured rail profile data to calculate and generate TGD. This functionality of on-board rail profile capture system 110 may be provided by the cooperative functionality and operations of data processor 120, TGD calculations manager 121, TGD file generator 122, TGD file transmission manager 123.

[0091] As also mentioned above, the rail profile data associated with a rail of track 115 encompasses a collection of data points, each captured by a respective one of the one or more profile capture sensors 125. These data points are defined as x-y coordinates, with each coordinate representing a specific location along the rail’s profile relative to the vantage point of the profile capture sensor. This capture of coordinates results in a detailed set of data points that collectively outline the contour and dimensions of the rail as perceived by the sensor. In this manner, the rail profile data associated with a rail represents a comprehensive representation of the rail’s profile.

[0092] Data processor 120 may be configured to serves as the initial processing unit for the rail profile data captured by the one or more profile capture sensors 125. As the railroad vehicle traverses the track 115, the left and right sensors collect rail profile data for their respective rails. This data, consisting of a series of x-y coordinates that map the rail’s profile, is then relayed to data processor 120. Data processor 120 may be tasked with the preliminary analysis and refinement of this raw data, preparing it for further processing and the generation of TGD.

[0093] In embodiments, data processor 120 may be configured to detect specific features of the rail, such as the top of the rail head — the uppermost portion of the rail’s head — and the gauge face, which is the interface surface of the rail head that comes into contact with train wheels.

[0094] In embodiments, to accurately identify these distinct parts of the rail’s profile, data processor 120 may be configured to employ template matching techniques. Data processor120 may utilize predefined templates that represent the standard shapes and features of rail sections. By comparing these templates to the captured rail profile data, data processor 120 can effectively pinpoint the top of the rail head and the gauge face within the data. This identification is pivotal for assessing the condition of the rail, as the data for the TGD channels may be calculated off these particular locations of the rails of track 115.

[0095] In embodiments, the use of templates may allow data processor 120 to automate the recognition process, enhancing the efficiency and accuracy of the system. This functionality may ensure that the subsequent TGD generated by TGD calculations manager121 is based on precise identification of the rail’s features, leading to more accurate diagnostics of the track’s condition. By distinguishing these features, data processor 120 contributes to the system’s overall capability to detect anomalies and potential defects that could affect the safety and integrity of the railway infrastructure.

[0096] TGD calculations manager 121 may be configured to transform the rail profile data captured by the one or more profile capture sensors into TGD 111. TGD calculations manager 121 may leverage mathematical and machine learning algorithms to analyze the intricacies of the rail profile data, enabling the calculation of various geometric parameters that define the track’s condition. In this manner, TGD 111 is not merely a singular data point but represents a comprehensive set of data channels, each representing a different geometric aspect of the track 115.

[0097] Each channel of TGD 111 provides insights into specific track characteristics. For example, a first TGD channel may include the gauge channel. The gauge channel may include calculations related to the lateral distance between the right and left rails of track 115.The gauge channel’s calculations may be performed at each predetermined interval of track 115, using the rail profile data captured for that specific location of track 115. In embodiments, the predetermined interval may include an interval anywhere between 6 inches to 5 feet. For example, for a predetermined interval of a foot, gauge measurements may be included in the gauge channel for every foot of track traveled, or each track foot. The data in the gauge channel may be a static measurement, meaning it is independent of the measurements taken at other track feet, providing a snapshot of the track width at each measured point.

[0098] A second TGD channel may include the crosslevel channel. The crosslevel channel may include calculations related to the vertical distance between the top of the right rail and the top of the left rail of track 115, offering a precise vertical profile of the track at each measured point. The data in the crosslevel channel may be particularly useful for identifying any tilting or unevenness that could affect train stability. The crosslevel channel’s calculations may be performed at each predetermined interval of track 115, using the rail profile data captured for that specific location of track 115. For example, for a predetermined interval of a foot, crosslevel measurements may be included in the crosslevel channel for every foot of track traveled, or each track foot. The data in the crosslevel channel may be a static measurement, meaning it is independent of the measurements taken at other track feet, providing a snapshot of the vertical profile of the track at each measured point.

[0099] In contrast to the static measurements (e.g., the gauge and crosslevel channels), some channels may include chordal measurements that require rail profile data over a series of track feet. For example, a third TGD channel may include the alignment channel. The alignment channel may include calculations related to the lateral deviation of the rails of track115 over a specified distance, providing an indication of the track’s straightness.

