Wheel turning planning support device, wheel turning planning support system, and wheel turning planning support method

Abstract

To contribute to optimization of a life cycle cost of a wheel in a railway vehicle.SOLUTION: A wheel rotary cutting plan support device in a railway vehicle traveling on a railroad comprises a processing unit which calculates an estimated shape of a wheel at at least one future timing after the reference time on the basis of shape data of a wheel of the railway vehicle in the reference time and planned travel data of the railway vehicle after the reference time, calculates a predicted wheel diameter after wheel rotary cutting at at least one future timing on the basis of the estimated shape, and calculates a wheel life cycle cost evaluation value according to processing and replacement costs per one life cycle of the wheel that occur when rotary-cutting the wheel in at least one rotary cutting cycle according to at least one future timing on the basis of the predicted wheel diameter after wheel rotary cutting at at least one future timing.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] This disclosure relates to technology for assisting in wheel turning planning for railway vehicles. [Background technology]

[0002] Patent document 1 discloses a technology that uses a gyro sensor in a railway vehicle to process the output signal of a vibration sensor that detects vibrations emitted from the wheels and rotation support device attached to the railway vehicle, and detects abnormalities in the vehicle's rolling device using the processed output signal.

[0003] Patent Document 2 discloses a technique for diagnosing anomalies by arithmetically processing measurements taken by a six-axis sensor provided on a vehicle together with wheel rotation speeds and GPS signals. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2008-209229 A [Patent Document 2] JP 2012-100434 A Summary of the Invention [Problem to be solved by the invention]

[0005] The timing of wheel turning for railway vehicles must be planned so that the wheels can be turned before any abnormality occurs. Turning refers to the process of cutting the circumference of worn wheels to create a shape suitable for rolling on rails.

[0006] In Patent Documents 1 and 2, wheel abnormalities caused by wear are detected.

[0007] However, technology that only detects abnormalities in wheels does not reflect the actual wear condition of the wheels, and it is not possible to optimize the life cycle cost of the wheels.

[0008] Therefore, an object of the present disclosure is to contribute to optimizing the life cycle costs of wheels in railway vehicles. [Means for solving the problem]

[0009] The wheel turning planning support device is a wheel turning planning support device for a railway vehicle running on a track, and includes a processing unit that calculates an estimated shape of the wheel for at least one future time period after a reference time based on wheel shape data of the railway vehicle at a reference time and planned running data of the railway vehicle after the reference time, calculates a predicted wheel diameter after wheel turning at the at least one future time period based on the estimated shape, and calculates a wheel life cycle cost evaluation value corresponding to the processing and replacement costs per life cycle of the wheel that will be incurred when the wheel is turned at at least one rolling period corresponding to the at least one future time period based on the predicted wheel diameter after wheel turning at the at least one future time period.

[0010] The wheel turning planning support method is a wheel turning planning support method for a railway vehicle running on a track, which comprises the steps of: (a) calculating an estimated shape of the wheel for at least one future time period after a reference time based on wheel shape data of the railway vehicle at a reference time and planned running data of the railway vehicle after the reference time; (b) calculating a predicted wheel diameter after wheel turning at the at least one future time period based on the estimated shape; and (c) calculating a wheel life cycle cost evaluation value corresponding to the processing and replacement costs per life cycle of the wheel that will be incurred when the wheel is turned at at least one rolling period corresponding to the at least one future time period based on the predicted wheel diameter after wheel turning at the at least one future time period. Effect of the Invention

[0011] The present wheel turning planning support device and method contribute to optimizing the life cycle costs of wheels in railway vehicles. [Brief description of the drawings]

[0012] [Figure 1] 1 is a block diagram showing a wheel turning plan support device according to a first embodiment; [Diagram 2] 1 is a flowchart showing a process related to a cutting plan support method. [Diagram 3] FIG. 11 is a block diagram showing a wheel turning planning support system according to a second embodiment. [Figure 4] 1 is a flowchart showing a process related to a cutting plan support method. [Diagram 5] FIG. 2 is a diagram showing an example of a wheel shape in a vertical cross section along an axle. [Figure 6] This is a diagram in which changes in curvature, changes in acceleration (or yaw rate), and changes in vertical acceleration are correlated to distance in a trajectory. [Figure 7] 10 is a flowchart showing an example of a process for calculating an estimated shape of a wheel at a future time. [Figure 8] FIG. 2 is a diagram showing a data flow of the above process. [Figure 9] FIG. 11 is a diagram showing an example of data input for a motion analysis simulation. [Figure 10] FIG. 11 is an explanatory diagram for explaining a process for calculating a predicted wheel diameter after turning. [Figure 11] FIG. 11 is a graph showing changes in wheel diameter, flange thickness, and flange height with respect to travel distance. [Figure 12] FIG. 13 is a diagram showing the change in probability of derailment occurrence with respect to the margin from the limit value. [Figure 13] FIG. 13 is a diagram showing the change in failure probability with respect to an increase in the degree of bogie vibration. [Figure 14] FIG. 13 is a diagram showing the change in failure probability with respect to the amount of bogie vibration exceeding the standard. [Figure 15] 13 is a flowchart illustrating an example of processing in a terminal device. [Figure 16] FIG. 4 is a diagram showing an example of a display screen of a display device in the terminal device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] {First embodiment} Hereinafter, a wheel turning plan support device and a wheel turning plan support method according to the first embodiment will be described. FIG 1 is a block diagram showing a wheel turning plan support device 30.

[0014] The wheel turning planning support device 30 is a device that supports the formulation of a wheel turning plan for a railway vehicle. That is, when a railway vehicle runs on a track, the wheels wear and deviate from their normal shape. In order to return the wheels to their normal shape, wheel turning is performed.

[0015] The wheel turning planning support device 30 includes, for example, a computer having a processor 32 and a storage device 34. The processor 32 is a processing unit configured with circuits such as a CPU (Central Processing Unit). The storage device 34 is a non-volatile storage device such as an HDD (hard disk drive) or an SSD (solid-state drive). The storage device 34 stores a program 35, scheduled travel data 36, ​​cost data 37, and the like.

[0016] The program 35 includes instructions for the processor 32 to execute processing as a milling planning support device.

[0017] The planned running data 36 is data related to the running of the railcar in the future. For example, the planned running data 36 includes track route information 36a, running speed information 36b, and running schedule information 36c. The track route information 36a is information related to the track route on which the railcar will run in the future. By referring to the track route information 36a, for example, the distance on which the railcar will run and the shape of the curve on the track on which the railcar will run can be specified. The running speed information 36b is information related to the running speed at which the railcar will run. The running speed is associated with a position on the track route. By referring to the running speed information 36b, the planned running speed at which the railcar will run at each position on the route in the future can be specified. The running schedule information is information related to the running schedule of the railcar. The running schedule information 36c is, for example, information in which a running time and a running speed are associated with a position on the track route. By referring to the train schedule information 36c, the number of times the railcar will travel on each track and the travel speed on each section in the future period can be identified.

[0018] The cost data 37 includes the cost related to the turning of the wheels. For example, the cost data 37 includes data related to the cost for turning the wheels and the cost for replacing the wheels. The cost for replacing the wheels includes the wheel price and the labor cost for replacing the wheels. By referring to the cost data 37, a wheel life cycle cost evaluation value according to the cost per one life cycle of the wheel can be calculated in the case where the wheels are turned at a period corresponding to a future time. Note that one life cycle of the wheel is a cycle in which the wheel is used in a range in which the wheel diameter is equal to or exceeds a limit value. That is, the wheel diameter of the wheel gradually becomes smaller due to wear and turning. A limit value is specified for the wheel diameter. When the wheel diameter falls below the limit value or falls below the limit value, the wheel is replaced. The cycle until the wheel is replaced in consideration of the limit value is one life cycle of the wheel.

[0019] The processor 32 performs calculations in accordance with instructions written in the program 35 while referring to the planned driving data 36 and cost data 37, thereby performing processing as the cutting planning support device 30, which will be described later.

[0020] The turning planning support device 30 may include an input unit 40 and a display device 42. The input unit 40 is a keyboard, a touch panel, or the like, and is a device that accepts input from a user. The planned traveling data 36, ​​the cost data 37, and the like can be input to the turning planning support device 30 via the input unit 40. The display device 42 is, for example, a liquid crystal display device. The wheel life cycle cost evaluation value, the candidate turning period, and the like may be displayed on the display device 42. An example of the display on the display device 42 may be the same as an example of the display on the display device 197 described later.

[0021] FIG. 2 is a flowchart showing a process in which the wheel turning plan support device 30 executes the wheel turning plan support method.

[0022] In step S1, an estimated shape of the wheel at at least one future time after a reference time is calculated. The reference time is, for example, a time when a turning plan is formulated. The reference time may be a time when an estimated shape of the wheel is calculated. The reference time is associated with a travel distance of the wheel at the reference time. The future time may be set by the travel distance after the reference time. For example, a time point at which a predetermined distance has been traveled after the reference time may be set as the future time. The future time may be set by a time after the reference time.

