Method and device for indirectly measuring acting force of railway wheel rail

Abstract

The invention relates to the technical field of railway wheel rail measurement, and discloses a railway wheel rail acting force indirect measurement method and device, and the method comprises the steps: obtaining the inertia sensing data of a wheel set axle box and a framework on a railway vehicle, and the strain sensing data of a primary spring and an axle box rotating arm; according to the inertial sensing data and the strain sensing data, obtaining state estimation data of the railway vehicle wheel set and the framework; and according to the state estimation data, the inertia sensing data and the strain sensing data, obtaining mechanical data of the railway wheel-rail acting force. The vibration displacement and speed of the axle and the bogie frame are calculated through the inertia sensing data and the strain sensing data, the axle transverse force is calculated, the wheel rail vertical force is calculated by combining the axle transverse force, indirect measurement of the wheel rail acting force is achieved, and the accuracy of wheel rail force measurement can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wheel-rail measurement, and particularly relates to a wheel-rail force indirect measurement method and a measurement device. BACKGROUND

[0002] In the prior art, a strain gauge is attached to a wheel to measure wheel strain, so as to obtain wheel-rail force between the wheel and the rail. The method of measuring wheel strain by attaching a strain gauge to the wheel to obtain wheel-rail force has the main disadvantage that the force measurement is difficult and the economic cost is high, because the radial deformation and the axial deformation of the wheelset are difficult to decouple by attaching the strain gauge to the wheel, and the relationship between the wheelset deformation and the wheel-rail force size needs to be calibrated by experiments.

[0003] Therefore, the prior art still needs to be improved and developed. SUMMARY

[0004] The main purpose of the present application is to provide a wheel-rail force indirect measurement method and a measurement device, aiming at solving the problem that the wheel deformation is measured by attaching a strain gauge to the wheel to obtain wheel-rail force in the prior art, resulting in difficult measurement and high economic cost.

[0005] The first aspect of the embodiment of the present application provides a wheel-rail force indirect measurement method, which comprises the following steps: Obtaining inertia sensing data of a wheelset axle box and a frame and strain sensing data of a primary spring and an axle box swing arm on a railway vehicle; Obtaining state estimation data of the wheelset and the frame of the railway vehicle according to the inertia sensing data and the strain sensing data; Obtaining mechanical data of the wheel-rail force according to the state estimation data, the inertia sensing data and the strain sensing data.

[0006] Optionally, in an embodiment of the present application, the inertia sensing data comprises axle box acceleration data and frame acceleration data, and the strain sensing data comprises primary spring strain data and axle box swing arm strain data; The obtaining of the inertia sensing data of the wheelset axle box and the frame and the strain sensing data of the primary spring and the axle box swing arm on the railway vehicle specifically comprises: Receiving axle box acceleration data sent by a three-direction vibration acceleration sensor of the wheelset axle box on the railway vehicle, and receiving frame acceleration data sent by a three-direction vibration acceleration sensor of the frame on the railway vehicle; Receiving primary spring strain data sent by a spring strain sensor on the railway vehicle, and receiving axle box swing arm strain data of an axle box swing arm strain gauge on the railway vehicle.

[0007] Optionally, in an embodiment of the present application, the state estimation data of the railway wheel rail is obtained according to the inertial sensing data and the strain sensing data, and specifically includes: The state vector is defined in the state space model, and the state vector is predicted according to the axle box acceleration data and the frame acceleration data to obtain prediction estimation data; The state observation data is obtained according to the primary spring strain data and the axle box swing arm strain data; The prediction estimation data and the state observation data are fused by using Kalman filtering method to obtain the state estimation data of the railway wheel rail.

[0008] Optionally, in an embodiment of the present application, the prediction estimation data includes lateral estimation data and vertical estimation data; The state vector is predicted according to the axle box acceleration data and the frame acceleration data to obtain prediction estimation data, and specifically includes: The lateral state in the state vector is predicted according to the lateral acceleration in the axle box acceleration data and the frame acceleration data to obtain lateral estimation data; The vertical state in the state vector is predicted according to the vertical acceleration in the frame acceleration data to obtain vertical estimation data.

[0009] Optionally, in an embodiment of the present application, the state observation data includes strain observation values of the primary spring and strain observation values of the swing arm; The state observation data is obtained according to the primary spring strain data and the axle box swing arm strain data, and specifically includes: The observation equation related to displacement components and velocity components in the state vector is established; Based on the observation equation, the primary spring strain observation values are obtained according to the primary spring strain data, and the swing arm strain observation values are obtained according to the axle box swing arm strain data.

