Estimation method, estimation device, and estimation program

Abstract

The estimation device estimates, on the basis of sensor information measured for bogies of some of the railway vehicles having sensors mounted thereon, the states of bogies of other railway vehicles that have the same conditions as those of the bogies of the some of the railway vehicles and do not have sensors mounted thereon.

Description

Technical Field

[0001] The technology disclosed herein relates to an estimation method, an estimation device, and an estimation program. Background Art

[0002] A method of configuring a sensor on the track side is known. For example, in Japanese Patent Publication No. 2016-218053, a technique for detecting an abnormality by measuring the temperature of the axle box from the ground is disclosed. In addition, in Japanese Patent Publication No. 2017-181061, a technique for measuring the longitudinal creep force acting on the contact portion between the wheel and the track by a strain gauge laid on the track is disclosed. Summary of the invention

[0003] In order to understand the state of a railway vehicle bogie, various sensors such as strain gauges and acceleration sensors are usually installed on the railway vehicle bogie. However, there is a problem that the number of railway vehicle bogies in use is large, and the number of sensors installed will increase when understanding the state of all bogies.

[0004] Furthermore, in the method described in the above-mentioned prior art, there is a problem in that only the instantaneous state when passing through the measurement point is known, and the state of the bogie other than when passing through the measurement point is not known.

[0005] The disclosed technology has been made in view of the above-mentioned situation, and an object is to provide an estimation method, an estimation device, and an estimation program capable of estimating the states of bogies of a plurality of railway vehicles over an operation period while suppressing the number of sensors.

[0006] Technical means for solving technical problems

[0007] A first scheme disclosed in the present invention is an estimation method for estimating the state of a railway vehicle, wherein an estimation device estimates the state of a bogie of another railway vehicle that has the same conditions as the bogie of the one part of the railway vehicle and is not equipped with sensors based on sensor information measured for the bogie of the one part of the railway vehicle equipped with sensors.

[0008] A second scheme disclosed in the present invention is an estimation device for estimating the state of a railway vehicle, which estimates the state of the bogies of other railway vehicles that have the same conditions as the bogies of the part of railway vehicles and are not equipped with sensors based on sensor information measured for the bogies of the part of railway vehicles equipped with sensors.

[0009] The third scheme of the present disclosure is an estimation program for inferring the state of a railway vehicle, which is a program for causing a computer to execute the following content: based on sensor information measured on the bogies of a part of railway vehicles equipped with sensors, the state of the bogies of other railway vehicles that have the same conditions as the bogies of the said part of railway vehicles and are not equipped with sensors is estimated.

[0010] Effects of the Invention

[0011] According to the disclosed technology, the number of sensors can be reduced and the states of bogies of a plurality of railway vehicles can be estimated over the operation period. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram showing a schematic configuration of an estimation device according to the present embodiment.

[0013] Figure 2 It is a schematic diagram showing a general structure of a railway vehicle.

[0014] Figure 3 This is a diagram showing an example of the structure of a bogie frame and its surrounding parts.

[0015] Figure 4 : is a block diagram showing the configuration of an estimating unit of the estimating device according to the present embodiment.

[0016] Figure 5 This is a schematic block diagram of an example of a computer that functions as an estimation device.

[0017] Figure 6 This is a diagram showing an example of data measured by a measuring device of a railway vehicle equipped with a sensor on a plurality of routes.

[0018] Figure 7 The diagram shows a state where a bogie of a railway vehicle not equipped with a sensor is traveling on a plurality of routes.

[0019] Figure 8 This is an example flow of the estimation process in this embodiment.

[0020] Fig. 9 This is a diagram showing an example of acquiring data measured on the same route for bogies of each railway vehicle.

[0021] Fig.10 : is a flowchart showing the flow of the process of estimating the state in the estimation process of this embodiment.

[0022] Fig.11 This is a flowchart of an example of the estimation process according to the modification. DETAILED DESCRIPTION

[0023] Below, an example of an implementation method of the disclosed technology is described with reference to the accompanying drawings. It should be noted that the same reference numerals are used for the same or equivalent components and parts in each of the drawings. In addition, the dimensional ratios of the drawings are appropriately exaggerated for the purpose of description and are sometimes different from the actual ratios.

[0024] <Overview of Embodiments of Disclosed Technology>

[0025] Measuring the vibration acceleration of the axle box through one bogie is the same as measuring the vibration acceleration of the axle box of other bogies running on the same route. Therefore, in this embodiment, the vibration acceleration of the axle box measured through one bogie is used to infer the state of each bogie.

[0026] The state of the bogie (stress, acceleration, etc.) is greatly affected by the vibration of the wheels and axles caused by the unevenness of the track (track displacement) when running on the track. The vibration of the wheels and axles is the same as the vibration of the axle box that holds the axle, and the vibration of the axle box is transmitted to the bogie via the axle box support device such as the axle spring. On the other hand, the vibration of the wheels and axles does not depend on the form of the connected bogie, and they vibrate in the same way if they run on the same route under the same operating conditions. Here, it is assumed that when there are multiple tracks due to bifurcations, etc., and the tracks are different, the routes are different.

[0027] Therefore, it is considered that bogies running on the same route under the same operating conditions are not subjected to the same axle box vibrations due to the bogie type. Therefore, by measuring the vibration acceleration of the axle box of a part of the bogie equipped with a sensor for one route, it is equivalent to measuring the vibration acceleration of the axle box of a bogie without a sensor running on the same route and under the same operating conditions.

[0028] Then, by using a model prepared for each bogie type that infers the state of the bogie based on the vibration acceleration of the axle box, it is possible to grasp the state of bogies traveling on the same route, including bogies without sensors, using a small number of sensors.

[0029] The operation conditions include operation conditions related to the travel speed indicating the travel speed pattern, such as stopping at each station, rapid operation, and express operation.

[0030] In addition, when a bogie without sensors is operated across multiple routes and operating conditions, the state of the bogie can be inferred by combining data from bogies equipped with sensors that operate under each combination of routes and operating conditions, thereby understanding the state of all bogies.

[0031] In addition, in the model for estimating the state of the bogie from the vibration acceleration of the axle box, the stress generated in the bogie frame can be estimated as the state of the bogie, and the vibration acceleration of the motor seat can also be estimated. As a method for estimating the stress generated in the bogie frame, the method described in Japanese Patent Publication No. 2022-028374 can be used as an example.

[0032] The method of collecting the data of the sensors of each bogie is not particularly limited, and for example, it is considered to collect the data of the sensors of each bogie by wireless communication. In addition, the method of collecting the route information and operating conditions of each bogie is not particularly limited, and for example, it is considered to collect the route information of the bogie by managing the passing points based on RFID (Radio Frequency Identification) or to collect the route information and operating conditions of the bogie based on the operation management information of the railway operator.

[0033] In addition, the interval for collecting data from sensors of each bogie can be determined in accordance with the temporal changes in the track. That is, when using sensor data measured by the bogie of a railway vehicle equipped with sensors, changes on the track side that affect the vibration acceleration of the axle box will not occur suddenly in the used data. Therefore, for example, it can be set to collect data from sensors of each bogie for one week in a month. In this way, data from sensors whose running times are close in time can be used.

[0034] <Composition of the estimation device>

[0035] Figure 1 Schematic diagram showing the general configuration of an estimation device according to one embodiment of the technology disclosed herein. Figure 1 As shown, the estimation device 100 of this embodiment is a device for estimating the state of a bogie of a railway vehicle. The estimation device 100 includes a measurement data storage unit 50, an acquisition unit 52, and an estimation unit 54. The estimation device 100 is not installed in the railway vehicle, but is installed in another place.

