Method and device for indirect measurement of wheel-rail forces on a railway

Abstract

The application relates to the technical field of railway wheel-rail measurement, and discloses a railway wheel-rail action force indirect measurement method and a measurement device. The railway wheel-rail action force indirect measurement method comprises the following steps: acquiring inertial sensing data of wheelset axleboxes and a bogie on a railway vehicle and strain sensing data of primary springs and axlebox swing arms; obtaining state estimation data of the wheelset and the bogie of the railway vehicle according to the inertial sensing data and the strain sensing data; and obtaining mechanical data of the railway wheel-rail action force according to the state estimation data, the inertial sensing data and the strain sensing data. The vibration displacement and speed of the wheel axle and the bogie frame are calculated through the inertial sensing data and the strain sensing data, so that the wheel axle lateral force is calculated, and then the wheel-rail vertical force is calculated in combination with the wheel axle lateral force, so that the indirect measurement of the wheel-rail action force is realized. The application can improve the accuracy of wheel-rail force measurement.

Description

Technical Field

[0001] This application relates to the field of wheel-rail measurement technology, and in particular to an indirect method and device for measuring the force between railway wheels and rails. Background Technology

[0002] In existing technologies, wheel strain is measured by attaching strain gauges to the wheel to obtain the wheel-rail force between the wheel and the rail. The main drawback of this method is that the force measurement is difficult and costly. This is because it is difficult to decouple the radial and axial deformation of the wheelset when attaching strain gauges to the wheel, and the relationship between the amount of wheelset deformation and the magnitude of the wheel-rail force needs to be calibrated experimentally.

[0003] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0004] The main purpose of this application is to provide an indirect method and device for measuring the force between railway wheels and rails, which aims to solve the problem that the existing technology obtains the wheel-rail force by measuring the wheel deformation by attaching strain gauges to the wheel, resulting in high measurement difficulty and high economic cost.

[0005] The first aspect of this application provides a method for indirectly measuring the force between railway wheels and rails, the method comprising the following steps:

[0006] 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;

[0007] Based on the inertial sensing data and the strain sensing data, state estimation data for the railway vehicle wheelset and frame are obtained;

[0008] 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.

[0009] Optionally, in one embodiment of this application, 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;

[0010] 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:

[0011] 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.

[0012] 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.

[0013] Optionally, in one embodiment of this application, obtaining the railway wheel-rail state estimation data based on the inertial sensing data and the strain sensing data specifically includes:

[0014] 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;

[0015] 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;

[0016] 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.

[0017] Optionally, in one embodiment of this application, the prediction estimation data includes lateral estimation data and vertical estimation data;

[0018] 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:

[0019] 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;

[0020] The vertical state in the state vector is predicted based on the vertical acceleration in the frame acceleration data to obtain vertical estimation data.

[0021] Optionally, in one embodiment of this application, the state observation data includes strain observation values ​​of a series of springs and strain observation values ​​of the rotating arm;

[0022] 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:

[0023] Establish observation equations related to the displacement and velocity components in the state vector;

[0024] 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.

[0025] Optionally, in one embodiment of this application, the state estimation data includes the axle box lateral displacement, axle box lateral velocity, axle box vertical displacement and axle box vertical velocity of the wheelset, and the frame lateral displacement, frame lateral velocity, frame vertical displacement and frame vertical velocity of the frame.

[0026] 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:

[0027] Calculate the Kalman gain based on the predicted estimation data and the state observation data;

[0028] 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.

[0029] Optionally, in one embodiment of this application, the mechanical data includes the lateral force of the wheel axle and the vertical force of the wheel and rail;

[0030] 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:

[0031] Calculate the lateral force of the wheel axle based on the axle box acceleration data and the axle box swing arm strain data;

[0032] 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.

[0033] Optionally, in one embodiment of this application, the step of calculating the lateral force of the wheel axle based on the axle box acceleration data and the axle box swing arm strain data specifically includes:

[0034] 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.