[0010] Optionally, in an embodiment of the present application, the state estimation data includes axle box lateral displacement, axle box lateral velocity, axle box vertical displacement and axle box vertical velocity of the wheel set, and frame lateral displacement, frame lateral velocity, frame vertical displacement and frame vertical velocity of the frame; The prediction estimation data and the state observation data are fused by using Kalman filtering method to obtain the state estimation data of the railway wheel rail, and specifically includes: The Kalman gain is calculated according to the prediction estimation data and the state observation data; updating the state vector according to the Kalman gain, the predicted estimation data and the state observation data to obtain the axle box lateral displacement, the axle box lateral velocity, the axle box vertical displacement and the axle box vertical velocity of the wheel set and the frame lateral displacement, the frame lateral velocity, the frame vertical displacement and the frame vertical velocity of the frame.

[0011] Optionally, in an embodiment of the present application, the mechanical data includes the wheelset lateral force and the wheel-rail vertical force. The state estimation data of the railway wheel-rail is obtained according to the state estimation data, the inertial sensing data and the strain sensing data. The wheelset lateral force is calculated according to the axle box acceleration data and the axle box swing arm strain data. The wheel-rail vertical force is calculated according to the wheelset lateral force, the axle box acceleration data, the axle box vertical displacement, the axle box vertical velocity, the frame vertical displacement and the frame vertical velocity.

[0012] Optionally, in an embodiment of the present application, the wheelset lateral force is calculated according to the axle box acceleration data and the axle box swing arm strain data, and specifically includes: The strain difference value is calculated according to the axle box swing arm strain data, and the initial lateral force is calculated according to the strain difference value; The inertial force is calculated according to the axle box acceleration data; The wheelset lateral force is obtained according to the initial lateral force and the inertial force.

[0013] The second aspect of the embodiment of the present application further provides a railway wheel-rail measurement device for implementing the railway wheel-rail force indirect measurement method in any of the above-mentioned schemes, wherein the railway wheel-rail measurement device includes a wheel set, an axle box, a primary steel spring, a frame and a swing arm, an axle box acceleration sensor, a frame acceleration sensor, a strain sensor, a strain gage and a processor; the axle box is connected with the wheel set, the primary steel spring is connected with the axle box, the frame is connected with the axle box, the swing arm is connected with the axle box, the axle box acceleration sensor is connected with the wheel set and the axle box, the frame acceleration sensor is connected on the frame, the strain sensor is connected on the primary steel spring, and the strain gage is connected on the inner and outer sides of the swing arm. The processor is configured to acquire the inertial sensing data of the wheel set axle box and the frame and the strain sensing data of the primary spring and the axle box swing arm on the railway vehicle; The processor is configured to obtain the state estimation data of the railway wheel-rail according to the inertial sensing data and the strain sensing data. The processor is configured to obtain the force data of the railway wheel-rail according to the state estimation data, the inertial sensing data and the strain sensing data.

[0014] Optionally, in one embodiment of the present application, the axle box acceleration sensor and the bogie acceleration sensor are both three-direction vibration acceleration sensors, and two strain gauges are provided, one of which is arranged on the outer side of the swing arm, and the other is arranged on the inner side of the swing arm.

[0015] Beneficial effects: the present application provides a railway wheel-rail force indirect measurement method and a measurement device, the present application calculates the vibration displacement and speed of the wheel axle and the bogie frame through inertial sensing data and strain sensing data, thereby calculating the wheel axle lateral force, and then calculating the wheel-rail vertical force, thereby realizing the indirect measurement of the wheel-rail force, and the present application can improve the accuracy of the wheel-rail force measurement. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0017] Figure 1 It is a front view of a preferred embodiment of the railway wheel-rail measurement device of the present application; Figure 2 It is a top view of a preferred embodiment of the railway wheel-rail measurement device of the present application; Figure 3 It is a flowchart of a preferred embodiment of the railway wheel-rail force indirect measurement method of the present application; Figure 4 It is a flowchart of the data fusion and Kalman filtering method in a preferred embodiment of the railway wheel-rail force indirect measurement method of the present application; Figure 5 It is a specific implementation step flowchart of the entire execution process in a preferred embodiment of the railway wheel-rail force indirect measurement method of the present application.

[0018] Explanation of reference signs: 1, wheel set; 2, axle box; 3, primary steel spring; 4, bogie frame; 5, axle box acceleration sensor; 6, strain sensor; 7, bogie acceleration sensor; 8, swing arm; 9, strain gauge.

