Rail shape monitoring system and rail shape monitoring method

Abstract

An objective of the present invention is to enable a rail shape to be monitored more accurately. This rail shape monitoring system comprises: an input unit that accepts input of data corresponding to the actual surface shape of a rail; a storage unit that stores reference shape data including a reference shape of the rail; and a processing unit. The processing unit calculates a shape relationship between a detected shape based on the data corresponding to the actual surface shape of the rail and the reference shape, sets one of the detected shape and the reference shape as a shape to be converted and sets the other as a shape for comparison, converts the shape to be converted into a converted shape for monitoring on the basis of the shape relationship, and outputs monitoring data on the basis of the converted shape for monitoring.

Description

Rail Shape Monitoring System and Rail Shape Monitoring Method

[0001] This disclosure relates to a technique for monitoring the shape of a rail.

[0002] In Patent Document 1, two two-dimensional captured images obtained by capturing the left and right sides of a contour line with two CCD cameras are subjected to three-dimensional processing to correspond to the cross-sectional shape of a rail, and the processed left and right images are combined by aligning any one leg measurement point of the rail leg in the captured image and the jaw measurement point of the rail at symmetric positions sandwiching the center line of the rail width or by aligning them with a reference image of a reference rail.

[0003] Japanese Patent Application Laid-Open No. 2006-258531

[0004] However, the imaging positions of the CCD cameras with respect to the rail may vary. Therefore, simply subjecting the two captured images to three-dimensional processing and combining the processed left and right images using the leg measurement point and the jaw measurement point may not sufficiently take into account the variation in the imaging positions. There is a demand to enable more accurate monitoring of the rail shape.

[0005] Therefore, an object of this disclosure is to enable more accurate monitoring of the rail shape.

[0006] A rail shape monitoring system includes an input unit to which data corresponding to the actual surface shape of a rail is input, a storage unit that stores reference shape data including the reference shape of the rail, and a processing unit that calculates the shape relationship between a detected shape based on the data corresponding to the actual surface shape of the rail and the reference shape, sets one of the detected shape and the reference shape as a conversion target shape and the other as a comparison target shape, converts the conversion target shape into a monitoring conversion shape based on the shape relationship, and outputs monitoring data based on the monitoring conversion shape.

[0007] Another rail shape monitoring system includes an input unit that receives data corresponding to the actual surface shape of the rail, a storage unit that stores reference shape data including the reference shape of the rail, and a processing unit that calculates the shape relationship between the detected shape based on the data corresponding to the actual surface shape of the rail and the reference shape, sets one of the detected shape and the reference shape as the shape to be converted and the other as the shape to be compared, converts the shape to be converted based on the shape relationship into a monitoring converted shape, and identifies the amount of wear on the rail by comparing the comparison shape and the monitoring converted shape.

[0008] The rail shape monitoring method acquires data corresponding to the actual surface shape of the rail, acquires reference shape data including the reference shape of the rail, calculates the shape relationship between the detected shape based on the data corresponding to the actual surface shape of the rail and the reference shape, uses one of the detected shape and the reference shape as the shape to be converted and the other as the shape to be compared, converts the shape to be converted into a monitoring conversion shape based on the shape relationship, and outputs monitoring data based on the monitoring conversion shape.

[0009] According to this disclosure, the shape relationship between the detected shape and the reference shape is calculated, so that the shape to be transformed matches the detected shape and the reference shape, and monitoring data is output based on the transformed shape for monitoring. During or after processing of the monitoring data, the transformed shape for monitoring is compared with the comparison target shape, allowing the rail shape to be monitored more accurately.

[0010] Figure 1 is a block diagram showing the overall configuration of a rail shape monitoring system according to an embodiment. Figure 2 is a block diagram showing a rail condition acquisition device. Figure 3 is a diagram showing an example of a reference shape. Figure 4 is a diagram showing an example of a detected shape. Figure 5 is an explanatory diagram showing the attitude of a railway vehicle and sensor in a curved section of the track. Figure 6 is a flowchart showing an example of processing in the rail shape monitoring device. Figure 7 is a diagram showing an example of setting a reference point. Figure 8 is a diagram showing an example of a converted shape for monitoring after shape conversion. Figure 9 is a diagram showing an image in which the converted shape for monitoring and the detected shape are superimposed. Figure 10 is a diagram showing an example of display on a display device. Figure 11 is a flowchart showing an example of processing according to a modified example. Figure 12 is a diagram showing an image in which the converted shape for monitoring and the reference shape according to a modified example are superimposed.

[0011] The following describes a rail shape monitoring system and rail shape monitoring method according to an embodiment. Figure 1 is a block diagram showing the overall configuration of the rail shape monitoring system.

[0012] An example of a rail 12 to be monitored by this system will be described. A railway vehicle 20 travels on the track 10. The track 10 is a path that guides the railway vehicle 20 along a predetermined route. The track 10 includes two rails 12, 12. The two rails 12, 12 are fixed on the laying surface 18. The rails 12, 12 may also be fixed on the laying surface 18 via sleepers 19. The laying surface 18 may be the surface of the land, the lower surface inside a tunnel, or the upper surface of a bridge such as a bridge or elevated bridge.

[0013] The railway vehicle 20 comprises a car body 22 and a bogie 24. The bogie 24 comprises a bogie frame 25 and a plurality of wheels 25W. The plurality of wheels 25W are rotatably supported on the left and right portions of the bogie frame 25 via axle portions. In this embodiment, the direction of travel of the railway vehicle 20 may be referred to as the front, and the direction of reversal as the rear. Also, left or right may be referred to as the left or right when viewed from the railway vehicle 20 in the direction of travel. In the direction of gravity, the side on which gravity acts may be referred to as the lower side, and the opposite side as the upper side. Each of the left and right wheels 25W runs on the rails 12, guided by the rails 12. The bogie 24 supports the car body 22 from below. As the bogie 24 runs on the track 10, the railway vehicle 20, including the car body 22, runs along the track 10. In other words, the rails 12 support the wheels 25W so that they can run and guide the wheels 25W.

[0014] The railway vehicle 20 can be any vehicle that runs on the track 10, and may be an electric train, a locomotive or freight car for a freight train, or a locomotive or passenger car for a passenger train. A freight car or passenger car may be an unattended car towed by a locomotive, or it may be a powered car that has its own power. A locomotive may be an electric locomotive or an internal combustion locomotive such as a diesel locomotive. The railway vehicle 20 may be a commercial vehicle for transporting people or goods, or it may be a service vehicle for monitoring track conditions. The railway vehicle 20 may be a rail-road vehicle that can run on both tracks and roads.

[0015] The rail 12 is worn down by contact with or sliding against the wheel 25W. As the wear of the rail 12 progresses, the sides of the rail head may wear down. When the sides of the rail head wear down, the track gauge may widen. The track gauge is the distance between the left and right rails 12. In addition, the top surface of the rail 12 may wear down. As the top surface of the rail 12 wears down, the rigidity of the rail 12 may decrease.

[0016] Therefore, it is desirable to periodically measure the shape of the rail 12 and determine the amount of wear on the rail 12. This embodiment relates to a technique for monitoring the shape of the rail 12.

[0017] The rail shape monitoring system 30 is a system for monitoring the shape of the rail 12, and comprises a rail condition acquisition device 40 and a rail shape monitoring device 70.

[0018] The rail condition acquisition device 40 is a device for acquiring the shape of the rail 12. In this embodiment, the rail condition acquisition device 40 comprises a sensor 42, a running position detection unit 44, a rail condition information generation device 46, and a communication device 48. The rail condition acquisition device 40 is mounted on the railway vehicle 20.

[0019] Sensor 42 is a sensor that detects the actual surface shape of the rail 12. Sensor 42 may detect the actual surface shape of the rail 12 while the railway vehicle 20 is in motion. Sensor 42 may also detect the actual shape of the upper surface and the actual shapes of the surfaces on both sides of the rail 12.

[0020] The running position detection unit 44 detects the running position of the railway vehicle 20 on the track 10.