[0100] A fourth TGD channel may include the surface channel, which may include calculations related to the vertical deviation between the tops of the rails over a distance, identifying any long-term undulations or dips in the track. A fifth TGD channel may include the curvature channel, which may include calculations related to the degree of curvature of the track over a distance, incorporating aggregated rail profile data and potentially GPS data to determine the track’s overall bend. The curvature channel may be particularly relevant for assessing the track’s geometry in curves and ensuring that the curvature falls within safe operational limits.

[0101] TGD calculations manager 121 may be configured to be adaptable, capable of calculating the alignment, surface, and curvature channels over varying distances, from a single foot to several hundred feet (e.g., 500 feet), depending on the requirements of the analysis. This flexibility allows for a detailed understanding of the track’s geometry, facilitating the early detection of potential issues and contributing to the proactive maintenance of the railway infrastructure.

[0102] TGD file generator 122 may be configured to compile and organize the data in the various channels of TGD 111 into a coherent and manageable format. In embodiments, after TGD calculations manager 121 has processed the rail profile data to calculate the data for the various channels of the TGD, TGD file generator 122 generates a structured TGD file that encapsulates this valuable information.

[0103] In embodiments, TGD file generator 122 operates based on a set of configurable parameters that dictate how TGD 111 is to be packaged in the TGD file. These parameters may be tailored to meet specific operational requirements or preferences. For example, one such parameter may specify that the TGD file is to encompass data for a particular distance of travel. This could range from 1 to 50 miles, allowing for flexibility in data segmentation. Ifthe parameter is set for 30 miles, for example, TGD file generator 122 may compile all the data for the different channels of TGD 111 corresponding to that 30-mile stretch of track into a single TGD file. This segmentation facilitates easier handling and analysis of the data by breaking it down into manageable sections that correspond to distinct segments of the railway.

[0104] In addition, or in the alternative to distance-based segmentation, TGD file generator 122 may be configured to create TGD files based on size. This may be particularly useful for ensuring compatibility with data transmission and storage systems that may have limitations on file size. By setting a parameter for the file size, such as a maximum of 1MB to 50MB, TGD file generator 122 may accumulate channel data of TGD 111 until the file reaches the specified size threshold before finalizing the TGD file. This approach ensures that the TGD files are neither too large to handle efficiently nor too small to be meaningful, striking a balance that suits the system’s operational context.

[0105] TGD file transmission manager 123 may be configured to send the TGD file to the backend rail profile processing system for processing, such as to determine defects or maintenance requirements, and / or generate notification and alerts based on the track geometry data. In embodiments, TGD file transmission manager 123 may determine when the TGD file is to be sent to the backend rail profile processing system. For example, TGD file transmission manager 123 may determine that the TGD file is to be transmitted when it has reached a particular size, such as anywhere between 10MB to 50MB. In some cases, the particular size may be related to a particular distance travel. For example, 30 miles traveled may generate a TGD file of approximately 30MB in size. In embodiments, TGD file transmission manager 123 may determine to transmit the TGD file to backend rail profile processing system 150 when it is able, such as when a good signal is present, when the file is complete (e.g., based on a sizeor distance traveled), or when it is beneficial to send it, such as when enough data has been collected to make the processing of the TGD file meaningful.

[0106] TGD file transmission manager 123 may be configured to manage the timely and efficient transmission of the TGD files including TGD 111 to backend rail profile processing system 150. TGD file transmission manager 123 ensures that the data collected by the on-board system is promptly sent for further analysis, which is pivotal for the detection of defects and the initiation of maintenance actions.

[0107] In embodiments, TGD file transmission manager 123 may be configured to intelligently determine the optimum moments or points for data transmission. For example, TGD file transmission manager 123 may evaluate several factors before initiating the transfer of TGD files. In one embodiment, one of the primary considerations is the size of the TGD file. TGD file transmission manager 123 may be configured to recognize when a file has reached a pre-determined size, which can be set anywhere between 10MB to 50MB, and transmit the file once this threshold is met. This size often correlates with a specific distance traveled by the railroad vehicle, such as approximately 30MBs for approximately 30 miles traveled. This correlation ensures that the data is sent in substantial yet manageable segments that represent a meaningful stretch of the railway track.