[0019] Through the above drawings, the specific embodiments of the present application have been shown, and more detailed descriptions will be given hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0020] For the purpose, technical solutions and effects of the present application to be more clear and explicit, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. The described embodiments are only possible technical implementations of the present application, and not all possible implementations. Based on the embodiments in the present application, those skilled in the art can certainly combine the embodiments of the present application to obtain other embodiments without creative labor, and these embodiments are also within the protection scope of the present application.

[0021] In the related art, the method of measuring wheel deformation by pasting strain gauges to obtain wheel-rail force has the main disadvantage of low force measurement accuracy, because strain gauge measurement is affected by many factors, such as temperature change, material fatigue, and paste quality, resulting in large errors in measurement data, thereby affecting the accuracy of wheel-rail force measurement.

[0022] In view of the problem that measuring wheel deformation by pasting strain gauges on the wheel to obtain wheel-rail force results in low measurement accuracy, the present application calculates the vibration displacement and speed of the wheelset and bogie frame through inertial sensor data and strain sensor data, thereby calculating the wheelset lateral force, and then calculating the wheel-rail vertical force in combination with the wheelset lateral force, thereby realizing indirect measurement of wheel-rail force. The present application can improve the accuracy of wheel-rail force measurement.

[0023] In the present application, the wheel-rail force between the wheel and the track is calculated through the parameters of the wheel and the parameters of the bogie by obtaining the displacement, speed and acceleration of the wheel and the displacement, speed and acceleration of the bogie.

[0024] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0025] As Figure 1 and Figure 2As shown, the railway wheel rail measuring device described in the preferred embodiment of the present application is used for the indirect measurement method of railway wheel rail force. The railway wheel rail measuring device comprises a wheel set 1, an axle box 2, a primary steel spring 3, a frame 4 and a swing arm 8, an axle box acceleration sensor 5, a frame acceleration sensor 7, a strain sensor 6 and a strain gauge 9; the axle box 2 is connected with the wheel set 1, the primary steel spring 3 is connected with the axle box 2, the frame 4 is connected with the axle box 2, the swing arm 8 is connected with the axle box 2, the axle box acceleration sensor 5 is connected with the wheel set 1 and the axle box 2, the frame acceleration sensor 7 is connected on the frame 4, the strain sensor 6 is connected on the primary steel spring 3, and the strain gauge 9 is connected on the inner and outer sides of the swing arm 8; the processor is used to acquire the inertial sensing data of the wheel set 1, the axle box 2 and the frame 4 on the railway vehicle and the strain sensing data of the primary spring and the swing arm 8 of the axle box 2; the processor is used to obtain the state estimation data of the railway wheel rail according to the inertial sensing data and the strain sensing data; and the processor is used to obtain the force data of the railway wheel rail according to the state estimation data, the inertial sensing data and the strain sensing data.

[0026] In an embodiment of the present application, as shown in Figure 1 The axle box acceleration sensor 5 and the frame acceleration sensor 7 are both three-direction vibration acceleration sensors. Figure 2 As shown, two strain gauges 9 are provided, one of which is arranged on the outer side of the swing arm 8, and the other is arranged on the inner side of the swing arm 8.

[0027] The indirect measurement method of railway wheel rail force described in the preferred embodiment of the present application comprises the following steps: Figure 3 As shown, the indirect measurement method of railway wheel rail force comprises the following steps: In step S101, the inertial sensing data of the wheel set axle box and the frame on the railway vehicle and the strain sensing data of the primary spring and the swing arm of the axle box are acquired.

[0028] In a possible implementation, the inertial sensing data comprises axle box acceleration data of the wheel set axle box and frame acceleration data of the frame, and the strain sensing data comprises primary spring strain data and axle box swing arm strain data. The axle box acceleration data sent by the three-direction vibration acceleration sensor of the wheel set axle box on the railway vehicle is received, and the frame acceleration data sent by the three-direction vibration acceleration sensor of the frame on the railway vehicle is received; the primary spring strain data sent by the spring strain sensor on the railway vehicle is received, and the axle box swing arm strain data of the axle box swing arm strain gauge on the railway vehicle is acquired.

[0029] Specifically, the longitudinal, lateral and vertical accelerations of the wheelset axle box and the frame are measured online using a three-axis acceleration sensor; the inner and outer strains of the axle box swing arm are measured using inner and outer strain gauges in the axle box swing arm; and the strain of the primary spring is measured using a primary spring strain gauge. The acceleration signals measured by the three-axis acceleration sensor and the strain signals measured by the axle box swing arm and the primary spring strain gauge are preliminarily processed by a signal conditioner; the analog signals after conditioning are converted into digital signals; and the converted digital signals are output to a processor.

[0030] In step S102, optimal state estimation data of the wheelset and the frame of the railway vehicle are obtained according to the inertial sensing data and the strain sensing data.