[0023] The estimated shape of the wheels in the future time is calculated based on the shape data of the wheels of the railcar at the reference time and the planned running data of the railcar after the reference time. The shape data of the wheels of the railcar at the reference time may be actual measurement data of the wheels. The shape data of the wheels of the railcar at the reference time may be data estimated based on data acquired during past running of the railcar. The amount of wear of the wheels in the future period from the reference time to the future time can be calculated based on the planned running data 36. For example, a motion analysis simulation of the wheels rolling on the track is performed based on the estimated shape of the wheels in the future time, the route information 36a, and the running speed information 36b. From the motion analysis simulation result, the amount of slip at each position of the wheel and the force in the slip direction are obtained. The larger the amount of slip, the larger the amount of wear, and the larger the force in the slip direction, the larger the amount of wear. Therefore, the amount of wear at each position of the wheel can be calculated as an amount that is positively correlated with the amount of slip and the force in the slip direction. An estimated wheel shape at a future time is calculated by deleting from the shape data of the railway vehicle's wheels at a reference time an amount equivalent to the amount of wear calculated based on the railway vehicle's planned running data from the reference time onwards.

[0024] In the next step S2, a predicted wheel diameter after turning is calculated. That is, the worn wheel is turned so as to have a normal shape. That is, since an estimated shape of the worn wheel in the future is estimated in step S1, it is possible to estimate how much turning is required to return the estimated shape to the normal shape. The estimated wheel diameter after turning can be calculated as the predicted wheel diameter after turning.

[0025] In the next step S3, a wheel life cycle cost evaluation value per one life cycle of the wheel is calculated. The wheel life cycle cost evaluation value is a value according to the processing and replacement costs per one life cycle of the wheel. The wheel life cycle cost evaluation value may be a value indicating the cost itself, or may be a value positively or negatively correlated with the cost. The processing and replacement costs per one life cycle of the wheel include the cost required for replacing the wheel itself and the cost required for rolling. Therefore, if the number of rollings per one life cycle is estimated, the total rolling cost per one life cycle can be calculated by multiplying the cost required for one rolling by the number of rollings. The wheel life cycle cost evaluation value per one life cycle can be calculated by adding the cost required for replacing the wheel itself to the total rolling cost per one life cycle.

[0026] The number of times of rolling per one life cycle can be calculated by calculating the reduced wheel diameter due to wear as in step S1, and calculating the reduced wheel diameter due to rolling as in step S2. For example, the number of times of rolling per one life cycle can be calculated as follows. The period from the initial time of the newly manufactured wheel to the above-mentioned future time is set as a rolling cycle, and rolling is repeated at a cycle corresponding to the rolling cycle. The cycle for repeating rolling may be, for example, the same as the initial rolling cycle, or may be a cycle in which the initial rolling cycle is gradually reduced. Since the diameter of the newly manufactured wheel is a known value in design, the reduced wheel diameter in the first cycle is calculated based on the diameter of the newly manufactured wheel and the predicted wheel diameter after rolling calculated in step S2. The reduced wheel diameter in the second and subsequent rolling cycles can also be estimated based on the reduced wheel diameter in the first rolling cycle. If it is considered that the wheel diameter decreases by the estimated reduced wheel diameter for each rolling cycle, and rolling is performed under conditions in which the wheel diameter after reduction exceeds or is equal to or greater than the limit value, the upper limit number of times of rolling can be obtained.

[0027] It is not necessary that the processing be performed based on the shape and diameter of a newly manufactured wheel, but the processing may be performed based on the shape and diameter of the wheel at the time of the previous turning.

[0028] The wheel life cycle cost evaluation value per one life cycle may be an evaluation value including not only the processing and replacement costs per one life cycle of the wheel but also other cost factors.

[0029] In the next step S4, the wheel life cycle cost evaluation value per one life cycle is output. The evaluation value may be displayed as a recognizable number on the display device 42. The evaluation value may be output as data and stored in the storage device 34. The evaluation value may be output in association with a future time that becomes a condition for performing the evaluation.

[0030] When the processes from step S1 to step S3 are performed for a plurality of future periods, the evaluation values ​​may be associated with each of the plurality of future periods and output. Also, the milling period corresponding to the evaluation value that results in the lowest cost may be output as a candidate milling period.

[0031] According to the wheel turning planning support device and the wheel turning planning support method configured in this way, an estimated wheel shape for at least one future time period after a reference time is calculated based on the wheel shape data and the scheduled running data, a predicted wheel diameter after wheel turning for the at least one future time period is calculated based on this estimated shape, and a wheel life cycle cost is calculated based on this predicted wheel diameter after wheel turning. The life cycle cost of a railway vehicle can be optimized based on this wheel life cycle cost evaluation value.

[0032] For example, it is conceivable to measure the shape of the wheels periodically according to the travel distance or travel time of the railway vehicle, and to plan the timing of turning based on the measured values. When carrying out such a planning method, it is necessary to carry out periodic measurement and confirmation work, which is costly.

[0033] In this embodiment, an estimated wheel shape in the future is calculated based on the planned running data. Therefore, the rolling time can be planned by reducing the periodic actual measurement of the wheel shape. Note that the wheel shape may be measured periodically or irregularly.

[0034] Also, for example, it is conceivable to detect abnormal vibrations caused by wheel wear from the vibrations of the vehicle, and perform rolling when abnormal vibrations occur. Abnormal vibrations caused by wheel wear can occur, for example, when the wheels slide on the track and a part of the wheel tread becomes flat. According to this idea, rolling is considered when abnormal vibrations are detected, but it is not useful for formulating a rolling plan that takes into account wear caused by normal driving.

[0035] The turning planning support device 30 and turning planning support method of this embodiment calculate the estimated shape and predicted wheel diameter of a wheel in the future, and calculate the wheel life cycle cost based on the predicted wheel diameter after wheel turning, which can be useful for properly formulating a turning plan that takes into account wear due to normal driving.

[0036] In addition, abnormal vibrations caused by wheel wear may be detected from the vibrations of the vehicle, and turning may be performed when abnormal vibrations are detected.

[0037] Note that one or more examples described in the second embodiment can be applied as specific examples of the contents described in the first embodiment, or as additional examples.

[0038] {Second embodiment} A wheel turning planning support device, a wheel turning planning support system, and a wheel turning planning support method according to the second embodiment will be described. Fig. 3 is a block diagram showing a wheel turning planning support system 100. In the description of this embodiment, the description of the same components as those described in the first embodiment may be omitted.

[0039] The turning plan support system 100 is a system that supports the formulation of a turning plan for wheels 50 of a railway vehicle 20.

[0040] An example of a railcar 20 will be described. The railcar 20 runs on a track 16. The track 16 is a road that guides the railcar 20 along a route. Here, the track 16 includes two rails. The two rails are laid parallel to each other on the ground with sleepers or the like interposed therebetween. The track may be provided at a position above ground level by an elevated bridge or the like. The track may be provided in a tunnel dug underground.

[0041] The railcar 20 includes a car body 22 and a bogie 24. The bogie 24 includes a bogie frame 25 and a plurality of wheels 50. The plurality of wheels 50 are rotatably supported on the left and right parts of the bogie frame 25 via axles. The part supporting the axles is sometimes called an axle box. In this embodiment, the traveling direction of the railcar 20 is sometimes called the front side, and the retreating direction is sometimes called the rear side. Also, the left and right sides may be based on the traveling direction as seen from the railcar 20. In the direction of gravity, the side where gravity is applied may be called the lower side, and the opposite side may be called the upper side. The left and right wheels 50 run on the rails 17 while being guided by the two rails 17. The bogie 24 supports the car body 22 from below. The bogie 24 runs on the track 16, and the railcar 20 including the car body 22 runs along the track 16. The railcar 20 may be any vehicle that travels on the track 16, and may be any of an electric train, a freight train locomotive, a freight car, a passenger train locomotive, and a passenger car. The freight car or passenger car may be a trailer car pulled by the locomotive, or may be a powered car that has its own power. The locomotive may be an electric locomotive, or an internal combustion engine such as a diesel locomotive.

[0042] A running state sensor 28 and a communication device 29 are installed in the railcar 20 .

[0043] The running state sensor 28 is a sensor that detects the behavior of the wheels 50 while the railcar 20 is running. The behavior of the wheels 50 is detected, for example, by the position or acceleration of the wheels 50 in a direction intersecting the longitudinal direction of the track 16 relative to the position in the longitudinal direction of the track 16. The behavior of the wheels 50 may be detected for each individual wheel 50. The behavior of the wheels 50 affects the behavior of the bogie frame 25 that supports the wheels 50 and the behavior of the car body 22. For this reason, the behavior of the wheels 50 may be detected as the behavior of the bogie frame 25 that supports a plurality of wheels 50, or may be detected as the behavior of the car body 22. The processing related to the turning plan support may be performed for all the wheels 50 of the railcar 20, or may be performed for one wheel 50 for each bogie 24 or for each railcar 20.

[0044] More specifically, the running state sensor 28 is attached to the bogie frame 25, and detects the behavior of the bogie frame 25 to detect the behavior of the wheels 50 supported by the bogie frame 25. The running state sensor 28 may include, for example, an acceleration sensor that detects the lateral acceleration of the bogie 24. The running state sensor 28 may include a gyro sensor that detects the yaw rate of the bogie 24. The lateral acceleration and the yaw rate of the bogie 24 show similar changes in response to the behavior of the bogie 24 caused by the curved portion of the track 16. For this reason, the running state sensor 28 may include at least one of an acceleration sensor that detects the lateral acceleration of the bogie 24 and a gyro sensor that detects the yaw rate of the bogie 24. The running state sensor 28 may include an acceleration sensor that detects the vertical acceleration of the bogie 24.