[0036] On the bogies of some of the railway vehicles among the plurality of railway vehicles to which the estimation device 100 of the present embodiment is applied, Figure 2 The assay device is shown. Figure 2 This is a diagram showing an example of the outline of a railway vehicle. Figure 3 is a diagram showing an example of the structure of a bogie frame and its surrounding parts. Figure 2 , Figure 3 In the diagram, it is assumed that the railway vehicle is moving in the positive direction of the x1 axis. That is, the x1 axis is an axis along the traveling direction of the railway vehicle. In addition, the x3 axis is a direction perpendicular to the track 20 (ground) (the height direction of the railway vehicle). The x2 axis is a horizontal direction perpendicular to the traveling direction of the railway vehicle (a direction perpendicular to both the traveling direction and the height direction of the railway vehicle). In addition, in each figure, the ● in the ○ indicates a direction from the back side of the paper to the front side.

[0037] like Figure 2 , Figure 3As shown, in this embodiment, the railway vehicle has a car body 11, bogies 12a, 12b, and axles 13a to 13d. In this embodiment, a railway vehicle having two bogies 12a, 12b and four sets of axles 13a to 13d on one car body 11 is described as an example. The axles 13a to 13d have axles 15a to 15d and wheels 14a to 14d provided at both ends thereof. In this embodiment, a case where the bogies 12a and 12b are bolsterless bogies is described as an example.

[0038] exist Figure 2 In the figure, for the convenience of marking, only the wheels 14a to 14d on one side of the axles 13a to 13d are shown. Wheels are also arranged on the other side of the axles 13a to 13d. Figure 2 In the example shown, there are eight wheels in total.

[0039] exist Figure 3 In the embodiment, axle boxes 17a and 17b are arranged on both sides of the wheel axles 13a and 13b along the x2 axis. The axle boxes 17a and 17b are connected to the bogie frame 16 via single links 18a and 18b. In addition, the axle boxes 17a and 17b are connected to the bogie frame 16 via axle springs 19a and 19b. It should be noted that the railway vehicle has Figure 2 , Figure 3 The components other than the components shown in the figure are not included in the figure for the convenience of marking and description. Figure 2 , Figure 3 In the figure, the component is omitted. Figure 3 In some cases, the railway vehicle has axle swing, and the axle boxes 17a and 17b are connected to the bogie frame 16 via the axle swing.

[0040] exist Figure 3 In the figure, a bogie frame 16 is arranged on a bogie 12a. Axle boxes 17a, 17b, single links 18a, 18b, and axle springs 19a, 19b are arranged one by one for each wheel. As mentioned above, four wheels are provided on one bogie 12a. Therefore, four axle boxes, four single links, and four axle springs are provided for one bogie 12a. Here, it is assumed that a portion that is integrated with the bogie frame 16 is also included in the bogie frame 16. For example, it is assumed that a snowplow welded to the main body of the bogie frame 16 is also included in the bogie frame 16. In addition, the boundary between the bogie frame 16 and the portion that is integrated with the bogie frame 16 is not fixed. In addition, the portion that is integrated with the bogie frame 16 performs the same movement as the bogie frame 16.

[0041] exist Figure 3In the figure, only the bogie frame 16, axle boxes 17a, 17b, single connecting rods 18a, 18b, and axle springs 19a, 19b in the bogie 12a are shown. The bogie frame, axle boxes, single connecting rods, and axle springs in the bogie 12b are also shown in the same manner as in the figure. Figure 3 It should be noted that the railway vehicle itself can be implemented by known technology, so its detailed description is omitted here.

[0042] In the following description, components connecting the bogie frame 16 and the axle boxes 17a and 17b are collectively referred to as assembly elements as necessary.

[0043] The combination element is connected to the bogie frame 16 and the axle boxes 17a and 17b. Therefore, the vibration of the axle boxes 17a and 17b is linked to the vibration of the combination element. These vibrations are transmitted to the bogie frame 16 via the combination element. The external force acting on the bogie frame 16 is represented by the sum of the viscous damping force and the rigid force in the vibration of the combination element. The viscous damping force is represented by the product of the viscous damping coefficient and the velocity. The rigid force is represented by the product of the rigidity and the displacement. Based on the above, the inventors of the present invention have found that the external force acting on the bogie frame 16 can be correctly derived by deriving the external force acting on the bogie frame 16 based on the acceleration (the velocity and displacement derived from the acceleration) measured in the connecting component. Here, the connecting component is a component that is integrated with the bogie frame 16 and is a component connected to the bogie frame 16 directly or via other components.

[0044] As such a connecting component, for example, a component constituting a combined element, and a component directly or via a component connected to at least one of the components of the combined element are cited. More specifically, in the present embodiment, the case where such a connecting component is axle boxes 17a and 17b is cited as an example for explanation. Therefore, in the present embodiment, acceleration sensors 21a and 21b are respectively installed on axle boxes 17a and 17b. In the present embodiment, the case where acceleration sensors 21a and 21b are three-dimensional acceleration sensors for detecting the vibration acceleration of the axle boxes is cited as an example for explanation. According to the acceleration data measured by the acceleration sensors 21a and 21b, the x1-axis direction component, the x2-axis direction component, and the x3-axis direction component of the acceleration are obtained. In addition, the acceleration sensor is also installed in an axle box other than the axle box 17a and 17b. In the present embodiment, for example, the acceleration sensors 21a and 21b are an example of a sensor. In addition, the acceleration measured by the acceleration sensors 21a and 21b (acceleration of the axle boxes 17a and 17b) is an example of a physical quantity related to the axle box.

[0045] If the external force acting on the bogie frame 16 is obtained in the above manner, the displacement distribution of the bogie frame 16 (the amount of change in the position of each part of the bogie frame 16) can be derived using the motion equation representing the vibration (motion) of the bogie frame 16. If the displacement distribution of the bogie frame 16 is obtained, the stress distribution of the bogie frame 16 is obtained. In addition, in this embodiment, in order to reduce the calculation burden, the motion equation representing the vibration of the bogie frame 16 is expressed using the modal analysis method. Hereinafter, an example of a method for deriving the stress distribution of the bogie frame 16 is described.

[0046] In this embodiment, the acceleration sensors 21a, 21b, 22a, and 22b for detecting acceleration related to the axle box are used, but the present invention is not limited thereto. Sensors for detecting physical quantities other than acceleration, such as angular velocity and displacement related to the axle box, may also be used.

[0047] The motion equation representing the vibration of the bogie frame 16 is expressed by the following equation (1).

[0048]

[0049] Here, [M](∈R 3l×3l ) is the mass matrix of the bogie frame 16. [C](∈R 3l×3l ) is the viscosity matrix (also called damping matrix) of the bogie frame 16. [K](∈R 3l×3l ) is the rigidity matrix of the bogie frame 16.

[0050] {u}(∈R 3l ) is the displacement vector of the bogie frame 16. {f}(∈R 3l ) is the external force vector of the bogie frame 16.

[0051] In this embodiment, the vibration of the bogie frame 16 has three components in the x1-axis direction, the x2-axis direction, and the x3-axis direction. Therefore, the displacement of each node constituting {u} and the external force of each node constituting {f} have three degrees of freedom. In addition, in formula (1), · represents d / dt (the first-order derivative of time), ·· represents d 2 / dt 2 (Second-order derivative with respect to time) (This also applies to the following equations).

[0052] l corresponds to the degree of freedom of the approximate solution of the displacement distribution in the numerical analysis. In the present embodiment, as a numerical analysis, the case of using the finite element method (FEM) is cited as an example for explanation. Therefore, l is, for example, the number of nodes of the mesh of the finite element method. At this time, for the components of the mass matrix [M] of the bogie frame 16, the viscosity matrix [C] of the bogie frame 16, and the rigidity matrix [K] of the bogie frame 16, the values ​​derived from the density corresponding to each component, the values ​​derived from the viscous damping coefficient corresponding to each component, and the values ​​derived from the rigidity corresponding to each component are provided respectively. The density, the viscous damping coefficient and the rigidity do not depend on the position and can be set to the same value or different values. The components of the mass matrix [M], the viscosity matrix [C], and the rigidity matrix [K] of the bogie frame 16 are derived using the mesh of the finite element method, the density, the viscous damping coefficient, and the rigidity of the entire bogie frame 16, for example, in a known solver for performing numerical analysis based on the finite element method.