[0108] Moreover, TGD file transmission manager 123 may be configured to account for the quality of the network connection. For example, TGD file transmission manager 123 may prioritize the transmission of TGD files when a robust network signal is available, reducing the risk of data corruption or loss during transfer. This consideration is particularly relevant for railway tracks located in remote or challenging environments where network connectivity may be intermittent.

[0109] TGD file transmission manager 123 may also assesses whether the TGD file is complete based on the set parameters, such as the specified size or distance traveled. This ensures that the backend system receives comprehensive data for each segment of the track, allowing for a thorough analysis of the track’s condition. Additionally, TGD file transmission manager 123 may be configured to send the TGD file when it is deemed beneficial, such as when enough data has been collected to provide a meaningful analysis of the track’s geometry and condition.

[0110] TGD file transmission manager 123 plays a central role in the data management process of the railway track monitoring system. By intelligently determining the timing and conditions for data transmission, TGD file transmission manager 123 ensures that backend rail profile processing system 150 receives TGD 111 in a timely, reliable, and organized manner, enabling the effective monitoring and maintenance of the railway infrastructure.

[0111] FIG. 3A show an example configuration of an on-board rail profile capture system 110 mounted to the underside of a railroad vehicle in accordance with embodiments of the present disclosure. As illustrated in FIG. 3A, on-board rail profile capture system 110 may be mounted and strategically positioned to have a direct line of sight to the railroad track 115, while being enabled to capture rail profile data effectively as the vehicle moves along track 115. FIG. 3B shows the example configuration of the on-board system 110 mounted to the underside of a railroad vehicle with the railroad vehicle removed to offer a clearer perspective of the system’s components and their arrangement in accordance with embodiments of the present disclosure.

[0112] Prominently featured in FIGS. 3A and 3B is main enclosure assembly 350 of on-board rail profile capture system 110. As shown, main enclosure assembly 350 may be securely mounted to the underside of a railroad vehicle, which could be a locomotive or anytype of train car. This strategic positioning is integral to the system’s operation, as it allows for the continuous and dynamic capture of rail profile data while the vehicle is in motion, traversing the railroad track.

[0113] In embodiments, main enclosure assembly 350 oriented such that it directly targets the rails, enabling the precise capture of the rail profile for both the left and right rails of the track. This configuration may be configured to ensure that the capture of the rail profile can be carried out without interrupting the regular flow of rail traffic. As the railroad vehicle moves along the track, on-board rail profile capture system 110 may continuously monitor the condition of the rails, collecting data that will later be used to assess the track’s integrity and identify any potential defects.

[0114] As mentioned above, main enclosure assembly 350 serves as the protective housing for some of on-board rail profile capture system 110’s core components. Main enclosure assembly 350 may be configured to be both lightweight and durable, capable of withstanding the high-speed travel and environmental conditions encountered during operation.

[0115] Also shown are one or more wheel encoders 127 mounted to a wheel of the railroad vehicle. As described herein, one or more wheel encoders 127 are integral to on-board rail profile capture system 110’s ability to measure the distance traveled by the railroad vehicle. One or more wheel encoders 127 may generate pulses corresponding to the rotation of the vehicle’s wheels, allowing the system to capture rail profile data at precise intervals (e.g., at every foot of track traveled).

[0116] Also shown are one or more sun shields 128, which may be placed at locations configured to prevent direct sunlight from interfering with the data capture process. These sun shields are an example of the system’s attention to detail, ensuring that the profile capturesensors can operate without the adverse effects of glare or reflections that could compromise the accuracy of the rail profde data.

[0117] FIG. 3C shows a perspective view of a main enclosure assembly 350 of the onboard rail profile capture system 110 configured in accordance with embodiments of the present disclosure. In embodiments, main enclosure assembly 350 may be configured as a robust housing that secures and protects the internal components of the on-board system, ensuring their functionality under the dynamic conditions of a moving railroad vehicle.

[0118] In embodiments, main enclosure assembly 350 may be configured with a durable exterior that is capable of withstanding the harsh environmental conditions typically encountered during rail operations, such as vibrations, temperature fluctuations, and potential impacts from debris.