[0045] The communication device 29 is a device including a circuit that communicates with the turning plan support device 130 via the communication network 13. A signal from the running state sensor 28 is transmitted to the turning plan support device 130 via the communication device 68 and the communication network 13. The detection result of the running state sensor 28 may be correlated with the running position of the railcar 20 on the track 16 and transmitted to the turning plan support device 130. For example, based on the output of a speed generator mounted on the railcar 20 or the output of a GPS (Global Positioning System) receiving unit, the detection result of the running state sensor 28 may be correlated with the running position of the railcar 20 on the track 16. The communication network 13 may be a wired or wireless type, or a combination of these. The communication network 13 may be a public communication network or a communication network using a dedicated line.

[0046] It is not essential that the detection results by the running state sensor 28 are transmitted to the turning plan support device 130 via the communication device 29 and the communication network 13. The railway vehicle 20 may be equipped with a non-volatile storage device that stores the detection results by the running state sensor 28 as running state data. In this case, the running state data stored in the non-volatile storage device may be transferred to the turning plan support device 130 at a stop or the like of the railway vehicle 20. In addition, the turning plan support device 130 may be equipped on the railway vehicle 20.

[0047] The vehicle turning planning support system 100 includes a wheel turning planning support device 130 and a terminal device 190.

[0048] The wheel turning planning support device 130, like the first embodiment, includes a computer having, for example, a processor 132 and a storage device 134. The storage device 134 stores a program 135, scheduled driving data 36, ​​cost data 37, driving state data 138, etc. The storage device 134 may also store wheel shape data 136.

[0049] The program 135 includes instructions for the processor 32 to execute processing as the milling plan support device 130 in the second embodiment.

[0050] As described in the first embodiment, the planned running data 36 is data related to future running of the railway vehicle, and the cost data 37 is data including costs related to wheel turning.

[0051] The running state data 138 is data including the running state of the railcar 20 while it is running. For example, the results detected by the running state sensor 28 are transmitted to the turning plan support device 130, and the data is accumulated. As described above, the running state data 138 may be, for example, data corresponding to the behavior of the wheels 50 based on the detection results of the running state sensor 28, which is associated with the running position of the railcar 20 on the track 16.

[0052] The milling plan support device 130 may include an input unit 40 and a display device 42 as described in the first embodiment.

[0053] The terminal device 190 includes, for example, a computer having a processor 192 and a storage device 194. The processor 192 is configured with circuits such as a CPU. The storage device 194 is a non-volatile storage device such as an HDD or SSD. The storage device 194 stores a terminal side program 195 and the like.

[0054] The terminal device 190 includes an input unit 196 and a display device 197. The input unit 196 is a keyboard, a touch panel, or the like, and is a device that accepts input from a user. The input unit 196 is an example of an input receiving unit that accepts input of the processing and replacement costs of the wheel 50 from the user. The display device 197 is, for example, a liquid crystal display device. The display device 197 is an example of a display device that displays an image based on the wheel life cycle cost evaluation value.

[0055] The terminal device 190 is equipped with a communication device 198. The communication device 198 is a device including a circuit for communicating with the turning planning support device 130 via the communication network 13. The processing and replacement costs of the wheel 50 input via the input unit 196 are transmitted to the turning planning support device 130 via the communication network 13. When the turning planning support device 130 calculates a wheel life cycle cost evaluation value based on the processing and replacement costs of the wheel 50, data based on the calculation results are transmitted to the terminal device 190. An image based on the calculation results is displayed on the display device 197.

[0056] The processor 192 performs calculations in accordance with the instructions written in the terminal side program 195, thereby accepting input via the input unit 196, transmitting the input data to the turning planning support device 130, receiving data based on the wheel life cycle cost evaluation value calculated by the turning planning support device 130, and displaying images based on the data.

[0057] FIG. 4 is a flowchart showing a process in which the wheel turning plan support device 130 executes the wheel turning plan support method.

[0058] In step S11, shape data of the wheels 50 at the reference time is set. As described in the first embodiment, the shape data of the wheels of the railway vehicle at the reference time may be actual measurement data of the wheels and stored in advance in the storage device 134, or may be data estimated based on data acquired during past travel of the railway vehicle.

[0059] Step S12 is the same process as step S1 in the first embodiment. Step S13 is the same process as step S2 in the first embodiment. Steps S14 and S15 are the same processes as those described in step S3 in the first embodiment.

[0060] The processes from step S12 to step S15 are executed for each of a plurality of future times. The plurality of future times may be set based on a travel distance after a reference time. For example, the plurality of future times may be set as times for each predetermined distance traveled after a reference time, within a range of a distance considered to be the life of the wheel 50.

[0061] In the next step S16, a result based on the calculated evaluation value is output. By viewing the result based on the evaluation value, the user can consider a milling plan, for example, a cycle in which milling should be performed.

[0062] After the result based on the evaluation value is output, the process may end. The above process may be executed by a plurality of processors, or may be distributed among the plurality of processors.

[0063] A specific example of the process of each of the above steps will be described.

[0064] An example of the process of estimating the shape data of the wheel 50 at the reference time in step S11 will be described.

[0065] A specific example of the shape of the wheel 50 will now be described. Fig. 5 is a diagram showing an example of the shape of a wheel in a vertical cross section along the axle. In Fig. 5, an example of the shape of a newly manufactured wheel 50 is shown by line 55. Examples of the shape of the wheel 50 after wear are shown by lines 56a, 56b, and 56c.

[0066] The outer periphery of the wheel 50 is a rim portion 51. The rim portion 51 includes a tread surface 52 and a flange portion 53.

[0067] The tread surface 52 is the outer portion of the outer peripheral surface of the rim portion 51 in the vehicle width direction. With the tread surface 52 in contact with the upward surface of the rail, the wheel 50 can roll on the rail. The tread surface 52 is formed in a shape that gradually increases in diameter toward the inside in the vehicle width direction. In the cross section shown in FIG. 5, the tread surface 52 is expressed as a shape that has a gradient with respect to a direction horizontal to the rotation axis of the wheel 50.

[0068] The flange portion 53 is an annular protrusion that protrudes from the inner side of the rim portion 51 in the vehicle width direction to the outer side in the radial direction of the wheel 50. The flange portion 53 contacts the rails between the rails, thereby restricting the position of the wheel 50 in the vehicle width direction relative to the rails from the inner side in the vehicle width direction. Since the left and right wheels 50 run on the rails, the position of the railway vehicle 20 is restricted from both sides in the vehicle width direction relative to the pair of rails. In the cross section shown in FIG. 5, the flange portion 53 is expressed as a protruding shape that draws a curve that is convex downward. The tread surface 52 and the flange portion 53 are connected in a curved line.

[0069] The shape of the wheel 50 is specified by, for example, a flange height Hf, a flange thickness Wf, a tread gradient θ, and a wheel diameter R. In Fig. 5, the flange height Hf, the flange thickness Wf, the tread gradient θ, and the wheel diameter R are shown for an example of the shape of the wheel 50 after wear.

[0070] The wheel diameter R may be, for example, the diameter of the wheel 50 at a portion of the tread 52 that is most likely to come into contact with the rail. The wheel diameter R may be, for example, the diameter of the wheel 50 at a position that is a predetermined distance D1 inward along the central axial direction from the outward surface of the wheel 50 in the vehicle width direction. The wheel diameter R may be specified based on the inward surface of the wheel 50 in the vehicle width direction. The wheel diameter R may be specified by the diameter at a position (for example, a central position) at a predetermined ratio between the outward surface and the inward surface of the wheel 50 in the vehicle width direction in the axial direction of the wheel 50.

[0071] The tread gradient θ may be, for example, an angle θ at which a portion of the tread 52 that is most likely to come into contact with the rail is inclined with respect to the central axis of the wheel 50. For example, the tread gradient θ may be an angle θ at which a portion of the tread 52 that is the reference for the wheel diameter R is inclined with respect to the central axis of the wheel 50.

[0072] The flange height Hf is, for example, a height based on any part of the tread surface 52. The flange height Hf may be, for example, a protruding length with respect to the wheel diameter R, that is, a value obtained by subtracting the wheel diameter R from the diameter of the most protruding top of the flange portion 53.

[0073] The flange thickness Wf is, for example, the thickness of a portion of the flange portion 53 that abuts against the rail from a side surface. The flange thickness Wf may be, for example, the thickness of the flange portion 53 along the central axis direction at a position obtained by adding a predetermined distance D2 to the wheel diameter R.

[0074] The shape of the newly manufactured wheel 50 is manufactured according to the design shape, and therefore can be specified by at least the flange height Hf, the flange thickness Wf, the tread gradient θ, and the wheel diameter R.

[0075] The shape data of the wheel 50 at the reference time in step S11 may be shape data of the wheel 50 after it has been worn down due to running. The shape data of the wheel 50 at the reference time may be data estimated based on data acquired during past running of the railcar. An example of a process for estimating the data will be described.

[0076] As described above, the shape of the wheel 50 after wear can be specified by, for example, the flange height Hf, the flange thickness Wf, the tread gradient θ, and the wheel diameter R.