[0053] The first term on the left side of equation (1) is an inertia term representing the gravity acting on the bogie frame 16. The second term on the left side of equation (1) is an attenuation term representing the viscosity force acting on the bogie frame 16. The third term on the left side of equation (1) is a rigidity term representing the rigidity force acting on the bogie frame 16.

[0054] The external force vector {f} on the right side of equation (1) is given by deriving the external force acting on the combined element.

[0055] Therefore, a method for deriving the external force acting on the combined element is described. The connection position between the bogie frame 16 and the combined element can be set to the area (the whole) where the bogie frame 16 and the combined element are in contact with each other, or it can be set to a representative point (for example, the position of the center of gravity) of the area where the bogie frame 16 and the combined element are in contact. For example, in order to simplify the description, it is assumed that the connection position between the bogie frame 16 and the combined element is a point. In the following description, the connection position between the bogie frame 16 and the combined element is referred to as a point of application as needed. For example, in a model in which a spring and a damper are connected in parallel, a single link 18a and an axle spring 19a are represented. The modeled single link 18a is connected to the bogie frame 16 through a point of application. The modeled axle spring 19a is connected to the bogie frame 16 through a point of application. In addition, similarly to the axle box 17b, the modeled single link 18b is connected to the bogie frame 16 through a point of application, and the modeled axle spring 19b is connected to the bogie frame 16 through a point of application.

[0056] The motion equation representing the vibration of the combined elements is expressed by the following equation (2).

[0057]

[0058] Here, [C bc ] is the viscosity matrix of the combined elements. [K bc ] is the rigidity matrix of the combined element. {u0} is the displacement vector of the combined element composed of the displacement at the connection position of the combined element with the axle box 17a and the displacement at the application point of the combined element. {f0} is the external force vector of the combined element composed of the external force at the connection position of the combined element with the axle box 17a and the external force at the application point of the combined element. For the viscosity matrix [C bc ], and the rigid matrix of the combined elements [K bc ], respectively, the values ​​derived from the viscous damping coefficient corresponding to each component and the values ​​derived from the rigidity corresponding to each component are given. The components of the viscosity matrix [C] and the rigidity matrix [K] of the combination element are derived using, for example, the position of the connection position of the combination element with the axle box 17a, the position of the application point of the combination element, the viscous damping coefficient of the combination element, and the rigidity of the combination element.

[0059] In formula (2), the displacements in the x1-axis direction, the x2-axis direction, and the x3-axis direction at the application point are set to 0 (zero). In addition, the displacement of the connection position of the combination element with the axle box 17a is set to the displacement of the axle box 17a. By setting in the above manner, the external force acting on the application point of the combination element can be derived as the external force vector {f0} constituting the combination element. That is, the external force acting on the application point of the single link 18a and the application point of the axle spring 19a is derived using formula (2) respectively constructed for the single link 18a and the axle spring 19a. In addition, similarly, with respect to the axle box 17b, the external force acting on the application point of the single link 18b and the application point of the axle spring 19b is derived according to the displacement of the axle box 17b. In addition, the external forces acting on each application point of the bogie frame 16 are derived as reaction forces of the external forces acting on the application points of the single links 18a, 18b and the application points of the axle springs 19a, 19a.

[0060] By giving the value derived by the above method to the components of the external force vector {f} acting on each point of application (of the bogie frame 16) of the external force vector (1), and giving 0 (zero) to the other components, the displacement vector {u} of the bogie frame 16 is derived, thereby being able to derive the displacement distribution of the bogie frame 16. In this embodiment, in order to shorten the calculation time, the motion equation of the physical coordinate system (coordinates indicating the position in the real space) represented by the formula (1) is expressed as the motion equation of the model coordinate system, and the motion equation of the model coordinate system is used as an example for explanation. An example of this method is described below.

[0061] By applying the eigenvalue analysis to the equation of motion of formula (1), the eigenfrequency ω and eigenvector Derived from the eigenvector The pattern matrix In addition, the eigenvector is also called the eigenmode vector. Here, n (∈N) is the number of modes. The mode number n eigenfrequency is selected in order from the lowest eigenfrequency. The method of eigenvalue analysis can be implemented, for example, by adopting the modal analysis method described in non-patent document 1. Since it is a well-known technology, the detailed description is omitted here. In addition, when performing eigenvalue analysis, as described in non-patent document 1, the right side of equation (1) is set to 0 (zero). In addition, the attenuation term [C]{u·} can also be set to 0 (zero) (u· corresponds to the term marked · above u in equation (1)). In addition, the eigenvector The coordinate system of the shift vectors based on is called the pattern coordinate system.

[0062] The mass matrix of the bogie frame 16 in the model coordinate system [M ξ ](∈R n×n ), the viscosity matrix of the bogie frame 16 in the model coordinate system [C ξ ](∈R n×n ), the rigid matrix of the bogie frame 16 in the model coordinate system [K ξ ](∈R n×n ) are respectively expressed by the following formulas (3), (4) and (5).

[0063] [φ] T [M][φ]=[M ξ ]…(3)

[0064] [φ] T [C][φ]=[C ξ ]…(4)

[0065] [φ] T [K][φ]=[K ξ ]…(5)

[0066] Here, T is expressed as a transposed matrix (this also applies to the following equations). In addition, the mass matrix [M] of the bogie frame 16 in the model coordinate system is ξ ], viscosity matrix [C ξ ], rigid matrix [K ξ ] are respectively diagonal matrices as shown in the following equations (6), (7), and (8). In addition, the mass matrix [M] of the bogie frame 16 in the model coordinate system is ξ ] and the viscosity matrix [C ξ ], when a component other than a diagonal component is not 0 (zero), it is assumed that the component is approximately 0 (zero).

[0067]

[0068] Here, (1), ..., and (n) respectively represent components corresponding to the first-order natural vibration mode, ..., and the n-order natural vibration mode (this also applies to the following equations).

[0069] The mass matrix of the bogie frame 16 in the model coordinate system [M ξ ], viscosity matrix [C ξ ], rigid matrix [K ξ ] is a diagonal matrix. Therefore, each natural vibration mode can be processed independently of each other. Therefore, the calculation time can be shortened.

[0070] The displacement vector {u} of the bogie frame 16 in the physical coordinate system is converted into the displacement vector {ξ} (∈R n The external force vector {f} of the bogie frame 16 in the physical coordinate system is converted into the external force vector {f} in the model coordinate system as shown in the following formula (10): ξ}(∈R n ).

[0071] {u}=[φ]{ξ}…(9)

[0072] {f}=[φ]{f ξ}…(10)

[0073] Multiply both sides of formula (1) from the left by Substitute into equations (9) and (10). Thus, we obtain the following equation (11).

[0074]

[0075] Here, ξ· corresponds to the term with · on ξ in formula (11). ξ·· corresponds to the term with ·· on ξ in formula (11) (this also applies to the following formulas).

[0076] When substituting equations (3) to (5) into equation (11), T is the unit matrix, so we get the following formula (12).

[0077]

[0078] As described above, the motion equation of the physical coordinate system shown in equation (1) can be expressed as the motion equation of the model coordinate system as shown in equation (12).

[0079] Here, the external force vector {f} in equation (1) derived by the above method needs to be used as the external force vector {f ξ} given.

[0080] Therefore, equation (13) obtained by transforming equation (10) is used.