[0031] In a possible implementation, a state vector is defined in a state space model, and the state vector is predicted according to the axle box acceleration data and the frame acceleration data to obtain prediction estimation data; state observation data are obtained according to the primary spring strain data and the axle box swing arm strain data; and the prediction estimation data and the state observation data are fused by using a Kalman filtering method to obtain state estimation data of the railway wheelset.

[0032] It should be noted that the data of different sensors are related to each other through the mechanical relationship of the wheelset. Strain-force / displacement: the strain (tensile stress ε1, compressive stress ε2) of the axle box swing arm is directly related to the lateral force (F x ) of the wheelset (F x ≈k(ε1-ε2) according to the theory of material mechanics), and at the same time, this force F x also causes the lateral displacement (d wx , d gx ) of the wheelset and the frame, i.e., F x ≈k(d wx -d gx ), so that the strain data indirectly reflect the displacement. Acceleration-displacement / speed: the accelerometer measures the vibration acceleration, and theoretically, the speed can be obtained by one-time integration, and the displacement can be obtained by twice integration, but direct integration will cause infinite accumulation of errors due to noise and drift. Wherein, k is the stiffness coefficient, d wx is the lateral displacement of the wheelset, and d gx is the lateral displacement of the frame.

[0033] In a possible implementation, the prediction estimation data includes lateral estimation data and vertical estimation data. The lateral state in the state vector is predicted according to the lateral acceleration in the axle box acceleration data and the frame acceleration data to obtain the lateral estimation data; and the vertical state in the state vector is predicted according to the vertical acceleration in the frame acceleration data to obtain the vertical estimation data.

[0034] In a possible implementation, the state observation data includes a set of spring strain observation values and a set of axle box boom strain observation values. An observation equation related to displacement components and velocity components in the state vector is established; based on the observation equation, a set of spring strain observation values is obtained from the set of spring strain data, and a set of axle box boom strain observation values is obtained from the set of axle box boom strain data.

[0035] In a possible implementation, the state estimation data includes axle box lateral displacement, axle box lateral velocity, axle box vertical displacement, axle box vertical velocity of a wheel set, and frame lateral displacement, frame lateral velocity, frame vertical displacement, and frame vertical velocity of a frame. A Kalman gain is calculated according to the prediction estimation data and the state observation data; the state vector is updated according to the Kalman gain, the prediction estimation data, and the state observation data, to obtain the axle box lateral displacement, the axle box lateral velocity, the axle box vertical displacement, and the axle box vertical velocity of the wheel set, and the frame lateral displacement, the frame lateral velocity, the frame vertical displacement, and the frame vertical velocity of the frame.

[0036] Specifically, the state vector x is defined as: [left axle box vertical displacement d wzL , left axle box vertical velocity v wzL , right axle box vertical displacement d wzR , right axle box vertical velocity v wzR , left frame vertical displacement d gzL , left frame vertical velocity v gzL , right frame vertical displacement d gzR , right frame vertical velocity v gzR , axle box lateral displacement d wy , axle box lateral velocity v wy , frame lateral displacement d gy , frame lateral velocity v gy ] In the prediction process, the lateral state (d wy , v wy , d gy , v gy ) is predicted using the lateral acceleration of the axle box and the frame, the left vertical state (d wzL , v wzL , d gzL , v gzL ) is predicted using the left vertical acceleration of the axle box and the frame, and the right vertical state (d wzR , v wzR , d gzR , v gzR), specifically by using the optimal state estimate at the last time step and the accelerometer data at the current time step, the filter predicts the state at the current time step according to the kinematic model of the system, calculating: current displacement ~ last time step displacement + last time step velocity * time step, current velocity ~ last time step velocity + current acceleration * time step. This stage relies on the accelerometer and can capture the high frequency dynamic changes well, but the prediction will drift over time due to integration, and the uncertainty will increase. That is, the filter predicts the values of velocity and displacement by using the measured acceleration data and the physical model. It should be noted that the observation equation is calculated according to the predicted displacement and velocity to calculate the strain of the boom and the strain of the primary spring, and the strain is compared with the measured strain, and then the coefficient matrix in the prediction equation is adjusted.

[0037] In the fusion process, the predicted value and the observed value are input into the Kalman filter to calculate the optimal state estimate (i.e., the most accurate d wzL , v wzL , d wzR , v wzR , d gzL , v gzL , d gzR , v gzR , d wy , v wy , d gy , v gy ). The filter compares the strain observation value derived from the acceleration data with the measured strain value; the filter calculates a Kalman gain, which determines whether the predicted value or the observed value should be trusted more; if the strain sensor is very accurate (small observation noise), the filter will pull the final displacement estimate value to the strain-derived value, thereby strongly correcting the drift caused by acceleration integration; if the accelerometer is very stable in a short time (small process noise), the filter will tend to believe the predicted value to deal with the possible transient interference of the strain signal. This updating process simultaneously corrects the estimates of displacement and velocity because they are related. The final output is the optimal estimate value fused from the two information sources.