[0021] The outputs of the sensor 42 and the running position detection unit 44 are input to the rail condition information generation device 46. The sensor 42 is associated with either the right or left rail 12. Therefore, if the timing of the sensor 42's detection of the actual surface shape of the right or left rail 12 is associated with the running position of the railway vehicle 20 at that timing, data corresponding to the actual surface shape of the right or left rail 12 is associated with the longitudinal position on the track 10. The rail condition information generation device 46 generates rail condition information that associates the data corresponding to the actual surface shape of the rail 12 with the longitudinal position on the track 10. The rail condition information is output to the communication device 48. The communication device 48 includes a communication circuit and transmits the rail condition information.

[0022] The rail condition acquisition device 40 and the rail shape monitoring device 70 are communicated to the data server 90. For example, the rail condition acquisition device 40 is communicated to the data server 90 via the communication network 38. At least a portion of the communication path between the rail condition acquisition device 40 and the data server 90 may be wireless. Alternatively, the rail shape monitoring device 70 is communicated to the data server 90 via the communication network 38. The rail shape monitoring device 70 may also be directly communicated to the data server 90 via a wired connection.

[0023] The data server 90 is a computer equipped with a storage device 92. The data server 90 may also be a cloud server. Rail condition information is transmitted from the rail condition acquisition device 40 to the data server 90 via the communication network 38. As a result, the rail condition information is stored in the storage device 92 of the data server 90 as collected data 92a. The collected data 92a may be data of one railway vehicle 20 traveling through one section, or data of one railway vehicle 20 traveling through multiple sections, or data of multiple railway vehicles 20 traveling through one or more sections.

[0024] The rail shape monitoring device 70 downloads rail condition information from the data server 90 and outputs monitoring data based on the rail condition information.

[0025] In this embodiment, the rail shape monitoring device 70 can be installed at any location. For example, the rail shape monitoring device 70 may be installed at a management base located on the ground to monitor the track 10. The rail shape monitoring device 70 may also be installed at a base for maintenance work. The rail shape monitoring device 70 may be mounted on a portable terminal device and carried by workers. The processing functions of the rail shape monitoring device 70 may be implemented on a data server 90. The processing functions of the rail shape monitoring device 70 may also be implemented on a cloud server.

[0026] The above-mentioned communication network 38 may be wired, wireless, or a combination of both. Furthermore, the communication network 38 may be a public communication network or a dedicated line communication network. Also, rail condition information may be directly transmitted from the rail condition acquisition device 40 to the rail shape monitoring device 70. In this case, the data server 90 may be omitted.

[0027] Figure 2 is a block diagram of the rail condition acquisition device 40.

[0028] The sensor 42 is supported by the railway vehicle 20 and detects the actual surface shape of the rail 12 at the railway vehicle 20's travel position while the railway vehicle 20 is in motion. The sensor 42 may also detect the actual surface shape of the rail 12 when the railway vehicle 20 is stopped.

[0029] The sensor 42 is supported at the bottom of the railway vehicle 20 and detects the actual surface shape of the rail 12 from above the rail 12. The sensor 42 may be, for example, a shape measuring device using the light section method. A shape measuring device using the light section method irradiates the rail 12 and its outer region with slit light from a slit light source, captures an image of the slit light reflected by an imaging device, and calculates the coordinate positions of the surface of the rail 12 and its outer region based on the position of the slit in the captured image.

[0030] The railway vehicle 20 may be equipped with two or more sensors 42 for each rail 12. The two or more sensors 42 may be supported by the railway vehicle 20 so as to be positioned diagonally above and outside and diagonally above and inside the pair of rails 12 with respect to the rail 12 to be detected. For example, when the left rail 12 is to be detected, the diagonally above and outside the pair of rails 12 is diagonally above and to the left of the left rail 12, and the diagonally above and inside the pair of rails 12 is diagonally above and to the right of the left rail 12. The two or more sensors 42 may include a sensor that detects the rail 12 from diagonally above and to the right of the rail 12 to be detected, and a sensor that detects the rail 12 from diagonally above and to the left of the rail 12.

[0031] Two or more sensors 42 detect the rail 12 from diagonally above on the left and right, thereby detecting not only the actual shape of the rail 12 when viewed from directly above, but also the actual shape when viewed from diagonally above on the right, and the actual shape when viewed from diagonally above on the left. In other words, the actual shape of the upper surface and the actual shapes of the surfaces on both sides of the rail 12 are detected.

[0032] Let d be the lateral resolution of sensor 42 and θ be the installation angle of sensor 42. The installation angle θ is the angle with respect to the vertical direction. In this case, the measurement resolution of rail 12 in the vertical direction is d / sinθ. Also, the measurement resolution of rail 12 in the left-right direction is d / cosθ.

[0033] By setting the installation angle of the sensor 42 to 45 degrees, the vertical resolution and horizontal resolution can be made the same as 1.414d.

[0034] The sensor 42 does not have to be a shape measuring device using the light section method. For example, the sensor may be a sensor including a distance sensor such as a laser sensor, ultrasonic sensor, or optical sensor, or a stereo camera.

[0035] The running position detection unit 44 detects the running position of the railway vehicle 20 on the track 10. The running position of the railway vehicle 20 is the position of the railway vehicle 20 in the longitudinal direction of the track 10. The running position of the railway vehicle 20 may be a position (e.g., kilometer marker) based on a fixed position in the longitudinal direction of the track 10 (e.g., the starting point of the track, any station), or it may be a position based on an arbitrary position in the longitudinal direction of the track 10. For example, the running position detection unit 44 may include a rotation speed detection sensor that detects the rotation speed of the wheels, and output the distance traveled from any position based on the detection result of the rotation speed detection sensor. A sensor that detects the vehicle speed based on the rotation speed of the railway vehicle 20 is sometimes called a speed generator. Since the distance traveled can be determined by integrating the speed, the running position detection unit 44 including the rotation speed detection sensor may output the speed at regular intervals.

[0036] Furthermore, for example, the driving position detection unit 44 may include a GPS (Global Positioning System) receiver in a GNSS (Global Navigation Satellite System), and output latitude and longitude information obtained from the received signal by the GPS receiver, or the position in the longitudinal direction of the trajectory 10 based on said latitude and longitude information.

[0037] The rail condition information generation device 46 is composed of a computer including a processor 46a such as a CPU (Central Processing Unit), a storage device 46b, an input / output interface 46c, and the like.

[0038] The processor 46a includes an arithmetic circuit. The storage device 46b is composed of a non-volatile storage device such as an HDD (hard disk drive) or SSD (solid-state drive). The program 46b1 is stored in the storage device 46b. The input / output interface 46c includes an input / output circuit.

[0039] The outputs from the sensor 42 and the running position detection unit 44 are provided to the rail state information generation device 46 via the input / output interface 46c. Based on the commands described in the program 46b1, the processor 46a generates rail state information 46b2, which associates data corresponding to the actual surface shape of the right or left rail 12 with the longitudinal position of the rail 12, and stores it in the storage device 46b.

[0040] The communication device 48 includes a communication circuit that can be connected to the communication network 38. The communication device 48 is, for example, a wireless communication device. The rail condition information generation device 46 transmits rail condition information 46b2 via the communication device 48. The rail condition information 46b2 may be transmitted in real time, or it may be transmitted at predetermined intervals or at predetermined distances traveled.

[0041] As shown in Figure 1, in this embodiment, rail condition information 46b2 is provided to the rail shape monitoring device 70 via the data server 90. The rail shape monitoring device 70 outputs monitoring data based on the rail condition information.

[0042] The rail shape monitoring device 70 is composed of a computer including a processor 72 such as a CPU, a storage device 74, a communication device 76, etc. The communication device 76 includes a communication circuit, and the rail shape monitoring device 70 is communicably connected to the data server 90 via the communication device 76. As described above, the communication device 76 may be a dedicated communication device connected only to the data server 90, or may be a communication device connected to the communication network 38. The communication device 76 may be a communication device capable of directly communicating with the rail state acquisition device 40. The communication device 76 is an example of an input unit to which data corresponding to the actual surface shape of the rail is input. The data of the sensor 42 is given to the input unit indirectly or directly. The input / output interface 46c of the rail state information generation device 46 may be an input unit to which data corresponding to the actual surface shape of the rail of the sensor 42 is input. The sensor 42 is an example of a rail shape detection sensor that detects the actual surface shape of the rail 12 and gives data corresponding to the actual surface shape of the rail 12 to the input unit.