[0081] {f ξ}=[φ] T {f}…(13)

[0082] In the present embodiment, the displacement vector {ξ} (= [ξ (1) ξ (2) ···ξ (n) ]T).

[0083] Therefore, first, the mode matrix is ​​derived by eigenvalue analysis Using the Mode Matrix The mass matrix [M], viscosity matrix [C], and rigidity matrix [K] of the bogie frame 16 are derived from equations (3) to (5) to obtain the mass matrix [M] of the bogie frame 16 in the model coordinate system. ξ ], viscosity matrix [C ξ ], and the rigid matrix [K ξ ].

[0084] In addition, the first-order integration and the second-order integration related to time are performed based on the acceleration data measured by the acceleration sensors 21a and 21b, thereby respectively deriving the displacement vector {u0} and the velocity vector {u0·} on the left side of equation (2). Based on the viscous damping coefficients of the single links 18a and 18b and the shaft springs 19a and 19b, the viscosity matrices [C bc ], according to the rigidity of the single link 18a, 18b and the shaft spring 19a, 19b, the rigidity matrix [K bc ]. Substitute the information obtained in this way into formula (2) to derive the external forces acting on the points of application of the single links 18a, 18b and the points of application of the axle springs 19a, 19b. Furthermore, the external forces acting on the points of application of the bogie frame 16 are derived as the reaction forces of the external forces acting on the points of application of the single links 18a, 18b and the points of application of the axle springs 19a, 19a. Substitute the derived values ​​for the components of the external forces acting on the points of application of the external force vector {f} in formula (13), and substitute 0 (zero) for the other components. In addition, the pattern matrix Substitute into the right side of equation (13). And, through equation (13), the external force vector {f ξ}.

[0085] Then, the mass matrix [M] of the bogie frame 16 in the model coordinate system derived in the above manner is ξ ], viscosity matrix [C ξ ], rigid matrix [K ξ ], external force vector {f ξ} is substituted into equation (12). By solving equation (12), the displacement vector {ξ} of the bogie frame 16 in the model coordinate system can be derived.

[0086] Among them, in this embodiment, in order to improve the estimation accuracy of the displacement vector {ξ} of the bogie frame 16 in the model coordinate system, the displacement in the bogie frame 16 in the physical coordinate system is set as an observation variable, and the displacement and speed of the bogie frame 16 in the model coordinate system are set as state variables. And, the estimated value of the unobserved variable (state variable) is determined in such a way that the difference between the measured value of the observation variable and the calculated value based on formula (12) becomes smaller (becomes minimum). The estimated value of the state variable is determined in this way by performing an operation using a filter that performs data assimilation. As an example of a filter that performs data assimilation, there is a linear Kalman filter. In this embodiment, the case of using a linear Kalman filter is cited as an example for explanation. In the linear Kalman filter, the Kalman gain at which the error between the measured value and the calculated value of the observation variable or the expected value of the error becomes smaller (becomes minimum) is obtained, and the value of the unobserved variable (state variable) at this time is obtained. The linear Kalman filter itself can be implemented by a well-known technology.

[0087] In order to derive the estimated value of the displacement vector {ξ} of the bogie frame 16 in the model coordinate system in the above-mentioned manner, in the present embodiment, acceleration sensors 22a, 22b are installed on the bogie frame 16. Then, by performing a second-order integral with respect to time based on the acceleration data measured by the acceleration sensors 22a, 22b, the displacement of the installation position of the acceleration sensors 22a, 22b (i.e., the measured value of the observed variable described later) is derived. The acceleration sensors 22a, 22b can be implemented, for example, by the same components as the acceleration sensors 21a, 21b. The acceleration sensors 22a, 22b are installed at predetermined positions of the bogie frame 16. Figure 3 In the example, the case where the number of acceleration sensors 22a, 22b mounted on the bogie frame 16 is two is shown. However, the number of acceleration sensors mounted on the bogie frame 16 may be any number as long as it is one or more.

[0088] In addition, the state variable Ξ(∈R 2n ). In addition, equation (12) is expressed as the state equation of equation (15) below.

[0089]

[0090] The constituent equations of the state equation constituting the equation (15) are as follows: equations (16) to (17).

[0091]

[0092] Here, the state transition matrix A(∈R 2n×2n ) is expressed as the following formula (18).

[0093]

[0094] In addition, the vector F(∈R n ) is the external force vector {f ξ} is a vector, expressed as the following formula (19).

[0095] F=[f ξ (1) f ξ (2) …f ξ (n) ] T …(19)

[0096] In addition, the matrix G(∈R 2n×n ) is expressed as the following formula (20).

[0097]

[0098] In equations (18) to (20), the state variable Ξ is expressed by equation (14), which is obtained by expressing equation (12) in the form of equation (15). In addition, W in equation (15) is the system noise.

[0099] The observation variable y∈R is defined as follows (21): 3m m∈Z is the number of observation positions. In addition, the observation equation is expressed as the following formula (22).

[0100]

[0101] exist Figure 3 In the example shown, the number of acceleration sensors 22a and 22b is 2, and therefore the number of observation positions m is 2. ~ denotes measured quantities. The measured quantities include not only quantities directly measured but also quantities derived using other measured quantities.

[0102] In formula (21), we use u~ 1,1 、u~ 1,2 、u~ 1,3 、···u~ m,1 、u~m,2 、u~ m,3 The variable j with the value of 1, .., m before the comma (,) represents the observation position (in Figure 3 In the example shown, the acceleration sensors 22a and 22b are installed at their respective locations. 1,1 、u~ 1,2 、u~ 1,3 、···u~ m,1 、u~ m,2 、u~ m,3 The 1, 2, and 3 after the comma (,) represent the x1-axis component, the x2-axis component, and the x3-axis component, respectively. 1,1 The measured amount of the x1-axis direction component of the displacement at the attachment position of the acceleration sensor corresponding to j=1 is shown.

[0103] In addition, u~ in equation (21) corresponds to the value marked with ~ above u (this also applies to the following equations). In addition, in equation (22), V is the observation noise.

[0104] The observation variable y is associated with the state variable Ξ through the observation equation shown in equation (22). The observation matrix H∈R 3m ×2n Formula (9) can be expressed as the following formula (23).

[0105]

[0106] In formula (23), we use

[0107] The variable j with the value of 1, .., m before the comma (,) represents the observation position (in Figure 2 In the example shown, the acceleration sensors 22a and 22b are installed at the following positions:

[0108] The 1, 2, and 3 after the comma (,) represent the x1-axis component, the x2-axis component, and the x3-axis component, respectively. Therefore, for example, is the mode matrix corresponding to the first natural vibration mode, the installation position of the acceleration sensor corresponding to j=1, and the x1-axis direction component ingredients.

[0109] As shown in equations (14), (22), and (23), among the state variables Ξ, only the displacements ξ(1), ···, ξ(n) of the bogie frame 16 in the model coordinate system are multiplied by the model matrix Specifically, the mode matrix obtained by multiplying the displacement ξ(1), ···, ξ(n) of the bogie frame 16 in the mode coordinate system is The components of are the same mode matrices as the displacements of the bogie frame 16 in the mode coordinate system in terms of the order of the natural vibration mode The component of the mode matrix corresponding to the observation position is the same as the observation position where the displacement of the bogie frame 16 in the mode coordinate system occurs ingredients.

[0110] The displacement vector {ξ} (= [ξ (1) ξ (2) ···ξ (n) Then, the estimated value of the displacement vector {ξ} of the bogie frame 16 in the model coordinate system is converted into the displacement vector {u} of the bogie frame 16 in the physical coordinate system. For example, as shown in the following formula (24), the x in the node q of the grid can be derived. i Shift u in the axis direction (i∈{1, 2, 3}) q,i .