[0038] It should be noted that the state observer or advanced filtering algorithm is used in the present application to "reconstruct" or "estimate" the required state quantities (i.e., displacement and velocity) from the acceleration and strain signals. Acceleration itself contains all the information of displacement and velocity (because acceleration is the derivative of velocity, and velocity is the derivative of displacement). The difficulty lies in how to accurately restore these information from noise through integration. Separate integration is not feasible, and other constraints must be introduced.

[0039] The quantity to be estimated is defined as X = [d wzL , v wzL , d wzR , v wzR, d gzL , v gzL , d gzR , v gzR , d wy , v wy , d gy , v gy ] T , T is transpose, where d wzL and v wzL are vertical displacement and velocity of the left axle box of the wheelset, d wzR and v wzR are vertical displacement and velocity of the right axle box of the wheelset, d gzL and v gzL are vertical displacement and velocity of the left side of the frame, d gzR and v gzR are vertical displacement and velocity of the right side of the frame, d wy and v wy are lateral displacement and velocity of the axle box of the wheelset, d gy and v gy are lateral displacement and velocity of the frame. The system state equation is established according to the mechanical principle to describe how the state evolves: X k = AX k-1 + Bu k-1 (discrete form), u can be known excitation or as unknown input, where X k is the internal state at the current time k, X k-1 is the internal state at the last time k-1, A is the state transition matrix, B is the input matrix, u k-1 is the input vector. The relationship between the measurement value and the state variable is described according to the measurement, and the Kalman filter observer is used: Z=HX+v, where H is the observation matrix, v is the measurement noise, and the observation value Z=[ε h , ε b ] T , ε h and ε b are the strains of the series spring and the swing arm. Working principle: at each time step, first, the evolution of the state is predicted according to the model (prediction step), and then the prediction is corrected by using the actual measured strain (correction step). By continuously minimizing the error between the predicted value and the measured value, the optimal estimate of the displacement and velocity of the wheelset and the frame is finally output.

[0040] In step S103, mechanical data of the railway wheel-rail force is obtained according to the state estimation data, the inertial sensing data and the strain sensing data.

[0041] In a possible implementation, the mechanical data includes wheel axle lateral force and wheel rail vertical force. The wheel axle lateral force is calculated according to the axle box acceleration data and the axle box swing arm strain data; the wheel rail vertical force is calculated according to the wheel axle lateral force and the vertical acceleration data, the vertical speed data and the vertical displacement data of the axle box and the frame of the wheel set.

[0042] In a possible implementation, a strain difference value is calculated according to the axle box swing arm strain data, and an initial lateral force is calculated according to the strain difference value; an inertial force is calculated according to the axle box lateral acceleration data; the wheel axle lateral force is obtained according to the initial lateral force and the inertial force.

[0043] It should be noted that the calculation of the wheel axle lateral force is based on the coupling relationship between the strain of the axle box swing arm and the lateral acceleration. The axle box swing arm produces bending deformation under the action of the lateral force, and the difference between the outer strain (tensile stress) and the inner strain (compressive stress) directly reflects the size of the lateral force, and the lateral acceleration provides dynamic correction to eliminate the inertial effect.

[0044] Specifically, the strain difference Δε=ε1-(-ε2)=ε 1+ 2 of the outer strain ε1 (tensile stress) and the inner strain ε2 (compressive stress) of the axle box swing arm is calculated, and a linear relationship between the strain difference and the lateral force is established through a bench test or finite element analysis; the lateral acceleration a wx is combined to correct the inertial force effect, F x =k(ε 1+ ε2)-ma wx ; m is the mass of the wheel set.

[0045] Specifically, in the process of calculating the wheel rail vertical force, a wheel set vertical dynamics equation (basic physical model) is established. The wheel set is subjected to the action of the left wheel rail vertical force P L , the right wheel rail vertical force P R , the left primary steel spring vertical force F L , the right primary steel spring vertical force F R and its own gravity Mg, and its motion follows Newton's second law. Ma wz =P L +P R Mg-F L -F R . Wherein: a wz is the vertical acceleration of the wheel set axle box mass center (direct measurement), M is the mass of the wheel set (known parameter), g is the gravity acceleration (known), this equation has only one equation, but two unknowns (P L and P R ), so another equation is needed.