[0043] In this example, the detected shape is calculated based on the data corresponding to the actual surface shape of the rail 12 by the sensor 42, and the detected shape is given to the communication device 76 of the rail shape monitoring device 70. It is not necessary to calculate the detected shape in the sensor 42. For example, when the actual surface shape of the rail 12 is detected by the sensor 42, the detected data may be given to the rail shape monitoring device 70, and the detected shape may be calculated based on the detected data in the rail shape monitoring device 70.

[0044] The storage device 74 is composed of a non-volatile storage device such as an HDD (hard disk drive) or an SSD (Solid-state drive). Each data including a program 74a, rail state information 74b, monitoring data 74c, reference shape 74d, detected shape 74e, etc. is stored in the storage device 74. The storage device 74 is an example of a storage unit that stores reference shape data including the reference shape 74d of the rail 12. The program 74a describes the processing for the processor 72 to realize the function as a processing unit.

[0045] The processor 72 includes an arithmetic circuit. By executing the processes described in the program 74a, the processor 72 calculates the shape relationship between the detected shape 74e and the reference shape 74d, converts the shape to be converted into a monitoring conversion shape based on the shape relationship, and outputs monitoring data based on the monitoring conversion shape, thereby executing the process as a processing unit.

[0046] The processor 72 may be one or plural. The plural processors 72 may be incorporated in one computer. The plural processors 72 may be incorporated in plural computers, and the plural computers may perform the processes as the above-described processing unit in a distributed manner.

[0047] The rail state information 74b is data corresponding to the rail 12 to be evaluated among the collected data 92a stored in the data server 90. The rail state information 74b is data in which data corresponding to the actual surface shape of the rail 12 is associated with the position data in the longitudinal direction of the rail 12. The detected shape 74e is a shape obtained by the sensor 42 detecting the actual surface shape of the rail 12. The detected shape 74e is data extracted as a monitoring target from the rail state information 74b, and is a detected shape at any position of the rail 12.

[0048] The rail shape monitoring device 70 may include an input reception unit 78 that receives various instructions from a user with respect to the rail shape monitoring device 70. The input reception unit 78 may be a keyboard, a mouse, a touch panel, or the like that includes a plurality of switches.

[0049] The rail shape monitoring device 70 may include a display device 79 that displays various information by the rail shape monitoring device 70. The display device 79 may be a liquid crystal display device, an organic EL (Electro-luminescence) display device, or the like. As the display device 79, a display device provided in a smartphone, a tablet terminal, or the like may be used.

[0050] Figure 3 shows an example of a reference shape 74d. The reference shape 74d may be the shape shown in the design drawing of the rail 12. The reference shape 74d may also be data obtained by measuring a rail 12 that is not worn. The measurement of the rail 12 that is not worn may be performed by the sensor 42 or by other sensors.

[0051] If the reference shape 74d is data obtained by measuring the rail 12, errors may be introduced into the measurement data of the rail 12 due to the resolution of the sensor measurement system or the influence of the reflection of the laser light used for measurement. In such cases, the data obtained by measuring the rail 12 may be corrected using design drawing information.

[0052] A coordinate system may be set in the reference shape 74d. For example, a coordinate system based on the railway vehicle 20 may be set. For example, assuming the railway vehicle 20 is stopped, the position of the rail 12 relative to the railway vehicle 20 is considered to be constant. Therefore, the reference shape 74d may be set in a coordinate system based on the railway vehicle 20. Also, the sensor 42 is supported by the railway vehicle 20. Therefore, based on the detection results of the sensor 42, the detected shape of the surface of the rail 12 can be calculated in a coordinate system based on the railway vehicle 20.

[0053] The rail 12 includes, for example, a bottom portion 12a, a belly portion 12b, and a head portion 12c, in order from bottom to top. The bottom portion 12a and the head portion 12c are wider than the belly portion 12b. The bottom portion 12a may be wider than the head portion 12c. The portion of the head portion 12c that is visible from above in a plan view is defined as the top portion 12c1.

[0054] For example, in the cross-section of the rail 12, the bottom 12a may be defined as the point of intersection P1 between the extension line L1 of the straight portion of the upper left side surface of the bottom 12a that is closest to the belly 12b and the vertical line Lm that passes through the center 12c2 of the top 12c1 of the rail 12.

[0055] The reference intersection point P1 may be the height position of the intersection of the extension line L1 and the extension line L3 of the straight section closest to the web 12b on the upper right side surface of the bottom 12a of the rail 12.

[0056] Alternatively, for example, in the cross-section of the rail 12, the intersection point P2 of the extension line L2 of the straight portion closest to the body 12b on the upper left side surface of the head 12c and the vertical line Lm passing through the center 12c2 of the head 12c of the rail 12 may be used as a reference, and the part above this intersection P2 may be defined as the head 12c.

[0057] Furthermore, for example, the portion between the intersection point P1 and the intersection point P2 may be defined as the abdomen 12b.

[0058] As the rail 12 is worn down by the wheel 25W, the top of the head 12c or the sides of the head 12c may wear down. The actual surface shape of the rail 12 may be affected by factors other than wear caused by the wheel 25W. For example, oil drips, wear, or chipping that occur on the jaw, which is the lower corner of the side of the head 12c, will affect the actual surface shape of the rail 12 as detected.

[0059] This disclosure aims to enable proper monitoring of the wear condition of the rail 12 by the wheel 25W by eliminating, for example, the effects of oil dripping, wear, or chipping that occurs on the jaw portion of the head 12c as much as possible.

[0060] Figure 4 shows an example of the detected shape 74e. The sensor 42 is supported by the railway vehicle 20. Therefore, the detected shape 74e can be calculated in a coordinate system based on the railway vehicle 20.

[0061] While the railway vehicle 20 is in motion, the position of the railway vehicle 20 relative to the rail 12 may not be constant. For example, as shown in Figure 5, suppose the railway vehicle 20 is traveling on a curved section of the track 10. In this case, the railway vehicle 20 may tilt relative to the rail 12. When the railway vehicle 20 tilts relative to the rail 12, the detected shape 74e may rotate, be displaced left or right, or be displaced up or down in a coordinate system based on the railway vehicle 20.

[0062] Furthermore, the vehicle body 22 does not curve to follow the curved portion of the rail 12. As a result, the detection axis of the sensor 42 supported by the vehicle body 22 may be inclined with respect to the tangent to the curved portion. The detection center axis of the sensor 42 is, for example, the optical center axis of the imaging device if the sensor 42 is an imaging device. In this case, the widthwise axis perpendicular to the detection center axis of the sensor 42 is inclined with respect to the widthwise direction of the rail 12. Consequently, the sensor 42 detects the actual surface shape of the rail 12 in a cross section inclined with respect to the widthwise direction of the rail 12. In this case, the detected shape of the rail 12 by the sensor 42 may be enlarged in the widthwise direction compared to the actual shape of the rail 12 in a cross section along the widthwise direction of the rail 12.

[0063] When calculating the shape relationship between the reference shape 74d and the detected shape 74e, it is preferable to take into account the displacement and expansion of the rail 12 relative to the railway vehicle 20.

[0064] An example of processing as a processing unit in the rail shape monitoring device 70 will be explained with reference to the flowchart shown in Figure 6.

[0065] In step S1, the processor 72 reads the reference shape 74d from the storage device 74.

[0066] In the next step S2, the processor 72 sets a reference point. The reference point is a point used as a reference when calculating the shape relationship between the reference shape 74d and the detected shape 74e. The reference point may be set in either the reference shape 74d or the detected shape 74e. In this embodiment, an example in which the reference point is set in the reference shape 74d will be described.