[0119] In embodiments, main enclosure assembly 350 may be configured to ensure that the one or more sensors 125 may be aligned at such an angle as to optimize the one or more sensors 125 ’s field of view, ensuring comprehensive coverage of the rail’s profile while also positioning the system high enough under the railroad vehicle to shield it from debris. This is particularly relevant as the vehicle travels at high speeds, where the likelihood of encountering flying debris is increased. The strategic positioning of the enclosure not merely facilitates the protection of the system but also ensures that the quality of the data is not compromised by external factors.

[0120] Also shown in FIG. 3C is connector panel 370, which may operate as the central hub for electrical and data connections. Connector panel 370 may be strategically positioned on the main enclosure 350 to facilitate the easy and organized connection of peripherals and the one or more profile capture sensors 125. In embodiments, connector panel 370 may be configured to accommodate a variety of connectors, allowing for the integration of additionalsensors, power supply lines, and communication interfaces that are integral to the operation of on-board rail profde capture system 110.

[0121] In embodiments, the configuration of main enclosure assembly 350 may be modular. The modular configuration of main enclosure assembly 350 may allow the components housed within main enclosure assembly 350 to be easily removed, replaced, or upgraded as technology advances or as maintenance requires. This modular configuration ensures that the system can be kept in peak operating condition with minimum downtime, which is paramount for the continuous monitoring of railway tracks.

[0122] For example, FIG. 3D presents an exploded view of the main enclosure assembly 350 configured with a modular configuration in accordance with embodiments of the present disclosure. FIG. 3D also shows the individual components of on-board rail profile capture system 110 that are housed within the main enclosure assembly 350, such as one or more sensors 125, connector panel 370, and main computer 360. In embodiments, shell 352 may provide the structural framework that enables the configuration of main enclosure assembly 350. The exploded view of FIG. 3D provides a detailed breakdown of the main enclosure assembly 350, demonstrating how each component fits together and the ease with which parts can be accessed, serviced, or replaced.

[0123] FIG. 4 shows a high-level flow diagram 400 of operation of a system configured for providing functionality for railway track monitoring and defect detection in accordance with embodiments of the present disclosure. For example, the functions illustrated in the example blocks shown in FIG. 4 may be performed by system 100 of FIG. 1 according to embodiments herein. In embodiments, the operations of the method 400 may be stored as instructions that, when executed by one or more processors, cause the one or more processors to perform the operations of the method 400.

[0124] At block 402, rail profile data is captured by one or more profile capture sensors of an on-board rail profile capture system mounted to a railroad vehicle at predetermined intervals as the railroad vehicle traverses a railroad track. In embodiments, the rail profile data is associated with one or more rails of the railroad track. In embodiments, functionality of one or more profile capture sensors (e.g., one or more profile capture sensors 125 as illustrated in FIG. 2) may be used to capture rail profile data at predetermined intervals as the railroad vehicle traverses a railroad track. In embodiments, the one or more profile capture sensors may perform operations to capture rail profile data at predetermined intervals as the railroad vehicle traverses a railroad track according to operations and functionality as described above with reference to one or more profile capture sensors 125 and as illustrated in FIGS. 1-3D.

[0125] At block 404, a GPS location of the railroad vehicle at one or more of the predetermined intervals is determined based on a GPS module. In embodiments, functionality of IMU (e.g., IMU 126 as illustrated in FIG. 2) may be used to determine, based on a GPS module, a GPS location of the railroad vehicle at one or more of the predetermined intervals. In embodiments, the operations server may perform operations to determine, based on a GPS module, a GPS location of the railroad vehicle at one or more of the predetermined intervals according to operations and functionality as described above with reference to IMU 126 and as illustrated in FIGS. 1-3D.

[0126] At block 406, a plurality of data channels related to the geometry of the railroad track is calculated based on the captured rail profile data. In embodiments, each data channel of the plurality of data channels is associated with a different feature of the geometry of the railroad track. In embodiments, at least one of the plurality of data channels is calculated based on the determined GPS location of the railroad vehicle. In embodiments, functionality of aTGD calculations manager (e.g., TGD calculations manager 121 as illustrated in FIG. 2) maybe used to calculate, based on the captured rail profile data, a plurality of data channels related to the geometry of the railroad track. In embodiments, the TGD calculations manager may perform operations to calculate, based on the captured rail profile data, a plurality of data channels related to the geometry of the railroad track according to operations and functionality as described above with reference to TGD calculations manager 121 and as illustrated in FIGS. 1-3D.