[0046] Establish the dynamic equations for the wheelset rotation. The wheelset rotation is caused by a vertical force P from the left wheel-rail axis. L Vertical force P on the right wheel-rail R Left-side steel spring force F L Right side steel spring force F R The effect of this motion is governed by Newton's second law: Ja... wt =(P L -P R )L1-(F L -F R L2, where a wt Let be the rotational acceleration of the wheelset axle box about its center of mass, J be the moment of inertia of the wheelset (known parameter), L1 be the lateral distance between the nominal rolling circle of the wheel tread and the center of mass of the wheelset (known geometric parameter), and L2 be the lateral distance between the first-stage spring and the center of mass of the wheelset (known geometric parameter). Let a be the rotational acceleration of the wheelset axle box about its center of mass. wt Ja can be obtained from the measured vertical accelerations on the left and right sides of the wheelset axle box. wt =0.5×(a wzL -a wzR ) / L2.

[0047] Solve the combined equations of motion for the wheelset vertical dynamics and the equations of motion for the wheelset rotation, and determine the vertical force P on the left side of the wheel-rail system. L and the vertical force P of the right wheel and rail R It is also necessary to know the vertical force F of the first series of steel springs. L Vertical force F of the steel spring on the right side R Vertical force F of a steel spring L And the vertical force F of the steel spring on the right side R The calculation formula is: F L =K L (d gyL -d wyL )+C L (v gyL -v wyL ) and F R =K R (d gyR -d wyR )+C R (v gyR -v wyR ), where K L and C L K represents the stiffness and damping of the left primary suspension (which can be obtained through calibration). R and C R The stiffness and damping of the right-side primary suspension (obtainable through calibration), d gyL For the vertical displacement on the left side of the frame, d wyL v represents the vertical displacement of the left axle box of the wheelset.gyL is the vertical velocity of the left side of the frame, v wyL is the vertical velocity of the left side of the axle box of the wheel set, d gyR is the vertical displacement of the right side of the frame, d wyR is the vertical displacement of the right side of the axle box of the wheel set, v gyR is the vertical velocity of the right side of the frame, v wyR is the vertical velocity of the right side of the axle box of the wheel set.

[0048] The present application provides dynamic excitation information through an inertial sensor (accelerometer), but simple integration will have drift, and direct measurement of structural deformation is provided through a strain sensor, which can be used to reverse the force, but is not sensitive to high-frequency vibration, and the two are fused through Kalman filtering and state estimation, the high-frequency performance of the accelerometer and the low-frequency stability of the strain gauge are used to output accurate, drift-free displacement and velocity states (d wzL , v wzL , d wzR , v wzR , d gzL , v gzL , d gzR , v gzR , d wy , v wy , d gy , v gy ), these high-quality state data lay a solid foundation for accurately calculating the left and right wheel rail vertical forces and wheel axle lateral forces. That is, the lateral and vertical state parameters are necessary inputs for solving the wheel rail vertical forces P L and P R , ensuring that the final force calculation results can reflect dynamic excitation and accurately consider the geometric deformation of the suspension system.

[0049] Referring to Figure 4 and Figure 5 , the above-mentioned indirect measurement method of railway wheel rail force of the present application will be further described through specific embodiments: Step K1, the calculation of wheel rail force is solved by using the motion equation. During the vibration signal test process: three-axis acceleration sensors are used to measure the longitudinal, lateral and vertical accelerations of the wheel set axle box and the frame; the inner and outer strain gauges of the axle box swing arm measure the inner and outer strains of the axle box swing arm; the strain gauge of the primary spring measures the strain of the primary spring; Step K2, digitization of vibration signals: the acceleration and strain are amplified and filtered through a signal conditioner to convert them into digital signals; Step K3, the wheel rail force is solved by using the motion equation, which needs to know the motion velocity and displacement of the frame and the wheel rail, and the stress of the swing arm. During the calculation process of the wheel axle and bogie frame vibration displacement and velocity: based on the collected acceleration and strain signals, the method of data fusion and Kalman filtering is used to calculate the lateral displacement dwx and lateral velocity v wx Lateral displacement d of the frame gx and lateral velocity v gx ; Step K4, Calculation of lateral force on the wheel axle: Based on the lateral acceleration a of the wheelset axle box. wx Calculate the lateral force F of the wheel axle based on the strain ε1 on the outer side and the strain ε2 on the inner side of the axle box swing arm. x ; Step K5, Calculation of vertical force between wheel and rail: Based on the vertical acceleration a of the wheelset axle box. wz Left vertical displacement d zL Right vertical displacement d zR Lateral force F of wheel and axle x Calculate the vertical force P on the left wheel rail. L and the vertical force P of the right wheel and rail R .

