Use of artificial intelligence to detect defects in trains and method to use

Abstract

It is imperative that the integrity of a train and its railcars be maintained. A defective part or parts on a train may cause the derailment of the train and its railcars. A derailment often causes serious personal and property damage. The use of artificial intelligence that has been applied to this system will detect possible flaws or defects in the railroad car. A portal through which the train will pass houses cameras and a means of illumination to take high resolution images of the train through the portal. This information is then stored on a software platform. It has a feature which can include a human in the loop process that verifies the accuracy of the information. The solution also transmits detected defect images and information to a remote location for analysis.

Claims

1. The use of artificial intelligence to detect anomalies in moving trains, which is comprised of a portal, wherein the portal has defined sides and a defined top, wherein the portal is a structure that surrounds the tracks, wherein a train passes through the portal, a plurality of cameras, wherein the plurality of cameras is mounted to the portal and/or to the surface below the train, wherein the plurality of cameras capture high resolution images, a means of illumination, wherein the means of illumination is mounted to the portal and/or to the surface below the train, a speed detection device, wherein the speed detection device is linked to the plurality of cameras, wherein the speed detection device determines the shutter speed of the plurality of cameras, an identification tag, wherein the identification tag is attached to the railcar, wherein the identification tag contains the information about a specific railcar, a storage device, wherein the storage device houses the captured images, wherein the storage device houses information about the railcars, a plurality of models of railroad cars, wherein models of railroad cars are uploaded to the storage device, algorithms, wherein algorithms are developed to detect areas of concern, said algorithms are incorporated into the storage device, a human in the loop step, wherein a human in the loop validation is provided, wherein the human in the loop validates detection images that can be used to augment existing training sets, wherein the augmented training sets improve the machine learning models, wherein results of areas of concern can be transmitted to a remote location, wherein the results are transmitted in near real time.

2. The use of artificial intelligence to detect anomalies in moving trains as described in claim 1 wherein the plurality of cameras are line scan cameras.

3. The use of artificial intelligence to detect anomalies in moving trains as described in claim 1 wherein the plurality of cameras are area scan cameras.

4. The use of artificial intelligence to detect anomalies in moving trains as described in claim 1 wherein the means of illumination is LED lighting.

5. The use of artificial intelligence to detect anomalies in moving trains as described in claim 1 wherein the means of illumination is stadium lighting.

6. The use of artificial intelligence to detect anomalies in moving trains as described in claim 1 wherein the plurality of models depict different types of railroad cars.

7. A method to use artificial intelligence to detect anomalies in moving trains which is comprised of the following steps: a. providing a plurality of cameras to capture high resolution images of a moving train, b. providing illumination proximate to the plurality of cameras, c. providing a means to detect the speed of the train, d. linking the speed of the train with the shutter speed of the plurality of the cameras, e. capturing detection images of the moving train, f. classifying detection images of the moving train, f. storing the detection images on a software platform, g. downloading images of model railcars on the software platform, h. comparing the captured detection images of the railcars with the stored images of model railcars on the software platform, i. analyzing the detection images by a human in the loop to the stored images in the software, k. transmitting the data to a remote location.

8. The method to use artificial intelligence to detect anomalies in moving trains as described in claim 7 which is further comprised of model retraining.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The FIGURE is a schematic of the components of this device.

NUMBERING REFERENCE

(2) 15 Models of Railcars 20 Artificial Intelligence 25 Speed Detection Apparatus 30 Storage Device 35 Human In The Loop (HITL) 40 Illumination 45 Cameras 50 Identification Tags 55 Area Scan Cameras 60 Line Scan Cameras

DETAILED DESCRIPTION OF THE EMBODIMENTS

(3) A train is comprised of an engine or locomotive and a plurality of railroad cars. During normal train operations, an engineer will operate the locomotive in the forward most car; this forward placement makes it extremely difficult if not impossible for the operator to view the other railroad cars that comprise the train. The operator cannot view the components of the train on the underside of the train. During normal operation the train will travel on a set of tracks from the point of origin to the point of destination at relatively high speeds in remote locations.