[0077] FIG. 6 is a diagram in which the change in curvature of the track 16, the change in acceleration (or yaw rate) of the bogie 24, and the change in vertical acceleration of the bogie 24 are associated with the distance on the track 16. The change in curvature of the track 16 is based on the track path information 36a including the track curvature information. The change in acceleration (or yaw rate) of the bogie 24 relative to the track 16 and the change in vertical acceleration of the bogie 24 are based on data detected by the running state sensor 28. The figure shows the railcar 20 moving from a straight path to a curved path and then to a straight path. The curved path Q includes an entrance transition curve Qa, a circular curve Qb, and an exit transition curve Qc. The entrance transition curve Qa is a portion where the curvature gradually increases toward the curvature of the circular curve Qb, and the circular curve Qb is a portion where the largest constant curvature continues, and the exit transition curve Qc is a portion where the curvature gradually decreases from the curvature of the circular curve Qb.

[0078] An example of a process for estimating the flange thickness Wf will be described. When the railcar 20 enters the entrance transition curve Qa, the bogie 24 is deflected toward the outer periphery of the entrance transition curve Qa by centrifugal force, and the flange portion 53 comes into contact with the rail. The more the flange portion 53 wears, the later the flange portion 53 comes into contact with the rail. Therefore, the greater the distance from the time when the bogie 24 enters the curve Q until the flange portion 53 comes into contact with the rail, the smaller the thickness of the flange portion 53 becomes. Note that the distance from the time when the bogie 24 enters the curve Q until the flange portion 53 comes into contact with the rail can be specified by referring to the position where the peak value of the bogie acceleration or the bogie yaw rate in the opposite direction to the curve Q appears after the bogie 24 enters the curve Q based on the route information 36a (see the boxed portion A1 in FIG. 6).

[0079] In addition, when the railcar 20 travels on the circular curve Qb, the wheels 50 on the inner circumference side contact the rail at the portion with a smaller diameter, and the wheels 50 on the outer circumference side contact the rail at the portion with a larger diameter, thereby absorbing the path difference between the inner circumference side and the outer circumference side. Here, the more worn the flange portion 53 is, the greater the amount of path difference that can be absorbed between the inner circumference side and the outer circumference side. Therefore, in the case of a circular curve Qb with a small radius of curvature, if the flange portion 53 is not worn, there is a possibility that the path difference between the inside and outside cannot be sufficiently absorbed. In this case, if the path difference between the inside and outside cannot be sufficiently absorbed, slippage occurs between the wheels 50 and the rail, and the vibration in the left-right direction or the yaw direction caused by this slippage becomes large. Therefore, it can be considered that the smaller the vibration strength of the bogie lateral acceleration or the bogie yaw rate, the smaller the thickness of the flange portion 53 (see the boxed area A2 in Figure 6). The vibration strength may be the maximum peak value, average value of the peak values, or effective value of the bogie lateral acceleration or the bogie yaw rate in the periodic component, or it may be an integrated value over a certain interval. Note that the vibration strength is expressed by the strength of the signal representing the bogie lateral acceleration or the bogie yaw rate.

[0080] A reference table is created based on a theory, experiment, or empirical rule in which at least one parameter, for example, the distance from when the bogie 24 enters the curve Q until the flange portion 53 contacts the rail and the vibration intensity in a predetermined periodic component on the circular curve Qb, corresponds to the thickness of the flange portion 53. At least one of the parameters is identified based on the route information 36a and data detected by the traveling state sensor 28, and the thickness of the flange portion 53 can be estimated based on the identified parameter and the reference table. A formula for estimating the thickness of the flange portion 53 from the at least one parameter may be preset.

[0081] An example of a process for estimating the tread gradient θ will be described. When the railcar 20 passes through the curve Q and then enters a straight path, the lateral vibration of the bogie 24 gradually decreases. The larger the tread gradient θ, the slower the vibration converges. Thus, it can be considered that the tread gradient θ increases as the vibration intensity in the periodic component corresponding to the lateral vibration when entering the straight path from the curve Q becomes equal to or falls below a predetermined reference value or the distance until the vibration converges increases (i.e., as the distance until the vibration converges increases) (see the boxed portion A3 in FIG. 6). The vibration intensity may be a peak value or an effective value of the bogie lateral acceleration or the bogie yaw rate in the periodic component, or may be an integral value over a certain section.

[0082] Then, for example, a reference table is created based on theory, experiment, or empirical laws, in which the distance from the point of entering the straight path from the curve Q until the vibration intensity in a predetermined periodic component falls below a predetermined reference value or falls below the reference value corresponds to the tread gradient θ. The distance until the vibration converges is specified based on the path information 36a and the data detected by the running state sensor 28, and the tread gradient θ can be estimated based on the specified distance and the reference table. A formula for estimating the tread gradient θ using the distance until the vibration converges as a parameter may be preset.

[0083] An example of a process for estimating the wheel diameter R will be described. A periodic waveform caused by the rotation of the wheels 50 appears in the change in the bogie vertical acceleration detected by the traveling state sensor 28. The smaller the distance between peak values ​​appearing in the change waveform of the bogie vertical acceleration, the smaller the wheel diameter R. Alternatively, the smaller the frequency component included in the change waveform, the smaller the wheel diameter R.

[0084] A reference table that associates the wheel diameter R with the distance between peak values ​​(see distance A4 in FIG. 6) or frequency components appearing in the waveform of the change in the vertical acceleration is created theoretically, experimentally, or based on empirical laws. Based on the data detected by the traveling state sensor 28, the distance between peak values ​​appearing in the waveform of the change in the bogie vertical acceleration or the frequency components contained in the waveform are extracted, and the wheel diameter R can be obtained based on the obtained distance between peak values ​​or frequency components and the reference table. A formula for estimating the wheel diameter R using the distance between peak values ​​or frequency components as parameters may be preset.

[0085] It can be considered that the top of the flange portion 53 of the wheel 50 does not wear because it does not contact the rail. Therefore, when the wheel diameter R is obtained as described above, the flange height Hf can be specified based on the original position of the top of the flange portion 53.

[0086] As described above, based on the running state data 138, the shape of the wheel 50 after wear can be specified by the flange height Hf, the flange thickness Wf, the tread gradient θ, and the wheel diameter R. In FIG. 5, based on the wheel diameter R and the tread gradient, a part of the tread 52 can be represented by a line 56a. Based on the flange thickness Wf, a part of the base end of the flange portion 53 on the tread 52 side can be represented by a line 56b. The line 56b may be represented as a line segment inclined at a predetermined angle with respect to the wheel 50. The top and the part of the flange portion 53 opposite to the tread 52 can be considered to be not worn and to retain their initial shape, and can be represented by a line 56c. The shape of the wheel 50 at the reference time is estimated for the part 56e between the lines 56a, 56b, and 56c by interpolating the part so as to connect them with a smooth line using spline interpolation or the like.

[0087] As already mentioned, the shape data of the wheels at the reference time may be actual measurement data. Also, in cases where past running state data of the railway vehicle 20 is known, the amount of wear may be estimated from the past running state data, and the shape data of the newly manufactured wheels 50 may be subtracted by the amount of wear to estimate the shape of the wheels 50 at the reference time, as in the process of step S12.

[0088] An example of the process of calculating the estimated shape of the wheel 50 at a future time in step S12 will be described. Fig. 7 is a flow chart showing an example of the process of calculating the estimated shape of the wheel 50 at a future time. Fig. 8 is a diagram showing the data flow in the process.

[0089] In step S21, the initial reference wheel shape for performing this process is set. The initially set wheel shape is the wheel shape at the reference time.

[0090] In the next step S22, a motion simulation process of the wheels 50 relative to the track 16 during future travel of the railway vehicle 20 is executed based on the wheel shape, the route information 36a of the track 16, and the travel speed information 36b.

[0091] For example, a certain period of time determined in advance from a reference time is set as the estimated period. Based on the route information 36a and the travel schedule information 36c, a plurality of representative curve types and the running speeds on the curves in the estimated period are extracted. For example, based on the route information 36a and the travel schedule information 36c, curve types that are most frequently traveled in the estimated period are extracted. Furthermore, for each extracted curve type, a running speed that appears most frequently is extracted. This allows a plurality of combinations of representative curve types and running speeds to be extracted.

[0092] Here, it is assumed that the wheel shape is the wheel shape set in step S21, and the rail shape is a known shape. It is also assumed that the mass of the railway vehicle 20 and the friction coefficient between the wheel 50 and the rail are known from design or experimental knowledge. From these known conditions, the physical conditions on the ground surface, the curvature determined by the curve type, and the running speed conditions, it is possible to estimate the behavior of the wheel 50 relative to the rail for each combination of the curve type and the running speed. More specifically, it is possible to perform a simulation process of the direction in which the wheel 50 moves relative to the rail and what kind of force it receives.

[0093] FIG. 9 is a diagram showing an example of data input for a motion analysis simulation. In this example, as route information 36a, the start and end kilometres of each curve from No. 1 to No. 6 (i.e., the position of the track 16 from a position based on the start point of the track 16, etc.), the entrance transition curve length and curvature of each curve, the exit transition curve length and curvature of each curve, the circular curve length and curvature of each curve, the cant amount of each curve, the slack of each curve, etc. are input. In addition, the wheel shape is input in No. 7. Furthermore, as running schedule information 36c, the running speed corresponding to the kilometre in No. 8 and the running schedule during the estimated period in No. 9 are input. A more accurate simulation is possible by simulating not only the curvature of the curve but also the cant amount of each curve, the slack of each curve, etc.