[0111]

[0112] Here, is the nth natural vibration mode, the node q of the grid, and x i The mode matrix corresponding to the axis direction By the above method, the estimated value of the displacement vector {ξ} of the bogie frame 16 in the model coordinate system is converted into the displacement vector {u} of the bogie frame 16 in the physical coordinate system. Thus, the displacement distribution (displacement in each grid) of the bogie frame 16 is obtained.

[0113] If the displacement distribution of the bogie frame 16 is obtained, each component ε of the strain tensor inside each element e of the mesh is e,11 , ε e,12 , ε e,13 , ε e,21 , ε e,22 , ε e,23 , ε e,31 , ε e,32 , ε e,33 It is represented by the following formulas (25a) to (25f).

[0114]

[0115] Here, x1, x2, and x3 represent coordinate components describing three degrees of freedom of displacement (here, x1-axis direction component, x2-axis direction component, and x3-axis direction component). e,1 、u e,2 、u e,3 The x1-axis direction component, the x2-axis direction component, and the x3-axis direction component respectively represent the internal displacement of each element e.

[0116] The relationship between stress and strain is expressed by the following equation (26). In addition, equation (26) can also be called the constitutive model of the elastic body.

[0117]

[0118] Here, σ e,11 , σ e,12 , σ e,13 The second to last 1 in 11, 12, and 13 represents the stress acting on the tiny surface perpendicular to the x1 axis, σ e,11 , σ e,21 , σ e,31 The second-to-last 1 in 11, 21, and 31 represents the value of the x1-axis component. e,21 , σ e,22 , σ e,23 The second to last digit 2 in 21, 22, and 23 represents the stress acting on the tiny surface perpendicular to the x2 axis, σ e,12 , σ e,22 , σ e,32 The last 2 of 12, 22, and 32 represents the value of the x2-axis component. e,31 , σ e,32 , σ e,33 The penultimate 3 of 31, 32, and 33 represents the stress acting on the tiny surface perpendicular to the x3 axis, σ e,13 , σ e,23 , σ e,33 The last digit 3 of 13, 23, and 33 represents the value of the component in the x3-axis direction.

[0119] λ and μ are Lame constant and rigidity coefficient, respectively. Lame constant λ and rigidity coefficient μ are respectively expressed by the following equations (27) and (28).

[0120]

[0121] Here, E is Young's modulus and ν is Poisson's ratio.

[0122] Through the above method, the stress distribution of the bogie frame 16 (the stress in each grid) is obtained.

[0123] The measuring device 300 is arranged in the body 11 of the railway vehicle. When the sampling time of a certain period arrives, the measuring device 300 obtains the following data: data indicating the acceleration measured by the acceleration sensors 21a, 21b, 22a, and 22b; and data indicating the running position of the railway vehicle. The method of obtaining the data indicating the running position of the railway vehicle is not particularly limited. The data indicating the running position of the railway vehicle can be obtained, for example, by using the GPS (Global Positioning System) configured on the railway vehicle. In addition, the moving distance of the railway vehicle at each time from the start time of the measurement can be derived based on the running speed of the railway vehicle and the time elapsed from the start time of the measurement, and the running position of the railway vehicle at each time can be derived based on the moving distance and the configuration of the track 20 on which the railway vehicle is running. In this case, the data indicating the position of the railway vehicle at the start time of the measurement is stored in the measuring device 300 before the start of the measurement.

[0124] The railway vehicle without the sensor is different from the above-mentioned railway vehicle except that it does not have the measuring device 300 and the acceleration sensors 21a, 21b, 22a, and 22b. Figure 2 , Figure 3 The railway vehicles shown have the same structure.

[0125] Data including data indicating acceleration measured by the acceleration sensors 21a, 21b, 22a, and 22b and data indicating the running position of the railway vehicle obtained by the measuring device 300 at each predetermined period is input into the estimation device 100 of the present embodiment together with the identification information of the railway vehicle. For example, the data successively obtained by the measuring device 300 is transmitted to the estimation device 100 using a wireless network.

[0126] The input data is stored in the measurement data storage unit 50. In addition, the measurement data storage unit 50 stores the operation conditions related to the travel route and travel speed of the railway vehicle according to the identification information of each railway vehicle. The operation conditions related to the travel speed are, for example, conditions related to the stop stations, indicating whether the railway vehicle stops at each station, whether it is a rapid train, or whether it is an express train.

[0127] The acquisition unit 52 regards a railway vehicle without a sensor as an estimation target, and acquires from the measurement data storage unit 50 sensor data measured for bogies of a portion of railway vehicles with sensors installed that have the same conditions as the bogies of the estimation target railway vehicle.

[0128] Specifically, the acquisition unit 52 acquires, from the measurement data storage unit 50, sensor data measured for a bogie of a portion of railway vehicles equipped with sensors that operate on the same route and have the same operating conditions as the bogie of the railway vehicle to be estimated, based on the route and operating conditions of the railway vehicle to be estimated.

[0129] The estimating unit 54 estimates the state of the bogie of the railway vehicle to be estimated, using a model prepared in advance for the bogie of the railway vehicle to be estimated, which estimates the state of the bogie from the vibration acceleration of the axle box, based on the data acquired by the acquiring unit 52. For example, the stress generated in the bogie is estimated as the state of the bogie.

[0130] Specifically, if Figure 4 As shown, the estimation unit 54 includes an eigenvalue analysis unit 402 , a mass derivation unit 403 , a viscosity derivation unit 404 , a rigidity derivation unit 405 , an external force derivation unit 406 , a displacement distribution derivation unit 407 , a stress distribution derivation unit 408 , and a stress-position relationship derivation unit 409 .

[0131] The estimation unit 54 executes the processing described below at each sampling time using information obtained at the same sampling time. For example, when each part uses data obtained at a certain sampling time, the data is obtained by the measurement device 300 at the sampling time.

[0132] The eigenvalue analysis unit 402 performs eigenvalue analysis on the motion equation of formula (1) to derive the eigenfrequency ω and the eigenvector Derived from the eigenvector The pattern matrix Furthermore, the mass matrix [M], the viscosity matrix [C], and the rigidity matrix [K] of the bogie frame 16 are derived from the formalization of the motion equation based on the finite element method.

[0133] The quality derivation unit 403 uses the pattern matrix The mass matrix [M] of the bogie frame 16 is derived from the mass matrix [M] of the bogie frame 16 in the model coordinate system according to equation (3). ξ ]. In the following description, the mass matrix of the bogie frame 16 in the modal coordinate system is referred to as the modal mass matrix as needed.

[0134] Viscosity derivation unit 404 uses a pattern matrix and the viscosity matrix [C] of the bogie frame 16. According to equation (4), the viscosity matrix [C] of the bogie frame 16 in the modal coordinate system is derived. ξ ]. In the following description, the viscosity matrix of the bogie frame 16 in the modal coordinate system is referred to as the modal viscosity matrix as needed.

[0135] The rigidity derivation unit 405 uses the mode matrix and the rigidity matrix [K] of the bogie frame 16, and the rigidity matrix [K] of the bogie frame 16 in the modal coordinate system is derived according to equation (5). ξ ]. In the following description, the rigidity matrix of the bogie frame 16 in the modal coordinate system is referred to as the modal rigidity matrix as needed.

[0136] The external force deriving unit 406 derives the displacement vector {u0} and the velocity vector {u0·} of the single links 18a, 18b and the shaft springs 19a, 19b, respectively, based on the acceleration data measured by the acceleration sensors 21a, 21b. The external force deriving unit 406 derives the viscosity matrix [C bc ]. In addition, the external force derivation unit 406 derives the rigidity matrices [K] of the single links 18a, 18b and the shaft springs 19a, 19b from the rigidities of the single links 18a, 18b and the shaft springs 19a, 19b, respectively. bc ].