[0067] Figure 7 shows an example of setting reference points Pa and Pb. Reference points Pa and Pb may be set in a region of the outer contour corresponding to the cross-section of the rail 12 that is less affected by the wheels 25W of the rail 12 and less affected by the fastening device of the rail 12.

[0068] For example, there may be multiple reference points, and these multiple reference points may include multiple abdominal reference points Pb set on both sides of the abdominal portion 12b of the rail 12, and multiple bottom reference points Pa set on the upper surfaces of both sides of the bottom portion 12a of the rail 12.

[0069] The abdomen 12b and bottom 12a are less susceptible to wear caused by the wheels 25W. By including multiple abdomen reference points Pb and multiple bottom reference points Pa as reference points, the shape relationship can be calculated based on the positional relationship of the parts of the rail 12 that are less susceptible to wear caused by the wheels 25W.

[0070] Multiple abdominal reference points Pb may include multiple points set at vertical intervals on each side of the abdominal surface 12b of the rail 12. For example, two or more abdominal reference points Pb may be set on each side of the abdominal surface of the rail 12. By using multiple abdominal reference points Pb, the positional relationship in the left-right direction, the scaling ratio in the left-right direction, and the inclination are more easily reflected in the shape relationship.

[0071] Multiple abdominal reference points Pb may be part of abdominal point cloud data that defines both sides of the abdominal 12b of the rail 12. For example, the reference shape 74d is defined by point cloud data that defines the boundary of the rail 12. Multiple abdominal reference points Pb may be a subset of points included in the point cloud that defines both sides of the abdominal 12b. In other words, multiple abdominal reference points Pb may be data extracted from a portion of the point cloud that defines both sides of the abdominal 12b.

[0072] Multiple bottom reference points Pa may include multiple points set at intervals in the left-right direction on each side of the bottom 12a of the rail 12. For example, two or more bottom reference points Pa may be set on each side of the bottom 12a of the rail 12. By using multiple bottom reference points Pa, the vertical positional relationship and inclination are more easily reflected in the shape relationship.

[0073] Multiple bottom reference points Pa may be part of bottom point cloud data that defines both sides of the bottom 12a of the rail 12. For example, the reference shape 74d is defined by point cloud data that defines the boundary of the rail 12. Multiple bottom reference points Pa may be a subset of points included in the point cloud that defines the upper surfaces of both sides of the bottom 12a. In other words, multiple bottom reference points Pa may be data extracted from a portion of the point cloud that defines the upper surfaces of both sides of the bottom 12a.

[0074] In terms of shape relationships, abdominal reference points Pb tend to influence the relationship in the left-right direction, while bottom reference points Pa tend to influence the relationship in the up-down direction. Therefore, in order to make the left-right and up-down relationships in shape relationships as consistent as possible, the number of abdominal reference points Pb and the number of bottom reference points Pa may be made the same or the difference may be reduced. Alternatively, the spacing between abdominal reference points Pb and the spacing between bottom reference points Pa may be made the same or the difference in spacing may be reduced.

[0075] For example, the vertical spacing of multiple abdominal reference points Pb on each side of the abdomen 12b may be within ±20% of the horizontal spacing of the bottom reference points Pa on the upper surfaces of each side of the multiple bottoms 12a. The vertical spacing of multiple abdominal reference points Pb on each side of the abdomen 12b may be an average value. The horizontal spacing of bottom reference points Pa on the upper surfaces of each side of the multiple bottoms 12a may be an average value.

[0076] Furthermore, the number of multiple abdominal reference points Pb may be within ±20% of the number of multiple base reference points Pa.

[0077] The reference point Pa may be set on a part of the rail 12 other than the head 12c. In this case, the influence of wear on the wheel 25W is more easily eliminated in terms of shape relationship. However, the reference point Pa may also be set on the surface of the head 12c, for example, the top surface or the side surface of the head.

[0078] The reference point Pa may be set in a part of the bottom 12a excluding both outer sides of the bottom 12a. Fastening devices such as dog nails may be placed on the outer sides of the bottom 12a. The actual shape of the fastening device may be reflected in the detected shape 74e. By setting the reference point Pa in a part of the bottom 12a excluding both outer sides of the bottom 12a, the influence of the fastening device is more easily eliminated. The reference point Pb may be set in a part of the abdomen 12b excluding the part that is in a blind spot due to the head 12c of the rail 12 and where the actual shape of the rail 12 cannot be detected by the sensor 42.

[0079] In step S2, after the reference points Pa and Pb are set on the reference shape 74d, the process proceeds to the next step S3. Note that the reference points Pa and Pb may be set at any time before the shape relationships are calculated.

[0080] In step S3, the processor 72 reads the detected shape 74e from the storage device 74. Note that the reading of the reference shape 74d and the detected shape 74e may be performed in any order.

[0081] In the next step S4, the processor 72 calculates the shape relationship between the reference shape 74d and the detected shape 74e. The calculation of the shape relationship is, for example, the calculation of the geometric transformation relationship between the reference shape 74d and the detected shape 74e. The shape relationship is a relationship that indicates what geometric transformation should be applied to one of the reference shape 74d or the detected shape 74e to make it closer to the other shape.

[0082] For example, shape relationships may be represented by affine transformations. For example, shape relationships may be represented by rotation, left-right scaling, up-down translation, and left-right translation.

[0083] The shape relationship may be calculated, for example, by the ICP (Iterative Closest Point) algorithm. For example, the nearest point in the point cloud defining the shape of the rail 12 in the detected shape 74e is searched for from each reference point Pa, Pb of the reference shape 74d. Then, the nearest point in the point cloud of the detected shape 74e is associated with each of the reference points Pa, Pb. The shape relationship is calculated so as to minimize the distance between each reference point Pa, Pb and its corresponding nearest point. For example, the reference shape 74d is rotated, scaled horizontally, translated vertically, and translated horizontally so as to minimize the distance between each reference point Pa, Pb and its corresponding nearest point. If necessary, the search for the nearest point corresponding to each reference point Pa, Pb, the association, and the recalculation of the shape relationship to minimize the distance may be repeated. The shape relationship may be understood as a function defined by the amount of vertical translation, the amount of horizontal translation, the rotation angle, and the horizontal scaling ratio.

[0084] As described above, while the railway vehicle 20 is in motion, the detected shape 74e may rotate, be displaced left or right, or be displaced up or down in a coordinate system based on the railway vehicle 20. Also, in the curved portion of the rail 12, the detected shape 74e may become larger in the width direction. Therefore, if the shape relationship includes the vertical translation, horizontal translation, rotation, and horizontal scaling relationships between the detected shape 74e and the reference shape 74d, it can reflect the positional shifts expected while the railway vehicle 20 is in motion. The shape relationship may also consist only of the vertical translation, horizontal translation, rotation, and horizontal scaling relationships between the detected shape 74e and the reference shape 74d.

[0085] The shape relationship may include other shape relationships. For example, the shape relationship may include an up-down scaling relationship. However, generally, the angle of the central axis of the sensor 42 with respect to the rail 12 is assumed to be larger in the yaw angle than in the pitch angle. In other words, generally, the radius of curvature of the vertical curve of the rail 12 in places where the gradient changes is larger than the radius of curvature of the lateral curve of the rail. For example, in Japan, if the radius of curvature of the lateral curve is greater than 800m, the radius of curvature of the vertical curve is set to greater than 3000m, and if the radius of curvature of the lateral curve is 800m or less, the radius of curvature of the vertical curve is set to greater than 4000m. Because the radius of curvature of the vertical curve is very large, even if an up-down scaling relationship is considered as a shape relationship, the scaling factor will be extremely close to 1.

[0086] Therefore, the influence of vertical scaling on shape relationships is negligible. Consequently, shape relationships do not necessarily need to include vertical scaling.

[0087] The shape relationship may include other shape relationships, such as shear relationships.

[0088] In step S4, the shape relationship between the reference shape 74d and the detected shape 74e is calculated.

[0089] In step S5, the processor 72 transforms the reference shape 74d based on the shape relationship, for example, by performing a shape transformation. In other words, in this embodiment, one of the reference shapes 74d, the detected shape 74e and the reference shape 74d, is used as the shape to be transformed. For example, in step S4, based on the reference points Pa and Pb, it is calculated how to rotate, expand left and right, translate up and down and left and right the reference shape 74d should be moved to approximate the detected shape 74e.