[0094] In the simulation results, at each position in the central axis direction of the wheel 50, the amount of slippage in two plane axial directions between the rail and the wheel 50 and the force in the slip direction generated on the wheel 50 in the two plane axial directions are calculated. Also, at each position in the central axis direction of the wheel 50, the amount of slippage in the rotation direction of the plane vertical axis between the rail and the wheel and the torque generated on the wheel 50 with the plane vertical axis as the center are calculated. The two plane axial directions may be horizontal directions perpendicular to the direction of gravity, and the direction of the plane vertical axis may be the direction of gravity. Here, the product of the amount of slippage and the force in the slip direction (torque in the case of slippage in the rotation direction) is defined as the wear coefficient. Between two objects, the greater the amount of slippage, the greater the amount of wear, and the greater the force in the slip direction, the greater the amount of wear. Therefore, the wear coefficient is a coefficient that indicates the ease of wear. For example, at each position along the central axis of the wheel 50, the time integral of the product of the amount of slippage between the rail and the wheel 50 in two axial directions on a plane and the force in the slip direction generated on the wheel 50, and the time integral of the product of the amount of slippage between the rail and the wheel 50 in the rotational direction of the axis perpendicular to the plane and the torque generated on the wheel are calculated, and the wear coefficient is determined by adding up both time integrals.

[0095] By executing the above simulation process for each combination of representative curve type and traveling speed, a curve speed-wear table is created in which the wear coefficient and wear location (each position in the direction along the central axis of the wheel 50) correspond to each combination of curve type and traveling speed (see Figure 8).

[0096] In order to improve processing efficiency, the simulation process is performed for each combination of representative curve types and running speeds, but the simulation process may be performed for each combination of all curve types and running speeds on which the railway vehicle 20 is to run.

[0097] In the next step S23, the amount of wear of the wheel 50 due to the curve during the estimation period is calculated based on the curve speed-wear table and the travel timetable information 36c. That is, since the wear coefficient indicates the ease of wear, wear occurs according to the friction coefficient determined by the table each time the wheel satisfies the conditions of the curve and travel speed defined in the table. The amount of wear may be calculated as an amount that is positively correlated with the wear coefficient, and for example, the amount of wear may be calculated as an amount that is simply proportional to the wear coefficient or an amount that increases polymorphically. The wear coefficient is determined for each portion of the wheel 50 divided along the central axis direction, and the amount of wear is also calculated for each portion of the wheel 50 divided along the central axis direction.

[0098] In addition, based on the travel timetable information 36c, the planned number of times that the wheel 50 will travel for each combination of the curve type and the travel speed during the estimation period is specified. The amount of wear that may occur due to the curve during the estimation period is calculated by integrating the above-mentioned wear amount according to the planned number of times that the wheel 50 will travel for each combination of the curve type and the travel speed.

[0099] In the next step S24, the amount of wear due to acceleration and deceleration is added. That is, based on the vehicle speed in the travel timetable information 36c, the presence or absence and amount of acceleration and deceleration of the railway vehicle 20 at each position on the track 16 is identified. Acceleration and deceleration cause the wheels 50 to slip on the rails, so the amount of wear due to that slippage is added.

[0100] When acceleration or deceleration occurs in a combination of curve type and speed specified in the above curve speed-wear table, the amount of wear due to acceleration or deceleration may be added by adding a fixed value to the wear coefficient specified in the curve speed-wear table or by multiplying the coefficient by a coefficient greater than 1.

[0101] When acceleration or deceleration occurs in a straight portion of the track 16, a motion analysis simulation is performed as described above, and a straight-line-wear table is created based on the results of the simulation in which a wear coefficient is associated with each position in the central axis direction of the wheel, and the amount of wear can be added by referring to this table.

[0102] The amount of wear caused by acceleration / deceleration may be increased as the magnitude of acceleration / deceleration increases. For example, the value added to or multiplied by the wear coefficient determined in the curved speed-wear table may be changed according to the magnitude of acceleration / deceleration. Also, for example, the straight line-wear table may be changed according to the magnitude of acceleration / deceleration.

[0103] In the next step S25, it is determined whether the estimated period has reached the estimated upper limit deadline. The estimated upper limit deadline is a preset deadline, and is set to, for example, an upper limit deadline for which milling should be performed empirically. If the estimated period has not reached the estimated upper limit deadline, the process proceeds to step S27.

[0104] In step S27, the post-wear wheel shape is calculated based on the amount of wear determined in steps S23 and S24. For example, the post-wear wheel shape is calculated by subtracting the amount of wear determined in steps S23 and S24 at each position along the central axis of the wheel based on the wheel shape set in step S21. The result of this calculation is the estimated shape of the post-wear wheel shape.

[0105] After that, the process returns to step S21. In step S21 from the second time onwards, the estimated post-wear wheel shape is set as the wheel shape.

[0106] In the following steps S22 to S24, a simulation process and calculation of the amount of wear are performed for the next estimation period from the end of the previous estimation period, using the estimated wheel shape after wear as a reference.

[0107] If it is determined in step S25 that the estimation period has reached the estimation upper limit deadline, the process proceeds to step S26. The above steps S21 to S25 and S27 are repeated until the estimation period reaches the estimation condition period, thereby estimating the wheel profile after wear at the end of each of the multiple estimation periods. This completes the calculation of the wear transition of the wheel profile from the reference time to the estimation upper limit period.

[0108] It is not necessary to estimate the amount of wear by a simulation process. For example, it is assumed that the railcar 20 has traveled on a similar track in the past and in the future, and the wear shape of the wheels 50 in the past has been actually measured or estimated from past travel data. In such a case, the wear shape of the wheels 50 in the future may be estimated from the wear shape of the wheels 50 in the past.

[0109] The process of calculating the predicted wheel diameter after turning in step S13 will be described below. Fig. 10 is an explanatory diagram for explaining the process of calculating the predicted wheel diameter after turning.

[0110] In step S12, the shape of the wheel 50 after wear in the future is estimated. The shape of the wheel 50 after wear is shown by a solid line in Fig. 10. The wheel diameter R of the wheel 50 after wear is also determined in step S12.

[0111] Since the purpose of the milling is to make the shape of the wheel 50 closer to the initial shape, the target shape after milling is determined in advance. In Fig. 10, the target shape after milling is indicated by a two-dot chain line.

[0112] The position of the target shape after rolling is set so that the entire target shape after rolling is included in the shape of the wheel 50 after wear in the future. The wheel diameter defined by the target shape after rolling at that position is the predicted wheel diameter after rolling R-Ra, which is the wheel diameter R after wear reduced by Ra.

[0113] Fig. 11 is a diagram showing the changes in wheel diameter R, flange thickness Wf, and flange height Hf relative to the travel distance obtained by the above process. As the change in wheel diameter R relative to the travel distance, a line connecting the predicted wheel diameters when rolling is performed at each future time is shown in addition to the change based on the predicted amount of wear.

[0114] As shown in the figure, it is predicted that as the travel distance increases, the wheel diameter R and the flange thickness Wf decrease, and the flange height Hf increases.

[0115] In addition, limit values ​​are set for the wheel diameter R, flange thickness Wf, and flange height Hf. The difference between the limit value and the flange thickness Wf is the tolerance Wfm(L), and the difference between the limit value and the flange height Hf is the tolerance Hfm(L).

[0116] From Fig. 10, it can be seen that if milling is performed early, milling will be performed at a stage where the tolerances Wfm(L) and Hfm(L) are close to their initial values, and there is a possibility that milling will be performed excessively. It can also be seen that if milling is delayed, there is a possibility that the tolerances Wfm(L) and Hfm(L) will be close to their limit values.

[0117] The process of calculating the number of rolling cycles Nf in step S14 will now be described. Assuming that rolling is performed with a driving distance L from when the wheel was manufactured until a future time, as one cycle, this is taken as the rolling cycle L. In this case, the wheel diameter reduction per rolling cycle calculated in step S13 is taken as Rd(L). The wheel diameter Rn at the time of manufacture is known as a design value, and the minimum wheel diameter Rr can also be considered to be known as a set value. Then, the number of rolling cycles Nf until the wheel 50 is used up while not falling below the limit value is expressed by Equation 1.

[0118]

number

[0119] An example of the process of calculating the wheel life cycle cost evaluation value in step S15 will be described.

[0120] As the wheel diameter R decreases, the number of rotations of the wheel 50 increases when the same distance is traveled. Assuming that the amount of wear increases in proportion to the amount of decrease in the wheel diameter R, the wheel life Tall when the rolling cycle is L is expressed by Equation 2. The wheel life Tall is a function of the rolling cycle L.

[0121]

number

[0122] Considering that the wheel life cycle cost Call is a cost corresponding to the processing and replacement costs per life cycle until the wheel 50 is completely used, the wheel life cycle cost Call can be defined by the following equation 3.

[0123]

number

[0124] A life cycle cost evaluation value is defined based on the wheel life cycle cost Call. The life cycle cost evaluation value may be the wheel life cycle cost Call itself, or may be an evaluation value that is positively or negatively correlated with the wheel life cycle cost Call.