[0137] The external force derivation unit 406 uses the displacement vector {u0}, velocity vector {u0·}, and viscosity matrix [C bc ], rigid matrix [K bc ], according to formula (2), the external forces acting on the points of application of the single links 18a, 18b and the points of application of the shaft springs 19a, 19b are derived, and as their reaction forces, the external forces acting on the various points of application of the bogie frame 16 are derived.

[0138] The external force derivation unit 406 substitutes the value derived in this way for the component of the external force acting on each application point among the components of the external force vector {f} of the bogie frame 16, and substitutes 0 (zero) for the corresponding components, thereby deriving the external force vector {f} of the bogie frame 16 as the external force distribution of the bogie frame 16. The external force vector {f} of the bogie frame 16 derived in this way and the mode matrix The external force vector {f ξ In the following description, the external force vector of the bogie frame 16 in the mode coordinate system is referred to as the mode external force vector as needed.

[0139] The shift distribution derivation unit 407 uses the mode quality matrix [M ξ ], mode viscosity matrix [C ξ ], mode rigidity matrix [K ξ ], model external force vector {f ξ}, the mode matrix corresponding to the installation positions of the acceleration sensors 21a and 21b The estimated value of the displacement vector {ξ} of the bogie frame 16 in the modal coordinate system is derived as the displacement distribution of the bogie frame 16 in the modal coordinate system based on the components of the acceleration measured by the acceleration sensors 22a and 22b. As described above, in the present embodiment, the displacement distribution deriving unit 407 uses the state equation shown in equation (15) and the observation equation shown in equation (22), as well as the measured values ​​of the observation variables derived from the acceleration data measured by the acceleration sensors 22a and 22b, and performs calculations based on the algorithm of the Kalman filter to derive the estimated value of the displacement vector {ξ} of the bogie frame 16 in the modal coordinate system. In the following description, the displacement vector of the bogie frame 16 in the modal coordinate system is referred to as a modal displacement vector as needed.

[0140] The shift distribution derivation unit 407 uses the estimated value of the modal shift vector {ξ} and the mode matrix According to equation (24), the displacement vector {u} of the bogie frame 16 in the physical coordinate system is derived as the displacement distribution of the bogie frame 16 in the physical coordinate system. In the following description, the displacement vector of the bogie frame 16 in the physical coordinate system is referred to as an actual displacement vector as needed.

[0141] The stress distribution deriving unit 408 uses the actual displacement vector {u} and derives the strain tensor inside each element e of the mesh as the stress distribution of the bogie frame 16 according to equations (25a) to (25f). The stress distribution deriving unit 408 uses the strain tensor inside each element e of the mesh and the Young's modulus E and Poisson's ratio ν of the bogie frame 16 and derives the stress tensor inside each element e of the mesh as the stress distribution of the bogie frame 16 according to equations (26) to (28).

[0142] The stress-position relationship derivation unit 409 stores the stress distribution of the bogie frame 16 and the running position of the railway vehicle in association with each other. In the following description, the stress distribution of the bogie frame 16 and the running position of the railway vehicle, which are associated with each other in this way, are referred to as stress-position relationship information as needed. The stress-position relationship information at each sampling time is obtained in the above manner.

[0143] As described above, the estimation unit 54 estimates the stress generated in the bogie at each sampling moment based on the data obtained by the acquisition unit 52, using a model prepared in advance for the bogie of the railway vehicle to be estimated, which estimates the state of the bogie from the vibration acceleration of the axle box, as the state of the bogie of the railway vehicle to be estimated.

[0144] The estimation device 100 is provided as an example. Figure 5 The computer 64 shown is implemented. The computer 64 includes a CPU 66, a memory 68, a storage unit 70 storing an estimation program 76, a display unit 26 including a monitor, and an input unit 28 including a keyboard or a mouse. The CPU 66, the memory 68, the storage unit 70, the display unit 26, and the input unit 28 are connected to each other via a bus 74.

[0145] The storage unit 70 is implemented by a HDD, SSD, flash memory, etc. The storage unit 70 stores an estimation program 76 for causing the computer 64 to function as the estimation device 100. The CPU 66 reads the estimation program 76 from the storage unit 70, expands it in the memory 68, and executes the estimation program 76.

[0146] <Function of the estimation device>

[0147] First, if Figure 6 As shown, on each route, the vibration acceleration of the axle box is measured by a measuring device 300 of a part of the railway vehicle equipped with a sensor, and then input to the estimation device 100 and stored in the measurement data storage unit 50. Figure 6 , an example is shown in which the vibration acceleration of the axle box is measured by a measuring device 300 of a part of railway vehicles equipped with sensors in routes R1 to R3.

[0148] Furthermore, route information and operating conditions of a part of railway vehicles equipped with sensors are stored in the measurement data storage unit 50 .

[0149] In addition, if Figure 7 As shown, when a railway vehicle without a sensor is traveling on each route, route information and operation conditions of the railway vehicle without a sensor are stored in the measurement data storage unit 50. Figure 7 2 shows an example in which a railway vehicle without a sensor is traveling on routes R1 to R3.

[0150] Next, the operator selects a plurality of railway vehicles including a railway vehicle without a sensor as estimation targets, and inputs information indicating the railway vehicles of the estimation targets into the estimation device 100. Figure 8 , an estimation process executed in the estimation device 100 in response to an operation such as an instruction to start the estimation process is described.

[0151] In step S100 of the estimation process, the acquisition unit 52 acquires, based on the route and operating conditions of the railway vehicle to be estimated, sensor data measured for the bogie of a portion of the railway vehicle equipped with sensors, which is operated on the same route and has the same operating conditions as the bogie of the railway vehicle to be estimated.

[0152] For example, Fig. 9 As shown, for each rail vehicle bogie, sensor data measured in the rail vehicle bogies on the same route and under the same operating conditions are obtained. Fig. 9 2 shows an example of acquiring sensor data measured in bogies of railway vehicles on the same routes and operating conditions as the plurality of routes when each railway vehicle travels across a plurality of routes.

[0153] In step S102 , the estimating unit 54 estimates the state of the bogie of the railway vehicle to be estimated based on the data acquired by the acquiring unit 52 , using a model prepared in advance for the bogie of the railway vehicle to be estimated, which estimates the state of the bogie from the vibration acceleration of the axle box.

[0154] In step S104 , the estimating unit 54 displays the estimation result based on the above-mentioned step S102 on the display unit 26 , and ends the estimation process.

[0155] The above step S104 is performed by Fig.10 The flowchart shown is implemented. Assume Fig.10 The processing of steps S601 to S610 of the flowchart is repeatedly executed at each sampling time.

[0156] First, in step S601 , the data acquisition unit 401 acquires data including data indicating the acceleration measured by the acceleration sensors 21 a , 21 b , 22 a , and 22 b and data indicating the travel position of the railway vehicle.

[0157] Next, in step S602, the eigenvalue analysis unit 402 performs eigenvalue analysis on the motion equation of formula (1) to derive the eigenfrequency ω and the eigenvector Derived from the eigenvector The pattern matrix

[0158] Next, in step S603, the quality derivation unit 403 uses the pattern matrix and the mass matrix [M] of the bogie frame 16, and according to equation (3), the modal mass matrix [M ξ ].

[0159] Next, in step S604, the viscosity derivation unit 404 uses the pattern matrix and the viscosity matrix [C] of the bogie frame 16, and the mode viscosity matrix [C] is derived through equation (4). ξ ].

[0160] Next, in step S605, the rigidity derivation unit 405 uses the mode matrix and the rigidity matrix [K] of the bogie frame 16, and the modal rigidity matrix [K] is derived through equation (5).ξ ].