[0090] In other words, the positional relationship between the reference point in the reference shape 74d and the detected shape 74e is applied to the entire reference shape 74d to transform the reference shape 74d so that it approaches the detected shape 74e. For example, shape transformation is a mapping operation that transforms geometric elements that define the shape before transformation into the shape after transformation based on predetermined transformation rules or mathematical relationships.

[0091] The reference shape 74d, which includes not only the reference points Pa and Pb but also the point cloud defining the head 12c, is transformed based on the calculated shape relationships. An example of the transformed monitoring shape 74ch after the shape transformation is shown in Figure 8. Figure 8 also shows the positions of the reference points Qa and Qb after the shape transformation. The positions of the reference points Qa and Qb after the shape transformation are also the positions of the reference points Pa and Pb after they have been geometrically transformed when the shape relationships are calculated in step S4. Data including the transformed monitoring shape 74ch is generated as monitoring data 74c for monitoring the rail 12.

[0092] In the next step S6, the processor 72 compares the monitoring transformation shape 74ch and the detection shape 74e. For example, as shown in Figure 9, the monitoring transformation shape 74ch and the detection shape 74e are superimposed on a coordinate system based on the railway vehicle 20. By comparing the detection shape 74e, which is the shape to be compared, with the monitoring transformation shape 74ch, the wear state of the rail 12 is determined by the amount of wear.

[0093] More specifically, the top of the head 12c and the areas corresponding to both sides of the head are compared between the monitoring conversion shape 74ch and the detection shape 74e.

[0094] For example, along a vertical reference line Sa, the difference d1 between the position R1 of the top surface of the monitoring transformation shape 74ch and the position R2 of the top surface of the detection shape 74e is obtained as a comparison result. This difference d1 indicates the amount of wear on the top surface. The amount of wear on the top surface may be determined at multiple different positions in the horizontal direction.

[0095] Furthermore, for example, along a horizontal reference line Sb, the difference d2 between the position R3 of the side surface of the head of the monitoring transformation shape 74ch and the position R4 of the side surface of the head of the detection shape 74e is obtained as a comparison result. This difference d2 indicates the amount of wear on the side surface of the head. The amount of wear on the side surface of the head may be determined on both the left and right sides. The amount of wear on the side surface of the head 12c may be determined at multiple different positions on the top and bottom.

[0096] Here, the detected shape 74e and the monitoring transformed shape 74ch correspond to the oblique cross-section of the rail 12. Therefore, the difference d2 is larger than the dimension of the cross-section of the rail 12. Thus, the horizontal difference d2 may be the difference d2a obtained by dividing it by the left-right scaling ratio used for shape transformation in step S5. This difference d2a is the dimension observed in the cross-section of the rail 12 perpendicular to the longitudinal direction of the rail 12. In the above example, the left-right scaling ratio of the detected shape 74e with respect to the reference shape 74d is calculated, so the reciprocal of this scaling ratio is the left-right scaling ratio of the reference shape 74d with respect to the detected shape 74e. The difference d2a is calculated by multiplying the horizontal difference d2 by the left-right scaling ratio of the reference shape 74d with respect to the detected shape 74e. In other words, dividing the difference d2 by the left-right scaling ratio of the detected shape 74e relative to the reference shape 74d is equivalent to multiplying the difference d2 by the left-right scaling ratio of the reference shape 74d relative to the detected shape 74e.

[0097] The above-mentioned reference lines Sa and Sb may be lines that have been set in advance in a coordinate system based on the railway vehicle 20. The reference lines Sa and Sb may also be lines that have been set based on the monitoring transformation shape 74ch or the detection shape 74e. For example, the reference lines Sa and Sb may be set based on the coordinate center, the rightmost point, the leftmost point, or the topmost point of the point cloud of the monitoring transformation shape 74ch or the detection shape 74e. The reference lines Sa and Sb may also be lines that are oblique to the vertical direction.

[0098] Furthermore, even if there is oil dripping on the chin of the head 12c or a fastening device is present on the bottom 12a, these shapes are unlikely to affect the comparison of the top surface of the head 12c or the comparison of the sides of the head.

[0099] The comparison result between the monitoring transformation shape 74ch and the detection shape 74e is not limited to the above example. For example, as a result of comparing the monitoring transformation shape 74ch and the detection shape 74e, the area of ​​the region enclosed by the top surfaces of both shapes may be calculated.

[0100] Alternatively, the width Wa of the head 12c may be determined based on the detected shape 74e. For example, the distance between two intersections of the detected shape 74e with respect to the reference line Sb is determined as the width W. The width W is then calculated as the width Wa obtained by dividing the above width W by the left-right scaling ratio used for shape transformation. This width Wa is the dimension observed in the cross-section of the rail 12 perpendicular to the longitudinal direction of the rail 12.

[0101] In the next step S7, the processor 72 determines the wear condition of the head 12c of the rail 12.

[0102] For example, the difference d1 is compared with a predetermined first reference value. The first reference value is set as a value that encourages maintenance regarding the amount of wear on the top of the head. For example, if the difference d1 is greater than or equal to the first reference value, it is determined that the amount of wear on the top of the head is large and the wear condition encourages maintenance.

[0103] Furthermore, the difference d2a is compared with a predetermined second reference value. The second reference value is set as the value at which maintenance should be considered for the amount of wear on the sides of the head 12c. If the difference d2a is greater than or equal to the second reference value, it is determined that the amount of wear on the sides of the top of the head is large and that maintenance is recommended.

[0104] Multiple values ​​may be set for each of the above-mentioned first and second reference values, depending on the level at which maintenance is encouraged.

[0105] In step S6, the amount of wear on the opposite side of the head 12c may also be similarly determined, and the wear condition may be judged by comparing the amount of wear with a reference value.

[0106] The differences d1 and d2a in the amount of wear, as well as the results of determining the wear state by comparing them with the reference value, are examples of information indicating the wear state, and are also examples of information included in monitoring data.

[0107] The processes from steps S1 to S7 are performed on the detected shape 74e, which is associated with the longitudinal position of the rail 12. As a result, the wear state associated with the longitudinal position of the rail 12 is determined.

[0108] The processes in steps S1 to S7 may be performed focusing on one location along the longitudinal direction of the rail 12, or they may be repeated for multiple locations along the longitudinal direction of the rail 12. This makes it easier to determine the wear state of the rail 12 along its entire longitudinal direction. For example, the processes in steps S1 to S7 may be repeated for detection shapes at regular intervals along the longitudinal direction of the rail 12.

[0109] The process in step S8 is explained assuming that the wear condition has been determined at multiple locations along the longitudinal direction of the rail 12.

[0110] In step S8, the processor 72 displays information based on the monitoring conversion shape 74ch on the display device 79. As shown in Figure 10, the display device 79 displays, for example, a wear state display image 80 that associates the wear state with the position of the rail 12.

[0111] For example, in step S7, suppose that multiple levels of wear, such as the difference d1 or d2a, that prompt maintenance are determined.

[0112] The wear condition display image 80 may include a track path diagram 81. The track path diagram 81 is displayed by a combination of segments divided into multiple monitoring sections. Part or all of each segment is displayed as visually distinguishable maintenance level information 81a, corresponding to the maintenance level of the wear amount of the rail 12. For example, the maintenance level information 81a may be information in which segments are colored with dots of higher density as the maintenance level increases, or displayed in a color that is more conspicuous as the maintenance level increases.

[0113] The level of each segment may be for one of the two rails 12 that has been selected, or it may be for the level of the rail with the greater amount of wear among the two rails 12.

[0114] The level of each segment may be the level with the greatest amount of wear among multiple locations within the section to which that segment belongs, or it may be the level of a representative location within the section to which that segment belongs.