[0125] In this embodiment, an evaluation function G1 with the rolling cycle L as a variable is defined by dividing the wheel life cycle cost Call by the wheel life Tall as shown in Equation 4. The calculation result of the evaluation function G1 is a cost evaluation value that represents the wheel usage cost per unit travel distance when the rolling cycle is L.

[0126]

number

[0127] By using the evaluation function G1, for example, it is possible to calculate the wheel usage cost per unit travel distance for each of a plurality of rolling cycles L, and to formulate a rolling plan including the rolling cycle by comprehensively considering the rolling cycle L and the wheel usage cost per unit travel distance, etc. Also, by deriving L so as to minimize the evaluation function G1, the wheel usage cost per unit travel distance is minimized.

[0128] Incidentally, as the flange thickness Wf and the flange height Hf approach their limit values, the probability of derailment increases. Therefore, a derailment cost evaluation value occurring due to derailment may be calculated based on an estimated shape of the wheel 50 in the future. The possibility of derailment depends on the flange height Hf and the flange thickness Wf as the estimated shape of the wheel 50 in the future (rolling period L). Therefore, a derailment cost evaluation value occurring due to derailment may be calculated based on the flange height Hf and the flange thickness Wf. In this case, a total life cycle cost of the railway vehicle 20 per one life cycle of the wheel 50 may be calculated based on the derailment cost evaluation value and the wheel life cycle cost evaluation value, taking into account the possibility of derailment.

[0129] The life cycle of the wheel 50 is the cost incurred by processing and replacing the wheel 50 when rolling is performed at the rolling cycle L. The life cycle cost evaluation function or evaluation value of the wheel is a function or value that evaluates the life cycle cost of the wheel 50 itself. The total life cycle cost of the railway vehicle 20 is a cost that includes the life cycle cost of the wheel 50 and further includes the operating cost of the railway vehicle 20 that is incurred due to the operation of the railway vehicle 20 when rolling is performed at the rolling cycle L. For example, the rolling cycle L is considered to affect the occurrence rate of failures and the derailment rate, and if the occurrence rate of failures and the derailment rate fluctuate, the operating cost of the railway vehicle 20 also fluctuates. The total life cycle cost of the railway vehicle 20 is a cost that takes into account the life cycle cost of the wheel 50 and the operating cost of the railway vehicle 20. The total life cycle cost evaluation function or evaluation value of the railway is a function or value that takes into account the life cycle cost evaluation function or value of the wheel 50 itself and the evaluation function or evaluation value due to the operating cost.

[0130] In this embodiment, the cost required for restoration or the like when a derailment occurs is defined as Cdr, and an evaluation function G2 shown in Equation 5 is defined. Note that the cost Cdr may be set based on past examples or the like. The calculation result of the evaluation function G2 becomes the total life cycle cost evaluation value of the railway vehicle 20.

[0131] The evaluation function G2 may be defined as shown in Equation 6.

[0132]

number

[0133]

number

[0134] In Equation 5 or 6, the probability Pwf of derailment caused by the flange thickness Wf increases as the margin Wfm(L) for the flange thickness Wf decreases. The relationship between the probability Pwf of derailment and the margin Wfm(L) can be obtained, for example, from past knowledge or simulation results. For example, as shown in FIG. 12, it is considered that there is a relationship in which the probability Pwf of derailment gradually increases as the margin Wfm(L) decreases, and the probability Pwf of derailment suddenly increases as the margin Wfm(L) approaches 0. It is considered that a reference table based on the above relationship is prepared in advance, and the probability Pwf of derailment can be obtained from the margin Wfm(L).

[0135] Furthermore, the tolerance Wfm(L) is the difference between the flange thickness Wf of the worn wheel 50 in the rolling cycle L and the limit value. Therefore, the tolerance Wfm(L) can be calculated by calculating the flange thickness Wf of the worn wheel 50 at the time of the rolling cycle L. Therefore, the derailment occurrence probability Pwf can be considered to be the derailment occurrence probability Pwf(Wf(L)) determined by the flange thickness Wf(L) specified by the rolling cycle L.

[0136] Similarly, as shown in Fig. 12, the probability PHf of derailment caused by the flange height Hf increases as the margin Hfm(L) related to the flange height Hf decreases. The relationship between the probability PHf of derailment and the margin Hfm(L) can be obtained, for example, from past knowledge or simulation results. For example, as shown in Fig. 12, it is considered that there is a relationship in which the probability PHf of derailment gradually increases as the margin Hfm(L) decreases, and the probability PHf of derailment suddenly increases as the margin Hfm(L) approaches 0. It is considered that a reference table based on the above relationship is prepared in advance, and the probability PHf of derailment can be obtained from the margin Hfm(L).

[0137] Furthermore, the tolerance Hfm(L) is the difference between the flange height Hf of the worn wheel 50 in the rolling cycle L and the limit value. The flange height Hf is determined by the wheel diameter R of the worn wheel 50 in the rolling cycle L. Therefore, the tolerance Hfm(L) can be calculated by calculating the wheel diameter R of the worn wheel 50 at the time of the rolling cycle L. Therefore, the derailment probability PHf can be considered to be the derailment probability PHf(Hf(L)) determined by the flange height Hf(L) specified by the rolling cycle L.

[0138] For this reason, the evaluation function G2 can also be expressed as a function of the rolling period L. By deriving L so as to minimize the evaluation function G2, it is possible to derive, as a candidate rolling period, the rolling period L that can minimize the total life cycle cost of the railway vehicle 20 per one life cycle of the wheel 50, taking into account the cost due to derailment.

[0139] Furthermore, if the wheel 50 has a flat portion or is eccentric due to wear, the vibration of the bogie 24 increases, and there is a possibility that a failure will occur in the mounted components of the bogie 24 or the bogie frame 25. The vibration of the bogie 24 can be grasped by the bogie vertical acceleration data included in the running state data 138. Therefore, a failure cost evaluation value caused by vibration may be calculated based on the vibration data of the railcar 20 (here, the bogie 24) while traveling. Then, based on the wheel life cycle cost evaluation value and the failure cost evaluation value, the total life cycle cost of the railcar 20 per one life cycle of the wheel 50 may be calculated taking into account the possibility of failure due to vibration. The total life cycle cost of the railcar 20 is the operating cost of the railcar 20 incurred due to the operation of the wheel 50 when the wheel 50 is rolled at the rolling period L, and is the cost per one life cycle of the wheel 50.

[0140] In this embodiment, a failure probability for an increase in vibration is defined for each bogie mounted part including the bogie frame 25. Also, an evaluation function G3 is defined by adding to the evaluation function G1 a value obtained by dividing the sum of the product of the price of each part, the cost required for replacement (including the cost of the part and the cost of labor) and the sum of the damage caused by the effect on the operation of the railway vehicle 20 in the event of a failure, and the failure probability for an increase in vibration, by the wheel life. Evaluation function G3 is shown in Equation 7.

[0141]

number

[0142] The failure probability Pcc,i is a value determined according to the increase in the degree of bogie vibration from the reference time. For example, the increase in the degree of bogie vibration may be any of the increase in the vibration effective value, the accumulated amplitude value, and the p-p value in a specific frequency range from the time of new manufacture or after wheel rolling.

[0143] As shown in FIG. 13, it is considered that the failure probability increases when the increase in the degree of bogie vibration from when the bogie was newly manufactured or after the wheels were turned.

[0144] The increase in the degree of bogie vibration may be understood as an excess over a predetermined standard value. As in the above, as shown in FIG. 14, it is considered that the failure probability gradually increases as the degree of the bogie vibration exceeding the standard value increases.

[0145] 13 or 14 is preset as a reference table, the increment of the bogie vibration degree at the reference time is calculated based on the vertical acceleration data, and the failure probability Pcc,i is calculated by referring to the reference table from the increment. The failure probability Pcc,i may be a probability common to each mounted component and the bogie frame 25, or may be a separate probability.

[0146] In this way, the evaluation function G3 can also be expressed as a function of the rolling period L. By deriving L so as to minimize the evaluation function G3, it is possible to derive, as a candidate rolling period, the rolling period L that can minimize the total life cycle cost of the railway vehicle 20 per one life cycle of the wheel 50, taking into account the cost resulting from failures due to vibration.

[0147] An evaluation function G4 that takes into account both the cost due to derailment and the cost due to increased bogie vibration is defined by Equation 8.

[0148]

number

[0149] By deriving the rolling period L so as to minimize the evaluation function G4, it is possible to calculate a candidate rolling period L that minimizes the sum of the costs required for rolling and replacing the wheels 50, the costs due to derailment, and the costs due to increased bogie vibration.

[0150] 15 is a flow chart showing an example of processing in the terminal device 190. This processing is performed by the processor 192 executing instructions written in the terminal side program 195.

[0151] As shown in the figure, a condition is input in step S31. The condition is a condition for calculating any one of the evaluation functions G1 to G4.