[0035] Calculate the inertial force based on the axle box acceleration data;

[0036] The lateral force of the wheel axle is obtained based on the initial lateral force and the inertial force.

[0037] A second aspect of this application also provides a railway wheel and rail measuring device for implementing the indirect measurement method of railway wheel and rail force as described in any of the above-described schemes. The railway wheel and 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.

[0038] 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.

[0039] The processor is used to obtain railway wheel-rail state estimation data based on the inertial sensing data and the strain sensing data;

[0040] 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.

[0041] Optionally, in one embodiment of this application, both the axle box acceleration sensor and the frame acceleration sensor are triaxial vibration acceleration sensors, and two strain gauges are provided, one strain gauge is disposed on the outer side of the rotating arm, and the other strain gauge is disposed on the inner side of the rotating arm.

[0042] Beneficial effects: This application provides an indirect measurement method and device for railway wheel-rail interaction force. This application calculates the vibration displacement and velocity of the wheel axle and bogie frame through inertial sensing data and strain sensing data, thereby calculating the lateral force of the wheel axle. Then, it calculates the vertical force of the wheel-rail by combining the lateral force of the wheel axle, thus realizing the indirect measurement of wheel-rail interaction force. This application can improve the accuracy of wheel-rail force measurement. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a front view of a preferred embodiment of the railway wheel and rail measuring device of this application;

[0045] Figure 2 This is a top view of a preferred embodiment of the railway wheel and rail measuring device of this application;

[0046] Figure 3 A flowchart illustrating a preferred embodiment of the indirect measurement method for railway wheel-rail interaction forces according to this application;

[0047] Figure 4 This is a flowchart illustrating the data fusion and Kalman filtering methods in a preferred embodiment of the indirect measurement method for railway wheel-rail interaction forces according to this application.

[0048] Figure 5 This is a flowchart illustrating the specific implementation steps of the entire execution process in a preferred embodiment of the indirect measurement method for railway wheel-rail forces according to this application.

[0049] Explanation of reference numerals in the attached figures:

[0050] 1. Wheelset; 2. Axle box; 3. Primary steel spring; 4. Frame; 5. Axle box accelerometer; 6. Strain sensor; 7. Frame accelerometer; 8. Swing arm; 9. Strain gauge.

[0051] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0052] To make the objectives, technical solutions, and effects of this application clearer and more explicit, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only possible technical implementations of this application and not all possible implementations. Based on the embodiments in this application, those skilled in the art can obtain other embodiments without creative effort, and these embodiments are also within the protection scope of this application.

[0053] In related technologies, the method of measuring wheel deformation by attaching strain gauges to obtain wheel-rail force has a major drawback: the accuracy of force measurement is low. This is because strain gauge measurement is affected by a variety of factors, such as temperature changes, material fatigue, and bonding quality, which leads to large errors in the measurement data and thus affects the accuracy of wheel-rail force measurement.

[0054] To address the issue of low accuracy in obtaining wheel-rail force by measuring wheel deformation using strain gauges attached to the wheel, this application calculates the vibration displacement and velocity of the wheel axle and bogie frame using inertial and strain sensor data, thereby calculating the lateral force of the wheel axle. The lateral force of the wheel axle is then combined with the lateral force of the wheel axle to calculate the vertical force of the wheel-rail, thus realizing indirect measurement of wheel-rail forces. This application can improve the accuracy of wheel-rail force measurement.

[0055] In this application, the displacement, velocity, and acceleration of the wheel and the displacement, velocity, and acceleration of the bogie are obtained, and the wheel-rail force between the wheel and the track is calculated using the parameters of the wheel and the bogie.

[0056] The technical solutions of this 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 may not be described again in some embodiments.