[0161] Next, in step S606, the external force deriving unit 406 derives the displacement vector {u0} and the velocity vector {u0·} of the single link 18a, 18b and the shaft spring 19a, 19b, respectively, based on the acceleration data measured by the acceleration sensors 21a, 21b. The external force deriving unit 406 uses the displacement vector {u0}, the velocity vector {u0·}, and the viscosity matrix [C bc ], and the rigid matrix [K bc ], Pattern Matrix By using equations (2) and (13), the model external force vector {f ξ}.

[0162] In addition, the order of steps S603 to S606 is not fixed.

[0163] Next, in step S607, the shift distribution deriving unit 407 uses the mode quality matrix [M ξ ], mode viscosity matrix [C ξ ], mode rigidity matrix [K ξ ], model external force vector {f ξ}, the pattern matrix corresponding to the installation positions of the acceleration sensors 21a and 21b The components of the modal shift vector {ξ} and the acceleration measured by the acceleration sensors 22a and 22b are used, and equation (15) is used as the state equation and equation (22) is used as the observation equation. The displacement derived from the acceleration measured by the acceleration sensors 22a and 22b is used as the measured value of the observation variable, and the estimated value of the modal shift vector {ξ} is derived. The displacement distribution deriving unit 407 uses the estimated value of the modal shift vector {ξ} and the mode matrix The actual displacement vector {u} is derived as the displacement distribution of the bogie frame 16 through equation (24).

[0164] Next, in step S608 , the stress distribution deriving unit 408 derives the strain tensor inside each element e of the mesh as the stress distribution of the bogie frame 16 by using the actual displacement vector {u} according to equations (25a) to (25f).

[0165] Next, in step S609, the stress distribution derivation unit 408 uses the strain tensor inside each element e of the mesh, and the Young's modulus E and Poisson's ratio ν of the bogie frame 16, and derives the stress tensor inside each element e of the mesh as the stress distribution of the bogie frame 16 through equations (26) to (28).

[0166] Next, in step S610, the stress-position relationship deriving unit 409 associates the stress distribution of the bogie frame 16 and the running position of the railway vehicle with each other and stores them as stress-position relationship information. Fig.10 The processing of the flowchart is completed.

[0167] As described above, the estimation device 100 of the present embodiment estimates the state of the bogies of other railway vehicles having the same conditions as the bogies of the railway vehicles and not equipped with sensors, based on the sensor data measured for the bogies of the railway vehicles equipped with sensors. This makes it possible to estimate the state of the bogies of a plurality of railway vehicles over the operation period while suppressing the number of sensors.

[0168] For example, if there are N bogies of railway vehicles without sensors and M bogies of railway vehicles equipped with sensors used on the same route, according to the prior art, it is necessary to equip (N+M) bogies with sensors. That is, in order to grasp the status of each bogie, it is necessary to install sensors and other equipment on each bogie of the estimated object. On the other hand, in this embodiment, by using the data of sensors measured in the bogies of M railway vehicles, the number of sensors can be reduced and the status of the bogies of multiple railway vehicles can be estimated. By reducing the number of sensors, the machine cost and maintenance cost can be reduced. In addition, there can be at least one bogie of a railway vehicle equipped with a sensor on the travel route.

[0169] Furthermore, if information on the route on which the bogie of a railway vehicle that is not equipped with a sensor is obtained, the state of the bogie of the railway vehicle can be estimated.

[0170] Furthermore, the state of the bogie of the railway vehicle is estimated based on the sensor data measured at each sampling time for the bogie of a part of the railway vehicle equipped with the sensor, so that the state of the bogie of the railway vehicle can be estimated at desired intervals.

[0171] Since the vibration acceleration is generated differently depending on the operating conditions such as stopping at each station, rapid train, and express train, the state of the bogie can be estimated with high accuracy by dividing the data used for estimating the state of the bogie for each operating condition.

[0172] Furthermore, the railway vehicle to be estimated may be operated across a plurality of routes and operating conditions. In this case, the estimation device may estimate the state of the bogie of the railway vehicle to be estimated that is not equipped with a sensor, based on the sensor information measured for the bogies of the railway vehicles that are operated on the route and under the operating conditions and are equipped with sensors, for each combination of operating conditions related to the route and the travel speed.

[0173] Specifically, Fig.11 The illustrated estimation process is executed in the estimation device 100 .

[0174] In step S301 of the estimation process, the acquisition unit 52 specifies a plurality of combinations of the routes and the operating conditions for each of a plurality of routes, based on the routes and the operating conditions of the railway vehicle to be estimated.

[0175] In step S302, Figure 8 The estimation process shown is similarly performed for a combination of one route and one operation condition.

[0176] In step S303, it is determined whether the process of step S302 has been performed for the plurality of combinations determined in step S301. If there is a combination of routes and operation conditions for which the process of step S302 has not been performed, the process returns to step S302 and performs estimation processing for the combination.

[0177] When the process of step S302 is performed on the plurality of combinations determined in step S301, the process proceeds to step S304.

[0178] In step S304 , the estimating unit 54 integrates the estimation results in step S302 for a plurality of combinations, and estimates the state of the bogie of the railway vehicle to be estimated.

[0179] In step S305 , the estimating unit 54 displays the estimation result based on the above-mentioned step S304 on the display unit 26 , and ends the estimation process.

[0180] In addition, although the estimation device 100 estimates the stress generated in the bogie as the state of the bogie of the railway vehicle, the present invention is not limited to this. For example, the fatigue damage degree of the bogie may be estimated as the state of the bogie of the railway vehicle. In this case, the fatigue damage degree of the bogie may be estimated using a model for estimating the fatigue damage degree of the bogie based on the time series of the vibration acceleration of the axle box.

[0181] In addition, the estimation device 100 may also suggest a countermeasure based on the estimation result of the state of the bogie of the railway vehicle. For example, it may be suggested as a countermeasure to repair the track of the route where the bogie with high fatigue damage degree travels to reduce the fatigue damage degree of the bogie. In addition, for the bogie with high fatigue damage degree, it may also be suggested to change the operation method to extend the service life as a countermeasure. In addition, for the bogie with high fatigue damage degree, it may also be suggested as a countermeasure to reduce the risk by preferentially inspecting the part with high fatigue damage degree.

[0182] In addition, the case where the sensor carried by the bogie detects the physical quantity related to the axle box is used as an example for explanation, but it is not limited to this. For example, the sensor carried by the bogie can detect the physical quantity related to the spring cap. More specifically, the sensor carried by the bogie can also detect the acceleration, angular velocity, or displacement related to the spring cap. In addition, the physical quantity related to the axle box can also be estimated based on the physical quantity related to the spring cap detected by the sensor, and the state of the bogie of the railway vehicle can be estimated using the physical quantity related to the axle box.

[0183] Furthermore, the case where the data of the sensor mounted on the bogie of the railway vehicle with the same conditions as the railway vehicle to be estimated is described as an example, but the invention is not limited to this. In the case where the bogies of the railway vehicle with the same conditions as the railway vehicle to be estimated are multiple, the data of the sensors mounted on the multiple bogies may be integrated, and the state of the bogie of the railway vehicle to be estimated may be estimated using the integrated sensor data. For example, the state of the bogie of the railway vehicle to be estimated may be estimated using the averaged data of the sensor data or the data of the sensor with the larger vibration.

[0184] Furthermore, although the case where the estimation device 100 is installed in a location different from the railway vehicle has been described as an example, the present invention is not limited thereto. The estimation device 100 may be installed in the railway vehicle.

[0185] All documents, patent applications, and technical standards described in this specification are incorporated by reference into this specification to the same extent as if each individual document, patent application, and technical standard was specifically and individually described as being incorporated by reference.

[0186] Regarding the above-mentioned embodiment, the following supplementary notes are further disclosed.

[0187] (Note 1)

[0188] An estimation method is a method for estimating a state of a railway vehicle.

[0189] The estimation device estimates the state of the bogies of other railway vehicles having the same conditions as the bogies of the one railway vehicle and not equipped with sensors, based on sensor information measured for the bogies of the one railway vehicle equipped with sensors.