[0050] It is important to note that the core innovation of this application is that the motion speed and displacement are calculated using an advanced filtering algorithm (Kalman filtering algorithm, sometimes also called a state observer), thus eliminating the need for displacement and speed sensors. Traditional methods require displacement and speed sensors, thus saving on sensor placement. Therefore, the measurement system of this application is simpler, easier to implement on-site, and the reduced impact of sensor placement on train operation is less significant and safer. The filtering algorithm uses state equations to predict (or calculate) the displacement and speed of the wheelset and axle box. The accuracy of the prediction is determined by the observation equations. The observation equations calculate the strain of the swing arm and primary spring based on the predicted displacement and speed, and then compare it with the measured stress. If the deviation is large, the parameters of the state equations are adjusted, and the displacement and speed of the wheelset and axle box are re-predicted until the predicted displacement and speed of the wheelset and axle box meet the requirements (i.e., the optimal estimate is achieved).

[0051] This application enables indirect measurement of wheel-rail interaction forces, and the results are more accurate and reliable, providing a more reliable basis for railway vehicle safety assessment and health monitoring.

[0052] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0053] In the description of the application, the meaning of "N" is at least two, for example two, three, etc., unless otherwise explicitly specifically limited.

[0054] Any processes or methods described in the flowcharts or otherwise described herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for performing specific logic functions (or steps) or portions thereof, and the various embodiments of the application can include additional or fewer processes, steps, or portions of code. Moreover, the various embodiments of the application can be implemented in hardware, software, or a combination thereof, and can be implemented with additional functions as desired. The various embodiments of the application can also be embodied as computer readable code on a computer readable medium. The computer readable medium can be a non-transitory computer readable medium. Where a computer program product is implemented as computer readable code on a computer readable medium, the computer program product, computer readable medium, and computer readable code can be essentially the same as described above.

[0055] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be embodied in non-transitory computer-readable storage medium that comprises code containing instructions that, when executed by an instruction execution system, apparatus, or device, cause the instruction execution system, apparatus, or device to perform the functions described herein. The computer-readable storage medium can be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable storage medium can be a computer- readable storage medium that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable storage medium can also be any medium that can be used to store the desired information dynamically generated during the execution of the program. A computer-readable storage medium can be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires (electronic), a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). Note that the computer-readable storage medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0056] It should be understood that portions of the application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, and in another embodiment, any of the following technologies, or combinations thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application-specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field-programmable gate arrays (FPGA), and the like.

[0057] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a computer readable storage medium, and when the programs are executed, one or a combination of the steps of the method embodiments is included.

[0058] In addition, each functional unit in each embodiment of the present application can be integrated into one processing module, or each unit can be physically present separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0059] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

[0060] It should be understood that the application of the present application is not limited to the above examples, and those skilled in the art can improve or change the above-mentioned examples according to the above-mentioned description, and all these improvements and changes shall fall within the scope of the claims of the present application.

[0061] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the above-mentioned embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for indirect measurement of railway wheel-rail interaction force, characterized in that, The indirect measurement method for railway wheel-rail forces includes: Acquire inertial sensing data of the wheel set axle box and frame of the railway vehicle, as well as strain sensing data of the primary spring and axle box swing arm; Based on the inertial sensing data and the strain sensing data, state estimation data for the railway vehicle wheelset and frame are obtained; Based on the state estimation data, the inertial sensing data, and the strain sensing data, the mechanical data of the force between the railway wheel and rail are obtained.

2. The indirect measurement method for railway wheel-rail force according to claim 1, characterized in that, The inertial sensing data includes axle box acceleration data and frame acceleration data, and the strain sensing data includes primary spring strain data and axle box swing arm strain data. The acquisition of inertial sensing data of the upper wheelset axle boxes and frames of railway vehicles, as well as strain sensing data of the primary springs and axle box swing arms, specifically includes: It receives axle box acceleration data sent by the three-dimensional vibration acceleration sensor of the wheel set axle box on the railway vehicle, and also receives frame acceleration data sent by the three-dimensional vibration acceleration sensor of the frame on the railway vehicle. It receives spring strain data from spring strain sensors on railway vehicles and axle box arm strain data from axle box arm strain gauges on railway vehicles.

3. The indirect measurement method for railway wheel-rail force according to claim 2, characterized in that, The step of obtaining railway wheel-rail state estimation data based on the inertial sensing data and the strain sensing data specifically includes: A state vector is defined in the state-space model, and the state vector is predicted based on the axle box acceleration data and the frame acceleration data to obtain the prediction estimate data; Based on the strain data of the first series springs and the strain data of the axle box swing arm, state observation data is obtained; The Kalman filter method is used to fuse the predicted estimation data and the state observation data to obtain the state estimation data of the railway wheel and rail.