[0152] FIG. 16 shows an example of a display screen of the display device 197 in the terminal device 190. The display screen displays blank spaces for inputting the cost of dismantling the bogie, the cost of the wheels, the cost of turning the wheels, and the like. The evaluation function G1 is calculated based on the values ​​input in these spaces. The display screen displays blank spaces for inputting the cost of derailment. The evaluation function G2 can also be calculated based on the values ​​input in these spaces. Furthermore, blank spaces for inputting the replacement costs of the bogie-mounted parts, such as anti-vibration rubber, braking devices, and differential pressure valves, are displayed. In addition, a "Set" button is provided for setting the failure probability for the bogie-mounted parts. By selecting the "Set" button, a screen for setting the failure probability is displayed. The failure probability is set, for example, by specifying multiple points through which a line indicating the failure rate passes in a graph with the bogie vibration increase degree on the horizontal axis and the failure rate on the vertical axis. The display screen is provided with a space for setting the existing turning period.

[0153] By using the above display screen, conditions are input in step S31.

[0154] In the next step S32, it is determined whether or not an optimization execution command has been issued. For example, when the "Optimization execution" button on the display screen is clicked, it is determined that an optimization execution command has been issued, and the process proceeds to step S33. If it is determined that an optimization execution command has not been issued, the process returns to step S31, and condition input is accepted.

[0155] In step S33, the input conditions are transmitted to the milling plan support device 130. As a result, the milling plan support device 130 executes each of the above processes based on the conditions input in the terminal device 190. For example, based on the evaluation function G4, a candidate milling period that minimizes the evaluation function G4 is calculated. Based on the evaluation function G4, an evaluation value for the candidate milling period is calculated. In addition, when a field for setting an existing milling period is provided, an evaluation value for the existing milling period is calculated based on the evaluation function G4. After the processing, the calculation result is transmitted from the milling plan support device 130 to the terminal device 190. The milling period L that minimizes the evaluation function G4 may be obtained by brute force based on a plurality of milling periods, for example.

[0156] In step S34, it is determined whether or not a calculation result has been received from the turning plan support device 130. If it is determined that a calculation result has been received, the process proceeds to the next step S35.

[0157] In step S35, the result is displayed. For example, as shown in FIG. 16, the candidate milling cycle may be displayed as an optimized milling cycle. The displayed optimized milling cycle is a proposed milling cycle proposed by this support device. In this case, an improvement value based on the difference between the evaluation value when milling is performed with the optimized milling cycle and the evaluation value when milling is performed with the existing milling cycle, and an improvement effect that can be expected by optimization may be displayed. In FIG. 14, the cost improvement effect of a one-day value is displayed. After this, the process is terminated. The above process may be repeated by modifying or re-inputting the conditions.

[0158] According to the wheel turning plan support device 130, the wheel turning plan support system 100, and the wheel turning plan support method configured in this manner, an estimated shape of the wheel 50 at each future time is calculated based on the shape data of the wheel 50 at the reference time and the scheduled running data 36, ​​and a predicted wheel diameter after wheel turning at each future time is calculated based on this estimated shape. A wheel life cycle cost evaluation value is calculated based on this predicted wheel diameter. The wheel 50 turning plan of the railway vehicle 20 can be optimized based on this wheel life cycle cost evaluation value. For example, by calculating the turning period L that minimizes the wheel life cycle cost evaluation value, it is possible to propose a turning period L that can minimize the wheel life cycle cost.

[0159] The shape data of the wheel 50 includes the flange height, the flange thickness, the tread gradient, and the wheel diameter, so that an estimated shape of the wheel 50 can be calculated appropriately.

[0160] The planned running data 36 includes the route information of the track 16 and the running speed information, so that the behavior of the wheels 50 relative to the track 16 during the future running of the railcar 20 can be estimated. This allows the estimated shape of the wheels 50 due to wear to be properly calculated.

[0161] In addition, by calculating wheel life cycle cost evaluation values ​​for a plurality of future times, optimization of the wheel life cycle cost can be considered using a plurality of rolling periods L corresponding to a plurality of future times as candidates.

[0162] Based on the wheel life cycle cost evaluation value and the derailment cost evaluation value, the total life cycle cost of the railway vehicle per life cycle of the wheel 50 that is incurred due to the operation of the wheel when the wheel 50 is rolled at a rolling period L corresponding to a future time is calculated, thereby making it possible to evaluate the derailment cost and optimize the life cycle cost.

[0163] At this time, by calculating the derailment cost evaluation value caused by the derailment based on the flange height and flange thickness as the estimated shape of the wheel 50, the derailment cost evaluation value can be appropriately calculated.

[0164] Based on the wheel life cycle cost evaluation value and the failure cost evaluation value, the total life cycle cost of the railway vehicle per life cycle of the wheel 50 that is incurred due to the operation of the wheel 50 when the wheel is rolled at a rolling cycle according to a future time is calculated, so that the failure cost due to vibration can be evaluated to optimize the life cycle cost.

[0165] If the total life cycle cost is defined by evaluation functions G2, G3, and G4 with the milling cycle L as a variable, the candidate milling cycle that minimizes the total life cycle cost can be easily calculated.

[0166] Furthermore, by using the actual running state data 138 of the railcar 20 as a basis, the shape data of the wheels 50 at the reference time can be easily estimated, and the work of actually measuring the shape of the wheels 50 can be reduced.

[0167] Furthermore, based on the shape data of the wheels 50 at the reference time and the planned running data of the railway vehicle 20 after the reference time, it is possible to estimate the behavior of the wheels relative to the track during future running of the railway vehicle. Based on this estimated behavior, the amount of wear can be calculated based on the amount of slippage between the track 16 and the wheels 50, the force in the slippage direction, etc., and an estimated shape of the wheels 50 can be calculated.

[0168] The display device 42, 197 displays the proposed rolling cycle based on the wheel life cycle cost evaluation value, so that the proposed rolling cycle can be easily confirmed.

[0169] The terminal device 190 is connected to the turning planning support device 130 via the communication network 13, and when the input of the wheel processing and replacement costs is accepted via the input unit 196 of the terminal device 190, the input contents are transmitted to the turning planning support device 130. When the processing results of the turning planning support device 130 are transmitted to the terminal device 190, an image based on the wheel life cycle cost evaluation value, which is the calculation result of the turning planning support device 130, is displayed on the display device 197. Therefore, even at a remote location far from the turning planning support device 130, it is possible to input the processing and replacement costs of the wheel 50 and visually confirm the image based on the wheel life cycle cost evaluation value.

[0170] {Additional Note} The functions of the elements disclosed herein can be performed using circuits or processing circuits, including general purpose processors, special purpose processors, integrated circuits, Application Specific Integrated Circuits (ASICs), conventional circuits, and / or combinations thereof, configured or programmed to perform the disclosed functions. Processors are considered processing circuits or circuits because they include transistors and other circuits. In this disclosure, a circuit, unit, or means is hardware that performs the recited functions or hardware that is programmed to perform the recited functions. The hardware may be hardware disclosed herein or other known hardware that is programmed or configured to perform the recited functions. In the case where the hardware is a processor, which is considered a type of circuit, 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.

[0171] The configurations described in the above embodiments and modifications can be combined as appropriate as long as they are not mutually inconsistent.

[0172] The above description is illustrative in all respects, and the present invention is not limited thereto. It is understood that countless variations not illustrated can be assumed without departing from the scope of the present invention.

[0173] The present specification and drawings disclose the following aspects.

[0174] A first aspect is a wheel turning planning support device for a railway vehicle running on a track, comprising a processing unit that calculates an estimated shape of the wheel for at least one future time period after a reference time based on wheel shape data of the railway vehicle at a reference time and planned running data of the railway vehicle after the reference time, calculates a predicted wheel diameter after wheel turning at the at least one future time period based on the estimated shape, and calculates a wheel life cycle cost evaluation value corresponding to the processing and replacement costs per life cycle of the wheel that will be incurred when the wheel is turned at at least one rolling period corresponding to the at least one future time period based on the predicted wheel diameter after wheel turning at the at least one future time period.

[0175] According to a first aspect, an estimated wheel shape at at least one future time period after a reference time is calculated based on wheel shape data and scheduled running data, a predicted wheel diameter after wheel rolling at the at least one future time period is calculated based on the estimated wheel shape, and a wheel life cycle cost evaluation value is calculated based on the predicted wheel diameter after wheel rolling. The wheel rolling plan for a railway vehicle can be optimized based on the wheel life cycle cost evaluation value.

[0176] A second aspect is a wheel turning planning support device according to the first aspect, in which the wheel shape data includes a flange height, a flange thickness, a tread gradient, and a wheel diameter, and thus an estimated wheel shape can be appropriately calculated based on the shape data including the flange height, the flange thickness, the tread gradient, and the wheel diameter.

[0177] A third aspect is the wheel turning planning support device according to the first or second aspect, in which the planned running data includes track path information and running speed information, thereby making it possible to appropriately calculate an estimated shape of the wheel caused by wear.

[0178] A fourth aspect is the wheel turning planning support device according to any one of the first to third aspects, wherein the processing unit executes calculations for a plurality of future times as the at least one future time, thereby making it possible to consider optimization of the life cycle cost of the wheels of a railway vehicle by using a plurality of future times as candidates.

[0179] A fifth aspect is a wheel turning planning support device according to any one of the first to fourth aspects, wherein the processing unit calculates a derailment cost evaluation value generated by derailment based on an estimated shape of the wheel at at least one future time period after the reference time, and calculates a total life cycle cost of the railway vehicle per life cycle of the wheel that is generated due to operation of the wheel when the wheel is turned at at least one rolling cycle corresponding to the at least one future time period based on the wheel life cycle cost evaluation value and the derailment cost evaluation value. This makes it possible to evaluate the derailment cost and optimize the life cycle cost.