[0115] Furthermore, for example, a lead-out arrow may be drawn from a selected segment in the track path diagram 81. At the end of the lead-out arrow, a graph 81b may be displayed that associates the amount of wear with multiple locations belonging to that segment. The graph 81b is, for example, a diagram in which dots indicating the amount of wear are placed at the kilometer mileage of the section belonging to that segment. In this way, the wear state display image 80 may include images showing the amount of wear at multiple locations in the longitudinal direction of the rail 12.

[0116] Furthermore, for example, the wear condition display image 80 may include a shape comparison image 81c that displays a comparison between the monitoring conversion shape 74ch and the detection shape 74e, which is the comparison target shape. The shape comparison image 81c may be an image of the longitudinal position of the rail 12 with the greatest amount of wear in the selected segment. If the amount of wear is calculated for multiple locations in the cross-section of the rail 12, the largest amount of wear in that cross-section may be used as the reference.

[0117] In graph 81b, a lead-out arrow may be added from the dot to the shape comparison image 81c, thereby associating the monitoring conversion shape 74ch with the position of the rail 12.

[0118] As described above, the wear condition display image 80 may include an image that associates the wear condition with the track path diagram 81, an image that associates the wear condition with the kilometer, or a shape comparison image 81c.

[0119] Each of the above images is controlled so that the displayed content changes if the detected shape 74e is different. For example, if wear progresses and the amount of wear increases, the image showing the maintenance level information 81a in the segment of the track path diagram 81 is changed. Also, the position of the dots corresponding to the wear state to the kilometer is changed. Furthermore, the monitoring conversion shape 74ch and the detection shape 74e, which is the comparison shape, are also changed in the shape comparison image 81c.

[0120] Therefore, the processor 72 controls the content of the wear status display image 80 according to the wear status.

[0121] The width Wa of the head portion 12c may also be used to determine the wear condition and to generate a wear condition display image indicating the wear condition.

[0122] According to this rail shape monitoring system 30 and rail shape monitoring method, the shape relationship between the detected shape 74e and the reference shape 74d is calculated. Then, the reference shape 74d is used as the shape to be converted, and the detected shape 74e is used as the shape to be compared, and based on the above shape relationship, the reference shape 74d, which is the shape to be converted, is converted into a monitoring conversion shape 74ch. For example, by comparing the monitoring conversion shape 74ch included in the monitoring data 74c with the detected shape 74e, which is the shape to be compared, the shape of the rail 12 can be monitored more accurately.

[0123] Furthermore, even with the monitoring transformation shape 74ch alone, the shape of the monitoring transformation shape 74ch can be determined by using a coordinate system based on the railway vehicle 20 as the reference. For this reason, it is not essential that the shape of the rail 12 be determined by comparing the monitoring transformation shape 74ch with the detection shape 74e, which is the comparison target shape.

[0124] Furthermore, the actual surface shape of the rail 12 is detected by the sensor 42, which is a rail shape detection sensor.

[0125] Furthermore, the shape relationship between the detected shape 74e and the reference shape 74d is calculated using multiple reference points Pb set on both sides of the belly 12b of the rail 12 and multiple reference points Pa set on the upper surfaces of both sides of the bottom 12a of the rail 12. This makes it possible to calculate the shape relationship between the detected shape 74e and the reference shape 74d while avoiding the effects of wear, chipping, or oil dripping of the head 12c of the rail 12.

[0126] If multiple abdominal reference points Pb are set at vertical intervals on each of the two sides of the abdomen 12b, and multiple bottom reference points Pa are set at horizontal intervals on each of the two sides of the bottom 12a, the shape relationship between the detected shape and the reference shape can be calculated more accurately.

[0127] Furthermore, by using a portion of the point cloud data of the abdomen 12b as multiple abdominal reference points Pb, and a portion of the point cloud data of the base 12a as the base reference point Pa, the shape relationship between the detected shape 74e and the reference shape 74d can be calculated with less computation compared to the case where the shape relationship is calculated using all of the point cloud data of the abdomen 12b and all of the point cloud data of the base 12a. In addition, even if there is data in a portion of the point cloud data of the abdomen 12b or a portion of the point cloud data of the base 12a that is caused by false detection, a more accurate shape relationship can be calculated by excluding that data and then calculating the relationship.

[0128] Furthermore, if the vertical spacing between multiple abdominal reference points Pb is within ±20% of the horizontal spacing between multiple bottom reference points Pa, and the number of multiple abdominal reference points Pb is within ±20% of the number of multiple bottom reference points Pa, then the weights used when calculating the shape relationship can be made roughly equal in the vertical and horizontal directions.

[0129] Furthermore, using the reference shape 74d as the shape to be converted, the shape to be converted is transformed based on the shape relationship to generate a monitoring conversion shape 74ch. Therefore, it is easy to determine how much the detected shape 74e deviates from the reference shape 74d. Also, by comparing this monitoring conversion shape 74ch with the detected shape 74e, it is easy to determine how much the detected shape 74e itself has worn down from the monitoring conversion shape 74ch that was transformed from the reference shape.

[0130] Furthermore, the wear condition of the rail 12 is determined by comparing the detection shape 74e, which is the comparison target shape, with the monitoring conversion shape 74ch.

[0131] Furthermore, an image 80 showing the wear condition, which is an example of information based on the monitoring data 74c, is displayed on the display device 79. By viewing the display device 79, the user can grasp the information based on the monitoring data 74c.

[0132] The display device 79 displays a wear condition display image 80 that associates the wear condition with the position of the rail 12, making it easy to understand the wear condition of the rail 12 in the longitudinal direction.

[0133] Furthermore, by controlling the display content of the wear condition display image 80 according to the wear condition, the wear condition of the rail 12 can be easily understood according to the wear condition.

[0134] Furthermore, if the shape relationship includes vertical translation, horizontal translation, rotation, and horizontal scaling relationships between the detected shape 74e and the reference shape 74d, the shape of the rail 12 can be monitored more accurately.

[0135] Furthermore, the shape relationship does not necessarily have to include the vertical scaling relationship between the detected shape 74e and the reference shape 74d. Here, it is assumed that the angle of the railway vehicle 20 with respect to the rail 12 is larger in the yaw angle than in the pitch angle. For this reason, the detected shape is more likely to be detected differently from the actual dimensions in the left-right direction than in the vertical direction. Therefore, including the left-right scaling relationship in the shape relationship makes it easier to accurately monitor the left-right size of the rail 12. Also, by not including the vertical scaling relationship in the shape relationship, the amount of computation required when calculating the shape relationship can be reduced.

[0136] Furthermore, by calculating the left-right width Wa, widthwise wear amount d2a, or both of the rail 12 based on the left-right scaling ratio identified in the calculation of the detected shape 74e and shape relationships, even when the reference shape 74d is used as the shape to be converted, the left-right width, widthwise wear amount d2a, or both of the rail 12 can be calculated to be close to the actual shape.

[0137] {Modification} In the above embodiment, an example was described in which the reference shape 74d is the shape to be converted and the detected shape 74e is the shape to be compared. However, it is sufficient if one of the detected shape 74e and the reference shape 74d is the shape to be converted and the other is the shape to be compared.

[0138] For example, as shown in Figure 11, in step S21 the detected shape 74e is read, in step S22 a reference point is set for the detected shape 74e, and in step S23 the reference shape 74d is read.

[0139] Then, in step S24, the shape relationship between the detected shape 74e and the reference shape 74d is determined using the reference point set for the detected shape 74e.

[0140] Subsequently, in step S25, the detected shape 74e is transformed as the shape to be transformed based on the shape relationship calculated in step S24. The monitoring transformed shape 174ch generated by the shape transformation becomes a shape that approaches the reference shape 74d, for example, as shown in Figure 12.

[0141] Alternatively, instead of performing steps S21 to S24, the processes in steps S1 to S4 may be performed, and in step S24, the inverse shape transformation of the obtained shape relationship may be performed.

[0142] In step S26, similar to step S6, the monitoring conversion shape 174ch and the reference shape 74d, which is the shape to be compared, are compared. Figure 12 shows an image in which the monitoring conversion shape 174ch and the reference shape 74d are superimposed.