[0190] (Note 2)

[0191] The inference method as described in Supplementary Note 1 is characterized in that:

[0192] The bogies of the other railway vehicles having the same condition are bogies of railway vehicles that are operated on the same route as the bogies of the part of railway vehicles.

[0193] (Note 3)

[0194] The inference method as described in Supplementary Note 1 is characterized in that:

[0195] The bogies of the other railway vehicles having the same conditions are bogies of railway vehicles that are operated on the same route as the bogies of the part of railway vehicles and are operated under the same operating conditions related to the same travel speed.

[0196] (Note 4)

[0197] The estimation method according to any one of Supplementary Notes 1 to 3, wherein:

[0198] The sensor detects a physical quantity related to the axle box.

[0199] (Note 5)

[0200] The estimation method according to any one of Supplementary Notes 1 to 3, wherein:

[0201] The sensor detects a physical quantity related to the spring cap.

[0202] (Note 6)

[0203] As described in Appendix 5,

[0204] The estimating device estimates the state of the bogie of the other railway vehicle using the physical quantity related to the axle box estimated from the physical quantity related to the spring cap.

[0205] (Note 7)

[0206] The estimation method according to any one of Supplementary Notes 1 to 6,

[0207] The estimating device estimates stress generated in the bogie as a state of the bogie.

[0208] (Note 8)

[0209] The estimation method according to any one of Supplementary Notes 1 to 6,

[0210] The estimating device estimates the fatigue damage degree of the bogie as the state of the bogie.

[0211] (Note 9)

[0212] As described in Appendix 1,

[0213] The sensor detects physical quantities related to the axle box,

[0214] The estimation device estimates stress generated in the bogies of some railway vehicles that are railway vehicles operating on the route and under the operating conditions and are equipped with sensors, based on sensor information measured for the bogies of some railway vehicles that are railway vehicles operating on the route and under the operating conditions and are equipped with sensors, and the states of bogies of other railway vehicles that are railway vehicles operating on the route and under the operating conditions and are not equipped with sensors.

[0215] (Note 10)

[0216] As described in Appendix 1,

[0217] The sensor detects physical quantities related to the axle box,

[0218] The estimation device estimates the fatigue damage degree of the bogies of some railway vehicles which are railway vehicles operating on the route and under the operating conditions and are equipped with sensors, based on sensor information measured for the bogies of some railway vehicles which are railway vehicles operating on the route and under the operating conditions and are equipped with sensors, and the states of the bogies of other railway vehicles which are railway vehicles operating on the route and under the operating conditions and are not equipped with sensors.

[0219] (Note 11)

[0220] The estimation method according to any one of Supplementary Notes 1 to 10,

[0221] When the estimation device estimates the state of the bogies of railway vehicles operating on a plurality of routes and the state of the bogies of other railway vehicles not equipped with sensors,

[0222] For each of the plurality of routes, based on sensor information measured on the bogies of the railway vehicles operating on the routes and on the bogies of some railway vehicles equipped with sensors, the state of the bogies of the other railway vehicles is estimated;

[0223] and integrating the inferred states of the bogies of the other railway vehicles for each of the plurality of routes.

[0224] (Note 12)

[0225] An estimation device is an estimation device for estimating a state of a railway vehicle.

[0226] Based on sensor information measured on bogies of some railway vehicles equipped with sensors, the states of bogies of other railway vehicles having the same conditions as the bogies of the some railway vehicles and not equipped with sensors are estimated.

[0227] (Note 13)

[0228] An estimation program for estimating the state of a railway vehicle, the program causing a computer to execute

[0229] Based on sensor information measured on bogies of some railway vehicles equipped with sensors, the states of bogies of other railway vehicles having the same conditions as the bogies of the some railway vehicles and not equipped with sensors are estimated.

[0230] (Note 14)

[0231] An estimating device is an estimating device for estimating the state of a railway vehicle, comprising:

[0232] Memory, and

[0233] at least one processor connected to the memory;

[0234] The processor estimates the state of the bogies of other railway vehicles having the same conditions as the bogies of the one railway vehicle and not equipped with sensors, based on the sensor information measured for the bogies of the one railway vehicle equipped with sensors.

[0235] (Note 15)

[0236] A non-transitory storage medium storing a program executable by a computer for executing an estimation process, the non-transitory storage medium comprising:

[0237] The estimation process estimates the state of the bogies of other railway vehicles having the same conditions as the bogies of the one railway vehicle and not equipped with sensors, based on sensor information measured for the bogies of the one railway vehicle equipped with sensors.

Claims

1. An estimation method for estimating a state of a railway vehicle, The estimation device estimates the state of the bogies of other railway vehicles having the same conditions as the bogies of the one railway vehicle and not equipped with sensors, based on sensor information measured for the bogies of the one railway vehicle equipped with sensors.

2. The estimation method according to claim 1, characterized in that: The bogies of the other railway vehicles having the same condition are bogies of railway vehicles that are operated on the same route as the bogies of the part of railway vehicles.

3. The estimation method according to claim 1, characterized in that: The bogies of the other railway vehicles having the same conditions are bogies of railway vehicles that are operated on the same route and under the same operating conditions related to the same travel speed as the bogies of the part of railway vehicles.

4. The estimation method according to claim 1, characterized in that: The sensor detects a physical quantity related to the axle box.

5. The estimation method according to claim 1, characterized in that: The sensor detects a physical quantity related to the spring cap.

6. The estimation method according to claim 5, The estimating device estimates the state of the bogie of the other railway vehicle using the physical quantity related to the axle box estimated from the physical quantity related to the spring cap.

7. The estimation method according to claim 1, The estimating device estimates stress generated in the bogie as a state of the bogie.

8. The estimation method according to claim 1, The estimating device estimates the fatigue damage degree of the bogie as the state of the bogie.

9. The estimation method according to claim 1, The sensor detects physical quantities related to the axle box, The estimation device estimates stress generated in the bogies of some railway vehicles that are operated on the route and under the operating conditions and are equipped with sensors, based on sensor information measured for the bogies of some railway vehicles that are operated on the route and under the operating conditions and are equipped with sensors, and the states of bogies of other railway vehicles that are operated on the route and under the operating conditions and are not equipped with sensors.

10. The estimation method according to claim 1, The sensor detects physical quantities related to the axle box, The estimation device estimates the fatigue damage degree of the bogies of some railway vehicles which are railway vehicles operating on the route and under the operating conditions and are equipped with sensors, based on sensor information measured for the bogies of some railway vehicles which are railway vehicles operating on the route and under the operating conditions and are equipped with sensors, and the states of the bogies of other railway vehicles which are railway vehicles operating on the route and under the operating conditions and are not equipped with sensors.

11. The estimation method according to any one of claims 1 to 10, When the estimation device estimates the state of the bogies of other railway vehicles which are bogies of railway vehicles operating on a plurality of routes and are not equipped with sensors, For each of the plurality of routes, based on sensor information measured on bogies of some of the railway vehicles that are bogies of railway vehicles operating on the route and are equipped with sensors, the state of the bogies of the other railway vehicles is estimated, and integrating the inferred state of the bogies of the other railway vehicles for each of the plurality of routes.

12. An estimating device for estimating a state of a railway vehicle, Based on sensor information measured on bogies of some railway vehicles equipped with sensors, the states of bogies of other railway vehicles having the same conditions as the bogies of the some railway vehicles and not equipped with sensors are estimated.

13. An estimation program for estimating the state of a railway vehicle, which is used to cause a computer to execute: based on sensor information measured on the bogies of a part of railway vehicles equipped with sensors, estimating the state of bogies of other railway vehicles that have the same conditions as the bogies of the part of railway vehicles and are not equipped with sensors.

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