(4) There are many different types of railroad cars that form a train. Each railroad car has an identification tag 50 that will store the characteristics of that specific railroad car. Some of the characteristics that may be stored include the weight of the car when empty, the connection points, the location of hoses or hopper doors, the positions of valves or latches and other components of the railroad car to name a few. This unique identifying information tag is scanned and stored in the system. Models of railcars are also stored in the system and will be used to compare with the captured images of the actual railcars on the train.

(5) As the train travels along the tracks the speed of the train is detected by a speed detection device. The speed detection device determines the shutter speed of the cameras as the train moves through a portal. The speed detection device will activate at a predetermined point before the train enters the portal. A plurality of sensors (not depicted) on the rail determine the speed of the wheel. The speed detection device 25 calculates the speed of the train and the speed of the train determines the shutter speed of the cameras which are used to capture the images. The speed detection device is linked to the cameras and the lighting to enable the capture of the images of the train as it moves through the portal.

(6) As the train moves along the tracks it will pass through a portal, which is a large structure on which cameras 45 and lighting 40 are mounted; cameras and lighting may also be provided on the ground below the train. The portal extends over the outside of the tracks on both sides of the tracks.

(7) As the train passes through the portal, a plurality of cameras, 45, capture high resolution images of the surfaces of the train. The cameras may be line scan cameras 60 or area scan cameras 55 depending on the specific needs of the user. A means of illumination 40 is also provided to ensure that high resolution images are captured in all lighting conditions. The means of illumination may be comprised of LED lights or stadium lighting depending on the specific needs of the user or the environment that is encountered.

(8) The captured images include all sides of the train including the bottom, top, and all sides of the train as well as the connection points between the individual railroad cars. These captured images are stored in the storage devices 30 that is part of this application.

(9) The system uses artificial intelligence supervised machine learning to detect areas of concern for railcars. Models of train cars 15 are downloaded into the system. Each of the models have the specific dimensions and characteristics of the specific types of train cars. These Artificial Intelligence Model 15 images of railroad train cars are stored in the system and are used to compare to the actual captured images of a specific type of railcar. Each railcar has a unique identification tag that is scanned as the train moves through the portal; this identification tag contains the information about the specific railcar. The information that is gathered from the unique identification tag about a specific railcar may include hose placement, cotter pin placement, or bearing placement that is specific to that type of railcar. The information about the specific railcar is then compared to the stored models of train cars. Models of the types of railcar that have been stored in the system are compared to the actual captured images. For instance, if the railcar that is scanned is a hopper car, stored images of hopper cars are compared to the captured images. The system detects defects on both freight train cars as well as railcars used for passenger rail service.

(10) As the train moves along the tracks, the speed detection system 25 will activate and calculate the speed of the train. The speed of the train is used to activate the acquisition rate of the cameras. The train will pass through the portal and the cameras will capture high-resolution images and store them into the system.

(11) Algorithms are developed for each type of defect, and the algorithms are used to detect defects or areas of concern in the captured images. Possible areas of concern may include hose placement, cotter pin placement and brake or wheel wear and tear. Specific algorithms that are directed to specific areas of concern are used depending on the component of the railroad car to detect and analyze specific areas of concern. The system catalogs the images and then forwards them to the human in the loop (HITL) system, 35. The HITL system validates the results of the analyzed data; a human will review the images and data to determine the validity of the information that has been captured and compared to the stored images. The human in the loop system and process, when enabled, can be used to increase the precision of the information that is being provided to the railcar operator. In addition, the validated images of defects are used to increase the accuracy of the machine learning models through supervised machine learning; as more data becomes available additional algorithms are developed to better detect defects.

(12) The stored information can be transmitted in real time to a remote location for analysis.

(13) While the embodiments of the invention have been disclosed, certain modifications may be made by those skilled in the art to modify the invention without departing from the spirit of the invention.