[0143] In the subsequent steps S27 and S28, similar to steps S7 and S8, the degree of wear is determined by comparing both shapes 174ch and 74d, and an image corresponding to the wear state can be displayed on the display device 79. In this way, the shape of the rail 12 can be monitored, similar to the embodiment described above.

[0144] In the above embodiment, an example was described in which the rail shape monitoring system 30 comprises a rail condition acquisition device 40 and a rail shape monitoring device 70.

[0145] The rail shape monitoring system 30 may be configured as a system that does not include a rail condition acquisition device 40 or a sensor 42. In other words, the rail shape monitoring system 30 may be configured as a device that receives data acquired by a sensor 42 mounted on a railway vehicle 20 via a communication device 76 which is an input unit, and generates monitoring data 74c.

[0146] The rail shape monitoring system 30 may be configured as a system mounted on a railway vehicle 20. For example, the rail shape monitoring device 70 may be mounted on a railway vehicle 20. In this case, the detection results obtained by the sensor 42 may be provided to the rail shape monitoring device 70 inside the railway vehicle 20 via communication lines inside the railway vehicle 20, without going through the communication network 38.

[0147] Based on the monitoring transformation shapes 74ch and 174ch obtained by transforming the above reference shape 74d or detection shape 74e, the positions of the left and right rails 12 at multiple locations in the longitudinal direction of the rail 12 are detected. The positions of the left and right rails 12 at multiple locations in the longitudinal direction of the rail 12 may be used to measure track displacement. Track displacement includes, for example, height displacement, alignment displacement, level displacement, track gauge displacement, and planarity displacement. An IMU (Inertial Measurement Unit) may be used to measure track displacement.

[0148] Furthermore, the configurations described in each of the above embodiments and modifications can be combined as appropriate, as long as they do not contradict each other.

[0149] {Note} This disclosure discloses the following aspects:

[0150] The first embodiment is a rail shape monitoring system comprising: an input unit that receives data corresponding to the actual surface shape of the rail; a storage unit that stores reference shape data including the reference shape of the rail; and a processing unit that calculates the shape relationship between a detected shape based on the data corresponding to the actual surface shape of the rail and the reference shape, sets one of the detected shape and the reference shape as the shape to be converted and the other as the shape to be compared, converts the shape to be converted based on the shape relationship to a monitoring converted shape, and outputs monitoring data based on the monitoring converted shape.

[0151] According to this system, the shape relationship between the detected shape and the reference shape is calculated, so that the shape to be transformed is converted into a monitoring transformation shape so that the detected shape and the reference shape are closer together, and monitoring data is output based on the monitoring transformation shape. During or after the processing of the monitoring data, the monitoring transformation shape is compared with the comparison target shape, so that the rail shape can be monitored more accurately.

[0152] A second embodiment is a rail shape monitoring system according to the first embodiment, which may further include a rail shape detection sensor that detects the actual surface shape of the rail and provides data corresponding to the actual surface shape of the rail to the input unit.

[0153] This allows the rail shape detection sensor to detect the actual surface shape of the rail.

[0154] A third embodiment is a rail shape monitoring system according to the first or second embodiment, wherein the processing unit calculates the shape relationship using a plurality of stomach reference points set on both sides of the stomach of the rail and a plurality of bottom reference points set on the upper surfaces of both sides of the bottom of the rail in the detected shape or the reference shape.

[0155] In this way, by using multiple reference points set on both sides of the rail's belly and multiple reference points set on the upper surfaces of both sides of the rail's bottom to calculate the shape relationship between the detected shape and the reference shape, it is possible to calculate the shape relationship between the detected shape and the reference shape while avoiding the effects of rail wear, chipping, or oil dripping.

[0156] A fourth aspect is a rail shape monitoring system according to the third aspect, wherein the plurality of belly reference points include a plurality of points set at intervals in the vertical direction on each of the two sides of the belly of the rail, and the plurality of bottom reference points include a plurality of points set at intervals in the horizontal direction on each of the two sides of the bottom of the rail.

[0157] As a result, multiple abdominal reference points are set with spacing in the vertical direction, and multiple bottom reference points are set with spacing in the horizontal direction, making it easier to calculate the shape relationship between the detected shape and the reference shape more accurately.

[0158] A fifth aspect is a rail shape monitoring system according to the fourth aspect, wherein the plurality of abdominal reference points are part of abdominal point cloud data that defines both sides of the abdominal part of the rail, and the plurality of bottom reference points are part of bottom point cloud data that defines the upper surfaces of both sides of the bottom of the rail.

[0159] This allows the shape relationship between the detected shape and the reference shape to be calculated with less computation.

[0160] The sixth aspect is a rail shape monitoring system according to the fifth aspect, wherein the vertical spacing of the plurality of abdominal reference points is within ±20% of the horizontal spacing of the plurality of bottom reference points, and the number of the plurality of abdominal reference points is within ±20% of the number of the plurality of bottom reference points.

[0161] This allows for equal weighting when calculating shape relationships in the up / down and left / right directions.

[0162] In addition, in the rail shape monitoring system according to any one of the first to sixth embodiments, the processing unit may use the reference shape as the shape to be converted and convert the shape to be converted based on the shape relationship.

[0163] This makes it easy to understand how much the detected shape deviates from the target shape corresponding to the reference shape.

[0164] The seventh embodiment is a rail shape monitoring system according to any one of the first to sixth embodiments, wherein the processing unit identifies the wear state of the rail by comparing the comparison target shape with the monitoring conversion shape.

[0165] This allows the wear condition of the rail to be identified by comparing the reference shape with the conversion shape used for monitoring.

[0166] The eighth aspect is a rail shape monitoring system according to any one of the first to seventh aspects, further comprising a display device for displaying information based on the monitoring data.

[0167] This allows information based on monitoring data to be grasped by making the display device visible.

[0168] The ninth aspect is a rail shape monitoring system according to the eighth aspect, wherein the processing unit identifies the wear state of the rail by comparing the comparison target shape with the monitoring conversion shape, and displays a wear state display image on the display device that associates the wear state with the position of the rail.

[0169] This makes it easy to understand the wear condition of the rails.

[0170] The tenth embodiment is a rail shape monitoring system according to the ninth embodiment, wherein the processing unit controls the display content of the wear state display image according to the wear state.

[0171] This makes it easy to understand the wear condition of the rails according to their wear state.

[0172] The eleventh embodiment is a rail shape monitoring system according to any one of the first to tenth embodiments, wherein the shape relationship includes vertical translation, horizontal translation, rotation, and horizontal scaling relationships between the detected shape and the reference shape.

[0173] Thus, if the shape relationship includes vertical translation, horizontal translation, rotation, and horizontal scaling relationships between the detected shape and the reference shape, the rail shape can be monitored more accurately.

[0174] In addition, in the rail shape monitoring system according to the 11th embodiment, the shape relationship may be configured to exclude the vertical expansion / contraction relationship between the detected shape and the reference shape.

[0175] The angle of the vehicle relative to the rail is expected to be greater in the yaw angle than in the pitch angle. Therefore, the detected shape is more likely to differ from the actual dimensions in the left-right direction than in the up-down direction. By including a left-right scaling relationship in the shape relationship, the left-right size of the rail can be monitored more accurately. In addition, by not including a vertical scaling relationship in the shape relationship, the amount of computation required when calculating the shape relationship can be reduced.

[0176] The twelfth aspect is a rail shape monitoring system according to the eleventh aspect, wherein the processing unit calculates the left-right scaling ratio of the reference shape with respect to the detected shape, using the reference shape as the shape to be converted, and calculates the left-right width of the rail, the amount of wear in the width direction, or both, based on the detected shape and the left-right scaling ratio.

[0177] As a result, even when the reference shape is the shape to be converted, the left-right width of the rail, the amount of wear in the width direction, or both can be calculated based on the detected shape and the left-right scaling ratio.