[0127] At block 408, TGD is generated based on the data calculated for the plurality of data channels. In embodiments, functionality of a TGD calculations manager (e.g., TGD calculations manager 121 as illustrated in FIG. 2) may be used to generate TGD based on the data calculated for the plurality of data channels. In embodiments, the TGD calculations manager may perform operations to generate TGD based on the data calculated for the plurality of data channels according to operations and functionality as described above with reference to TGD calculations manager 121 and as illustrated in FIGS. 1-3D.

[0128] At block 410, the TGD is transmitted from the on-board rail profile capture system to a backend rail profile processing system via a network. In embodiments, the TGD is processed at the backend rail profile processing system to determine defects on the railroad track and to generate track defect reports and notifications based on the determined defects. In embodiments, functionality of a TGD file transmission manager (e.g., TGD file transmission manager 123 as illustrated in FIG. 2) may be used to transmit the TGD from the on-board rail profile capture system to a backend rail profile processing system via a network. In embodiments, the TGD file transmission manager may perform operations to transmit the TGD from the on-board rail profile capture system to a backend rail profile processing system via a network according to operations and functionality as described above with reference to TGD file transmission manager 123 and as illustrated in FIGS. 1-3D.

[0129] Persons skilled in the art will readily understand that advantages and objectives described above would not be possible without the particular combination of computer hardware and other structural components and mechanisms assembled in this inventive system and described herein. Additionally, the algorithms, methods, and processes disclosed herein improve and transform any general-purpose computer or processor disclosed in this specification and drawings into a special purpose computer programmed to perform the disclosed algorithms, methods, and processes to achieve the aforementioned functionality, advantages, and objectives. It will be further understood that a variety of programming tools, known to persons skilled in the art, are available for generating and implementing the features and operations described in the foregoing. Moreover, the particular choice of programming tool(s) may be governed by the specific objectives and constraints placed on the implementation selected for realizing the concepts set forth herein and in the appended claims.

[0130] The description in this patent document should not be read as implying that any particular element, step, or function can be an essential or critical element that must be included in the claim scope. Also, none of the claims can be intended to invoke 35 U.S.C. § 112(f) with respect to any of the appended claims or claim elements unless the exact words “means for” or “step for” are explicitly used in the particular claim, followed by a participle phrase identifying a function. Use of terms such as (but not limited to) “mechanism,” “module,” “device,” “unit,” “component,” “element,” “member,” “apparatus,” “machine,” “system,” “processor,” “processing device,” or “controller” within a claim can be understood and intended to refer to structures known to those skilled in the relevant art, as further modified or enhanced by the features of the claims themselves, and can be not intended to invoke 35 U.S.C. § 112(f). Even under the broadest reasonable interpretation, in light of this paragraph of this specification, the claims are not intended to invoke 35 U.S.C. § 112(f) absent the specific language described above.

[0131] The disclosure may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. For example, each of the new structures described herein, may be modified to suit particular local variations or requirements while retaining their basic configurations or structural relationships with each other or while performing the same or similar functions described herein. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive. Accordingly, the scope of the disclosure can be established by the appended claims. All changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Further, the individual elements of the claims are not well-understood, routine, or conventional. Instead, the claims are directed to the unconventional inventive concept described in the specification.

[0132] Those of skill in the art would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure. Skilled artisans will also readily recognize that the order or combination of components, methods, or interactions that are described herein are merely examples and that the components, methods, or interactions of the various embodiments of the present disclosure may be combined or performed in ways other than those illustrated and described herein.

[0133] Functional blocks and modules in FIGS. 1-4 may comprise processors, electronics devices, hardware devices, electronics components, logical circuits, memories, software codes, firmware codes, etc., or any combination thereof. Consistent with the foregoing, various illustrative logical blocks, modules, and circuits described in connection with the disclosure herein may be implemented or performed with a general -purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0134] The steps of a method or algorithm described in connection with the disclosure herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal, base station, a sensor, or any other communication device. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0135] In one or more exemplary designs, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. Computer-readable storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general- purpose or special-purpose computer, or a general -purpose or special-purpose processor. Also, a connection may be properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL), then the coaxial cable, fiber optic cable, twisted pair, or DSL, are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0136] Although the present disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the disclosure as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means,methods, and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the disclosure of the present disclosure, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the present disclosure. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.