[0180] A sixth aspect is the wheel turning planning support device according to the fifth aspect, wherein the processing unit calculates a derailment cost evaluation value caused by derailment based on the flange height and flange thickness as the estimated shape of the wheel. In this case, the derailment cost evaluation value can be appropriately calculated based on the flange height and flange thickness.

[0181] A seventh aspect is a wheel turning planning support device according to any one of the first to sixth aspects, wherein the processing unit calculates a failure cost evaluation value caused by vibration based on vibration data of the railway vehicle while the railway vehicle is running, and calculates a total life cycle cost of the railway vehicle per life cycle of the wheel that is caused by operation of the wheel when the wheel is turned in at least one rolling cycle corresponding to the at least one future time based on the wheel life cycle cost evaluation value and the failure cost evaluation value. In this case, the failure cost due to vibration can be evaluated to optimize the life cycle cost.

[0182] An eighth aspect is a wheel turning planning support device according to any one of the fifth to seventh aspects, in which the total life cycle cost of the railway vehicle is defined by an evaluation function having the rolling period as a variable, and the processing unit calculates, from among the multiple rolling periods, a period that minimizes the evaluation function as a candidate rolling period. This allows the candidate rolling period that minimizes the total life cycle cost to be calculated.

[0183] A ninth aspect is the wheel turning planning support device according to any one of the first to eighth aspects, wherein the processing unit estimates the wheel shape data at the reference time based on the railcar running state data, thereby reducing the work of actually measuring the wheel shape.

[0184] A tenth aspect is a wheel turning planning support device according to any one of the first to ninth aspects, wherein the processing unit estimates behavior of the wheels relative to a track during future running of the railway vehicle based on shape data of the wheels at the reference time and planned running data of the railway vehicle after the reference time, and calculates, at each position of the wheel based on the estimated behavior, a time integral value of a product of an amount of slippage between the track and the wheel in two axial directions on a plane and a force in the slip direction generated on the wheel, and a time integral value of a product of an amount of slippage between the track and the wheel in a rotational direction of an axis perpendicular to a plane and a torque generated on the wheel, thereby estimating shape data of the wheels at the future time. This makes it possible to appropriately calculate an estimated shape of the wheels based on a product of the amount of slippage and frictional force based on the behavior of the wheels during future running of the railway vehicle.

[0185] An eleventh aspect is a wheel turning planning support device according to any one of the first to tenth aspects, which includes a display device that displays a proposed turning period based on the wheel life cycle cost evaluation value based on a calculation result by the processing unit. This allows the proposed turning period to be visually recognized.

[0186] A wheel turning planning support system according to a twelfth aspect includes the wheel turning planning support device according to any one of the first to eleventh aspects, and a terminal device that is communicatively connected to the wheel turning planning support device via a communication network and includes an input receiving unit that receives input of the wheel processing and replacement costs, and a display device that displays an image based on the wheel life cycle cost evaluation value that is the calculation result of the processing unit in the wheel turning planning support device. This makes it possible to input the wheel processing and replacement costs and visually view the image based on the wheel life cycle cost evaluation value even at a remote location away from the turning planning support device.

[0187] A thirteenth aspect is a method for supporting planning for turning wheels of a railway vehicle running on a track, comprising: (a) calculating an estimated shape of the wheel for at least one future time period after a reference time based on wheel shape data of the railway vehicle at a reference time and planned running data of the railway vehicle after the reference time; (b) calculating a predicted wheel diameter after wheel turning at the at least one future time period based on the estimated shape; and (c) calculating a wheel life cycle cost evaluation value corresponding to the processing and replacement costs per life cycle of the wheel that will be incurred when the wheel is turned at at least one turning period corresponding to the at least one future time period based on the predicted wheel diameter after wheel turning at the at least one future time period.

[0188] According to a thirteenth aspect, an estimated wheel shape at at least one future time period after a reference time is calculated based on the wheel shape data and the scheduled running data, a predicted wheel diameter after wheel rolling at the at least one future time period is calculated based on the estimated wheel shape, and a wheel life cycle cost is calculated based on the predicted wheel diameter after wheel rolling. The life cycle cost of the railway vehicle can be optimized based on the wheel life cycle cost evaluation value. [Explanation of symbols]

[0189] 13. Communications Network 16 orbit 20. Railroad cars 24 Cart 25 Bogie Frame 28 Driving condition sensor 29 Communication Equipment 30, 130 Turning planning support device 32, 132 processors 36 Planned driving data 36a Route Information 36b Speed ​​information 36c Schedule Information 37 Cost Data 40 Input section 42 Display device 50 wheels 52 Tread 53 Flange 68 Communication Equipment 100 Turning planning support system 138 Driving condition data 190 Terminal Equipment 196 Input section 197 Display device 198 Communication Equipment

Claims

1. A wheel turning planning support device for a railway vehicle running on a track, comprising: a processing unit that calculates an estimated shape of the wheel for at least one future time period after a reference time based on shape data of the wheels of the railway vehicle at a reference time and planned running data of the railway vehicle after the reference time, calculates a predicted wheel diameter after wheel rolling at the at least one future time period based on the estimated shape, and calculates a wheel life cycle cost evaluation value corresponding to the processing and replacement costs per life cycle of the wheel that will be incurred when the wheel is rolled at at least one rolling period corresponding to the at least one future time period based on the predicted wheel diameter after wheel rolling at the at least one future time period.

2. The wheel turning planning support device according to claim 1, The wheel shape data includes flange height, flange thickness, tread gradient, and wheel diameter.

3. The wheel turning planning support device according to claim 1 or 2, The wheel turning planning support device, wherein the planned traveling data includes track route information and traveling speed information.

4. A wheel turning planning support device according to any one of claims 1 to 3, The processing unit executes calculations for a plurality of future times as the at least one future time.

5. A wheel turning planning support device according to any one of claims 1 to 4, the processing unit calculates a derailment cost evaluation value generated by the derailment based on an estimated shape of the wheel at at least one future time period after the reference time, and calculates a total life cycle cost of the railway vehicle per life cycle of the wheel that is generated due to the operation of the wheel when the wheel is turned at least one rolling period corresponding to the at least one future time period, based on the wheel life cycle cost evaluation value and the derailment cost evaluation value.

6. The wheel turning planning support device according to claim 5, The processing unit is a wheel turning planning support device that calculates a derailment cost evaluation value generated by the derailment based on a flange height and a flange thickness as the estimated shape of the wheel.

7. A wheel turning planning support device according to any one of claims 1 to 6, The processing unit calculates a failure cost evaluation value caused by vibration based on vibration data of the railway vehicle while the railway vehicle is running, and calculates a total life cycle cost of the railway vehicle per life cycle of the wheel that will be incurred due to the operation of the wheel when the wheel is turned at least one turning period corresponding to the at least one future time period, based on the wheel life cycle cost evaluation value and the failure cost evaluation value.

8. The wheel turning planning support device according to any one of claims 5 to 7, a total life cycle cost of the railcar is defined by an evaluation function having the rolling period as a variable; The processing unit calculates, from among the plurality of rolling periods, a period that minimizes the evaluation function as a candidate rolling period.

9. A wheel turning planning support device according to any one of claims 1 to 8, The processing unit estimates shape data of the wheels at the reference time based on running state data of the railway vehicle.

10. A wheel turning planning support device according to any one of claims 1 to 9, The processing unit estimates the behavior of the wheels relative to the track during future running of the railway vehicle based on shape data of the wheels at the reference time and planned running data of the railway vehicle after the reference time, and calculates, at each position of the wheel based on the estimated behavior, a time integral of the product of the amount of slippage between the track and the wheel in two axial directions on a plane and the force in the slipping direction generated in the wheel, and a time integral of the product of the amount of slippage between the track and the wheel in the rotational direction of an axis perpendicular to a plane and the torque generated in the wheel, thereby estimating shape data of the wheel at the future time.

11. The wheel turning planning support device according to any one of claims 1 to 10, A wheel turning planning support device comprising a display device that displays a proposed turning period based on the wheel life cycle cost evaluation value based on the calculation result by the processing unit.

12. A wheel turning planning support device according to any one of claims 1 to 11, a terminal device including an input receiving unit that is communicatively connected to the wheel turning support device via a communication network and that receives input of the wheel processing and replacement costs, and a display device that displays an image based on the wheel life cycle cost evaluation value that is the calculation result of the processing unit in the wheel turning planning support device; A wheel turning planning support system equipped with the above-mentioned.

13. A method for supporting planning for turning wheels of a railway vehicle running on a track, comprising: (a) calculating an estimated shape of the wheels at at least one future time period after the reference time based on shape data of the wheels of the railway vehicle at a reference time and scheduled running data of the railway vehicle after the reference time; (b) calculating a predicted wheel diameter after wheel rolling at the at least one future time based on the estimated shape; (c) A wheel turning planning support method, which calculates a wheel life cycle cost evaluation value corresponding to the processing and replacement costs per life cycle of the wheel that will be incurred when the wheel is turned in at least one turning cycle corresponding to the at least one future time period, based on a predicted wheel diameter after wheel turning at the at least one future time period.

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