[0178] A rail shape monitoring system according to the 13th embodiment includes: an input unit into which data corresponding to the actual surface shape of the rail is input; a storage unit that stores reference shape data including the reference shape of the rail; and a processing unit that calculates the shape relationship between a detected shape based on the data corresponding to the actual surface shape of the rail and the reference shape, sets one of the detected shape and the reference shape as the shape to be converted and the other as the shape to be compared, converts the shape to be converted based on the shape relationship to a monitoring converted shape, and identifies the amount of wear on the rail by comparing the comparison shape and the monitoring converted shape.

[0179] According to this rail shape monitoring system, the shape relationship between the detected shape and the reference shape is calculated. The shape to be transformed is converted into a monitoring transformation shape so that the detected shape and the reference shape are closer together. By comparing the comparison target shape with the monitoring transformation shape, the amount of rail wear is determined. This allows for more accurate monitoring of the rail shape.

[0180] The fourteenth embodiment is a rail shape monitoring system according to any one of the first to thirteenth embodiments, wherein the processing unit determines the amount of wear on the top of the rail by comparing the portion corresponding to the top of the rail between the comparison target shape and the monitoring conversion shape.

[0181] This makes it easier to accurately determine the amount of wear on the top of the head.

[0182] The 15th embodiment is a rail shape monitoring system according to any one of the first to 14 embodiments, wherein the processing unit determines the amount of wear on the side surface of the rail head by comparing the portion corresponding to the side surface of the rail head between the comparison target shape and the monitoring conversion shape.

[0183] This makes it easier to accurately identify the amount of wear on the sides of the crown.

[0184] A rail shape monitoring method according to the 16th embodiment is a rail shape monitoring method which acquires data corresponding to the actual surface shape of the rail, acquires reference shape data including the reference shape of the rail, calculates the shape relationship between the detected shape based on the data corresponding to the actual surface shape of the rail and the reference shape, uses one of the detected shape and the reference shape as the shape to be converted and the other as the shape to be compared, converts the shape to be converted based on the shape relationship to a monitoring conversion shape, and outputs monitoring data based on the monitoring conversion shape.

[0185] This method calculates the shape relationship between the detected shape and the reference shape. The shape to be transformed is then converted so that the detected shape and the reference shape match, and monitoring data is output based on the transformed shape for monitoring. During or after processing the monitoring data, the transformed shape for monitoring is compared with the comparison target shape, allowing for more accurate monitoring of the rail shape.

[0186] The functions of the elements disclosed herein can be performed using circuits or processing circuits, including general-purpose processors, dedicated processors, integrated circuits, ASICs (Application Specific Integrated Circuits), conventional circuits, and / or combinations thereof, configured or programmed to perform the disclosed functions. A processor is considered a processing circuit or circuit because it includes transistors and other circuits. In this disclosure, a circuit, unit, or means is hardware that performs the enumerated functions, or hardware programmed to perform the enumerated functions. The hardware may be hardware disclosed herein, or other known hardware that is programmed or configured to perform the enumerated functions. If the hardware is a processor, which is considered a type of circuit, then the circuit, means, or unit is a combination of hardware and software, and the software is used to configure the hardware and / or the processor.

[0187] The above description is illustrative in all respects, and the invention is not limited thereto. It is understood that countless variations not illustrated can be conceivable without falling outside the scope of this invention.

[0188] 12 Rail 12a Bottom 12b Side 20 Railway vehicle 30 Rail shape monitoring system 40 Rail condition acquisition device 42 Sensor 44 Running position detection unit 46 Rail condition information generation device 70 Rail shape monitoring device 72 Processor 74 Storage device 74a Program 74b Rail condition information 74c Monitoring data 74ch, 174ch Monitoring conversion shape 74d Reference shape 74e Detected shape 76 Communication device 79 Display device 80 Wear condition display image 81 Track path diagram 81a Maintenance level information 81b Graph 81c Shape comparison image Pa Bottom reference point Pb Side reference point W, Wa Left and right width d2a Width direction wear amount

Claims

1. A rail shape monitoring system comprising: an input unit that receives data corresponding to the actual surface shape of the rail; a storage unit that stores reference shape data including the reference shape of the rail; and a processing unit that calculates the shape relationship between a detected shape based on the data corresponding to the actual surface shape of the rail and the reference shape, sets one of the detected shape and the reference shape as the shape to be converted and the other as the shape to be compared, converts the shape to be converted based on the shape relationship to a monitoring converted shape, and outputs monitoring data based on the monitoring converted shape.

2. A rail shape monitoring system according to claim 1, further comprising a rail shape detection sensor that detects the actual surface shape of the rail and provides data corresponding to the actual surface shape of the rail to the input unit.

3. A rail shape monitoring system according to claim 1 or claim 2, wherein the processing unit calculates the shape relationship using a plurality of stomach reference points set on both sides of the stomach of the rail and a plurality of bottom reference points set on the upper surfaces of both sides of the bottom of the rail in the detected shape or the reference shape.

4. A rail shape monitoring system according to claim 3, wherein the plurality of abdominal reference points include a plurality of points set at intervals in the vertical direction on each of the two sides of the abdominal portion of the rail, and the plurality of bottom reference points include a plurality of points set at intervals in the horizontal direction on each of the two sides of the bottom of the rail.

5. A rail shape monitoring system according to claim 4, wherein the plurality of abdominal reference points are part of abdominal point cloud data defining both sides of the abdominal portion of the rail, and the plurality of bottom reference points are part of bottom point cloud data defining the upper surfaces of both sides of the bottom of the rail.

6. Rail shape monitoring system according to claim 5, wherein the vertical spacing of the plurality of abdominal reference points is within ±20% of the horizontal spacing of the plurality of bottom reference points, and the number of the plurality of abdominal reference points is within ±20% of the number of the plurality of bottom reference points.

7. A rail shape monitoring system according to claim 1 or claim 2, wherein the processing unit identifies the wear state of the rail by comparing the comparison target shape with the monitoring conversion shape.

8. A rail shape monitoring system according to claim 1 or claim 2, further comprising a display device for displaying information based on the monitoring data.

9. A rail shape monitoring system according to claim 8, wherein the processing unit identifies the wear state of the rail by comparing the comparison target shape with the monitoring conversion shape, and displays a wear state display image on the display device that associates the wear state with the position of the rail.

10. Rail shape monitoring system according to claim 9, wherein the processing unit controls the display content of the wear state display image according to the wear state.

11. A rail shape monitoring system according to claim 1 or claim 2, wherein the shape relationship includes vertical translation, horizontal translation, rotation and horizontal scaling relationships between the detected shape and the reference shape.

12. A rail shape monitoring system according to claim 11, wherein the processing unit calculates the left-right scaling ratio of the reference shape with respect to the detected shape, using the reference shape as the shape to be converted, and calculates the left-right width of the rail, the amount of wear in the width direction, or both, based on the detected shape and the left-right scaling ratio.

13. A rail shape monitoring system comprising: an input unit that receives data corresponding to the actual surface shape of the rail; a storage unit that stores reference shape data including the reference shape of the rail; a processing unit that calculates the shape relationship between a detected shape based on the data corresponding to the actual surface shape of the rail and the reference shape, sets one of the detected shape and the reference shape as the shape to be converted and the other as the shape to be compared, converts the shape to be converted based on the shape relationship to a monitoring converted shape, and identifies the amount of wear on the rail by comparing the comparison shape and the monitoring converted shape.

14. Rail shape monitoring system according to claim 13, wherein the processing unit determines the amount of wear on the top of the rail by comparing the portion corresponding to the top of the rail with the comparison target shape and the monitoring conversion shape.

15. Rail shape monitoring system according to claim 13 or claim 14, wherein the processing unit determines the amount of wear on the side surface of the rail head by comparing a portion corresponding to the side surface of the rail head with the comparison target shape and the monitoring conversion shape.

16. A rail shape monitoring method comprising: acquiring data corresponding to the actual surface shape of the rail; acquiring reference shape data including the reference shape of the rail; calculating the shape relationship between the detected shape based on the data corresponding to the actual surface shape of the rail and the reference shape; using one of the detected shape and the reference shape as the shape to be converted and the other as the shape to be compared; converting the shape to be converted based on the shape relationship to a monitoring conversion shape; and outputting monitoring data based on the monitoring conversion shape.

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