4. The indirect measurement method for railway wheel-rail force according to claim 3, characterized in that, The prediction estimation data includes horizontal estimation data and vertical estimation data; The step of predicting the state vector based on the axle box acceleration data and the frame acceleration data to obtain prediction estimation data specifically includes: Based on the lateral acceleration data of the axle box and the frame acceleration data, the lateral state in the state vector is predicted to obtain lateral estimation data; The vertical state in the state vector is predicted based on the vertical acceleration in the frame acceleration data to obtain vertical estimation data.

5. The indirect measurement method for railway wheel-rail force according to claim 4, characterized in that, The state observation data includes the strain observation values ​​of the first series springs and the strain observation values ​​of the rotating arm; The process of obtaining state observation data based on the strain data of the primary spring and the strain data of the axle box swing arm specifically includes: Establish observation equations related to the displacement and velocity components in the state vector; Based on the observation equation, the observed values ​​of the spring strain of the first series are obtained from the spring strain data of the first series, and the observed values ​​of the swing arm strain are obtained from the axle box swing arm strain data.

6. The indirect measurement method for railway wheel-rail force according to claim 5, characterized in that, The state estimation data includes the lateral displacement, lateral velocity, vertical displacement, and vertical velocity of the wheelset axle box, as well as the lateral displacement, lateral velocity, vertical displacement, and vertical velocity of the frame. The process of fusing the predicted estimation data and the state observation data using the Kalman filter method to obtain the railway wheel-rail state estimation data specifically includes: Calculate the Kalman gain based on the predicted estimation data and the state observation data; The state vector is updated based on the Kalman gain, the predicted estimation data, and the state observation data to obtain the axle box lateral displacement, axle box lateral velocity, axle box vertical displacement, and axle box vertical velocity of the wheelset, as well as the frame lateral displacement, frame lateral velocity, frame vertical displacement, and frame vertical velocity of the frame.

7. The indirect measurement method for railway wheel-rail force according to claim 6, characterized in that, The mechanical data includes the lateral force of the wheel and axle and the vertical force of the wheel and rail; The step of obtaining the mechanical data of the railway wheel-rail interaction force based on the state estimation data, the inertial sensing data, and the strain sensing data specifically includes: Calculate the lateral force of the wheel axle based on the axle box acceleration data and the axle box swing arm strain data; The wheel-rail vertical force is calculated based on the lateral force of the wheel axle, the acceleration data of the axle box, the vertical displacement of the axle box, the vertical velocity of the axle box, the vertical displacement of the frame, and the vertical velocity of the frame.

8. The indirect measurement method for railway wheel-rail force according to claim 7, characterized in that, The calculation of the lateral force of the wheel axle based on the axle box acceleration data and the axle box swing arm strain data specifically includes: The strain difference is calculated based on the strain data of the axle box boom, and the initial lateral force is calculated based on the strain difference. Calculate the inertial force based on the axle box acceleration data; The lateral force of the wheel axle is obtained based on the initial lateral force and the inertial force.

9. A railway wheel-rail measuring device for implementing the indirect measurement method of railway wheel-rail force according to any one of claims 1 to 8, characterized in that, The railway wheel-rail measuring device includes a wheelset, an axle box, a primary steel spring, a frame and a swing arm, an axle box acceleration sensor, a frame acceleration sensor, a strain sensor, strain gauges, and a processor. The axle box is connected to the wheelset, the primary steel spring is connected to the axle box, the frame is connected to the axle box, the swing arm is connected to the axle box, the axle box acceleration sensor is connected to the wheelset and the axle box, the frame acceleration sensor is connected to the frame, the strain sensor is connected to the primary steel spring, and the strain gauges are connected to the inner and outer sides of the swing arm. The processor is used to acquire inertial sensing data of the wheelset axle boxes and frames of railway vehicles, as well as strain sensing data of the primary springs and axle box swing arms. The processor is used to obtain railway wheel-rail state estimation data based on the inertial sensing data and the strain sensing data; The processor is used to obtain the force data of the railway wheel and rail based on the state estimation data, the inertial sensing data, and the strain sensing data.

10. The railway wheel and rail measuring device according to claim 9, characterized in that, Both the axle box acceleration sensor and the frame acceleration sensor are triaxial vibration acceleration sensors. Two strain gauges are provided, one of which is located on the outside of the rotating arm and the other is located on the inside of the rotating arm.

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