Claims

CLAIMSWhat is claimed is:

1. A method of capturing and processing rail profde data, comprising: capturing, by one or more profde capture sensors of an on-board rail profde capture system mounted to a railroad vehicle, rail profde data at predetermined intervals as the railroad vehicle traverses a railroad track, wherein the rail profde data is associated with one or more rails of the railroad track; determining, based on a global positioning system (GPS) module, a GPS location of the railroad vehicle at one or more of the predetermined intervals; calculating, based on the captured rail profde data, a plurality of data channels related to the geometry of the railroad track, wherein each data channel of the plurality of data channels is associated with a different feature of the geometry of the railroad track, wherein at least one of the plurality of data channels is calculated based on the determined GPS location of the railroad vehicle; generating track geometry data (TGD) based on the data calculated for the plurality of data channels; and transmitting the TGD from the on-board rail profde capture system to a backend rail profde processing system via a network, wherein the TGD is processed at the backend rail profde processing system to determine defects on the railroad track and to generate track defect reports and notifications based on the determined defects.

2. The method of claim 1, wherein the plurality of data channels includes one or more of: a gauge channel representing a lateral distance between the one or more rails of the railroad track; anda crosslevel channel representing vertical distance between a top of a first rail of the one or more rails of the railroad track and a top of a second rail of the one or more rails of the railroad track, wherein each of the gauge channel and the crosslevel channel is calculated at each predetermined interval of the predetermined intervals.

3. The method of claim 1, wherein the plurality of data channels includes one or more of: an alignment channel representing lateral deviation between the one or more rails of the railroad track over a traveled distance; a surface channel representing vertical deviation over between the top of the first rail of the one or more rails of the railroad track and the top of the second rail of the one or more rails of the railroad track over the traveled distance; and a curvature channel representing a degree of curvature of the railroad track over the traveled distance, wherein each of the alignment channel, the surface channel, and the curvature channel is calculated based on aggregated rail profile data captured over multiple predetermined intervals of the predetermined intervals.

4. The method of claim 1, wherein the one or more profile capture sensors include one or more laser modules, each laser module of the one or more laser modules configured to project a laser beam onto a respective rail of the one or more rails and capture reflections of the laser beam to define the rail profile data of the respective rail as a series of reflection points.

5. The method of claim 1, further comprising: utilizing an inertial measurement unit (IMU) to capture rates and angles of the orientation of the railroad vehicle, wherein the IMU data is used to refine the rail profile data based on the orientation of the vehicle.

6. The method of claim 1, wherein the predetermined intervals correspond to a specific distance traveled by the railroad vehicle, and the distance is determined using one or more wheel encoders mounted to the railroad vehicle.

7. The method of claim 1, wherein transmitting the TGD from the on-board rail profile capture system to the backend rail profile processing system via the network includes: transmitting the TGD is transmitted to the backend rail profile processing system when a robust network signal is available to ensure reliable data transfer.

8. The method of claim 1, wherein generating the TGD includes: organizing the TGD into TGD files based on one or more file configuration parameters, wherein the one or more file configuration parameters include a parameter indicating that the file is to include TGD data for a particular distance of travel or until the file reaches a particular size.

9. A system configured for capturing and processing rail profile data, comprising: at least one processor; anda memory operably coupled to the at least one processor and storing processor- readable code that, when executed by the at least one processor, is configured to perform operations including: capturing, by one or more profile capture sensors of an on-board rail profile capture system mounted to a railroad vehicle, rail profile data at predetermined intervals as the railroad vehicle traverses a railroad track, wherein the rail profile data is associated with one or more rails of the railroad track; determining, based on a global positioning system (GPS) module, a GPS location of the railroad vehicle at one or more of the predetermined intervals; calculating, based on the captured rail profile data, a plurality of data channels related to the geometry of the railroad track, wherein each data channel of the plurality of data channels is associated with a different feature of the geometry of the railroad track, wherein at least one of the plurality of data channels is calculated based on the determined GPS location of the railroad vehicle; generating track geometry data (TGD) based on the data calculated for the plurality of data channels; and transmitting the TGD from the on-board rail profile capture system to a backend rail profile processing system via a network, wherein the TGD is processed at the backend rail profile processing system to determine defects on the railroad track and to generate track defect reports and notifications based on the determined defects.

10. The system of claim 9, wherein the plurality of data channels includes one or more of: a gauge channel representing a lateral distance between the one or more rails of the railroad track; anda crosslevel channel representing vertical distance between a top of a first rail of the one or more rails of the railroad track and a top of a second rail of the one or more rails of the railroad track, wherein each of the gauge channel and the crosslevel channel is calculated at each predetermined interval of the predetermined intervals.

11. The system of claim 9, wherein the plurality of data channels includes one or more of: an alignment channel representing lateral deviation between the one or more rails of the railroad track over a traveled distance; a surface channel representing vertical deviation over between the top of the first rail of the one or more rails of the railroad track and the top of the second rail of the one or more rails of the railroad track over the traveled distance; and a curvature channel representing a degree of curvature of the railroad track over the traveled distance, wherein each of the alignment channel, the surface channel, and the curvature channel is calculated based on aggregated rail profile data captured over multiple predetermined intervals of the predetermined intervals.

12. The system of claim 9, wherein the one or more profile capture sensors include one or more laser modules, each laser module of the one or more laser modules configured to project a laser beam onto a respective rail of the one or more rails and capture reflections of the laser beam to define the rail profile data of the respective rail as a series of reflection points.

13. The system of claim 9, wherein the operations further include: utilizing an inertial measurement unit (IMU) to capture rates and angles of the orientation of the railroad vehicle, wherein the IMU data is used to refine the rail profile data based on the orientation of the vehicle.

14. The system of claim 9, wherein the predetermined intervals correspond to a specific distance traveled by the railroad vehicle, and the distance is determined using one or more wheel encoders mounted to the railroad vehicle.

15. The system of claim 9, wherein transmitting the TGD from the on-board rail profile capture system to the backend rail profile processing system via the network includes: transmitting the TGD is transmitted to the backend rail profile processing system when a robust network signal is available to ensure reliable data transfer.

16. The system of claim 9, wherein generating the TGD includes: organizing the TGD into TGD files based on one or more file configuration parameters, wherein the one or more file configuration parameters include a parameter indicating that the file is to include TGD data for a particular distance of travel or until the file reaches a particular size.

17. A computer-based tool for capturing and processing rail profile data, the computer-based tool including non-transitory computer readable media having stored thereon computer code which, when executed by a processor, causes a computing device to perform operations comprising:capturing, by one or more profile capture sensors of an on-board rail profile capture system mounted to a railroad vehicle, rail profile data at predetermined intervals as the railroad vehicle traverses a railroad track, wherein the rail profile data is associated with one or more rails of the railroad track; determining, based on a global positioning system (GPS) module, a GPS location of the railroad vehicle at one or more of the predetermined intervals; calculating, based on the captured rail profile data, a plurality of data channels related to the geometry of the railroad track, wherein each data channel of the plurality of data channels is associated with a different feature of the geometry of the railroad track, wherein at least one of the plurality of data channels is calculated based on the determined GPS location of the railroad vehicle; generating track geometry data (TGD) based on the data calculated for the plurality of data channels; and transmitting the TGD from the on-board rail profile capture system to a backend rail profile processing system via a network, wherein the TGD is processed at the backend rail profile processing system to determine defects on the railroad track and to generate track defect reports and notifications based on the determined defects.

18. The computer-based tool of claim 17, wherein the plurality of data channels includes one or more of: a gauge channel representing a lateral distance between the one or more rails of the railroad track; and a crosslevel channel representing vertical distance between a top of a first rail of the one or more rails of the railroad track and a top of a second rail of the one or more rails of the railroad track,wherein each of the gauge channel and the crosslevel channel is calculated at each predetermined interval of the predetermined intervals.

19. The computer-based tool of claim 17, wherein the plurality of data channels includes one or more of: an alignment channel representing lateral deviation between the one or more rails of the railroad track over a traveled distance; a surface channel representing vertical deviation over between the top of the first rail of the one or more rails of the railroad track and the top of the second rail of the one or more rails of the railroad track over the traveled distance; and a curvature channel representing a degree of curvature of the railroad track over the traveled distance, wherein each of the alignment channel, the surface channel, and the curvature channel is calculated based on aggregated rail profile data captured over multiple predetermined intervals of the predetermined intervals.

20. The computer-based tool of claim 17, wherein the one or more profile capture sensors include one or more laser modules, each laser module of the one or more laser modules configured to project a laser beam onto a respective rail of the one or more rails and capture reflections of the laser beam to define the rail profile data of the respective rail as a series of reflection points.

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