[0057] like Figure 1 and Figure 2 As shown in the preferred embodiment of this application, the railway wheel-rail measuring device is used for an indirect method of measuring the force of railway wheels and rails. The railway wheel-rail measuring device includes a wheelset 1, an axle box 2, a primary steel spring 3, a frame 4 and a rotating 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 to the wheelset 1, the primary steel spring 3 is connected to the axle box 2, the frame 4 is connected to the axle box 2, the rotating arm 8 is connected to the axle box 2, the axle box acceleration sensor 5 is connected to the wheelset 1 and the axle box 2, the frame acceleration sensor 7 is connected to the frame 4, and the strain sensor 6 is connected to the axle box 2. Sensor 6 is connected to the primary steel spring 3, and strain gauge 9 is connected to the inner and outer sides of the rotating arm 8; the processor is used to acquire inertial sensing data of the wheelset 1, axle box 2, and frame 4 on the railway vehicle, as well as strain sensing data of the primary spring, axle box 2, and rotating arm 8; 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 railway wheel-rail force data based on the state estimation data, the inertial sensing data, and the strain sensing data.

[0058] In one embodiment of this application, as Figure 1 As shown, both the axle box acceleration sensor 5 and the frame acceleration sensor 7 are triaxial vibration acceleration sensors; Figure 2 As shown, there are two strain gauges 9, one strain gauge 9 is disposed on the outside of the rotating arm 8, and the other strain gauge 9 is disposed on the inside of the rotating arm 8.

[0059] The preferred embodiment of this application describes an indirect method for measuring the force between railway wheels and rails, such as... Figure 3As shown, the indirect measurement method for railway wheel-rail force includes the following steps:

[0060] In step S101, 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, are acquired.

[0061] In one possible implementation, the inertial sensing data includes axle box acceleration data and frame acceleration data of the wheelset axle box, and the strain sensing data includes primary spring strain data and axle box swing arm strain data. The system receives axle box acceleration data from a three-dimensional vibration acceleration sensor of the wheelset axle box on the railway vehicle, and also receives frame acceleration data from a three-dimensional vibration acceleration sensor of the frame on the railway vehicle; it receives primary spring strain data from a spring strain sensor on the railway vehicle, and also collects axle box swing arm strain data from strain gauges on the axle box swing arm of the railway vehicle.

[0062] Specifically, a triaxial accelerometer is used to measure the longitudinal, lateral, and vertical accelerations of the wheelset axle box and frame online; strain gauges on the inner and outer sides of the axle box swing arm are used to measure the strain on the inner and outer sides of the axle box swing arm, respectively; and strain gauges on the primary spring are used to measure the strain of the primary spring. The acceleration signals measured by the triaxial accelerometer and the strain signals measured by the axle box swing arm and primary spring strain gauges are preliminarily processed by a signal conditioner; the conditioned analog signals are converted into digital signals; and the converted digital signals are output to the processor.

[0063] In step S102, optimal state estimation data for the railway vehicle wheelset and frame are obtained based on the inertial sensing data and the strain sensing data.

[0064] In one possible implementation, a state vector is defined in a state-space model, and the state vector is predicted based on the axle box acceleration data and the frame acceleration data to obtain predicted estimation data; state observation data is obtained based on the primary spring strain data and the axle box swing arm strain data; the predicted estimation data and the state observation data are fused using the Kalman filter method to obtain the railway wheel-rail state estimation data.

[0065] It should be noted that the data from different sensors are interconnected 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) of the wheel axle. x Related (according to the theory of mechanics of materials, F) x ≈k(ε1-ε2)), and at the same time, this force F x This will also cause lateral displacement (d) of the wheel axle and frame. wx ,d gx ), that is, F x ≈k(dwx -d gx Thus, strain data indirectly reflects displacement. Accelerometers measure vibration acceleration. Theoretically, velocity can be obtained through one integration, and displacement through two integrations. However, direct integration will lead to infinite accumulation of errors due to noise and drift. Here, k is the stiffness coefficient, and d... wx d represents the lateral displacement of the wheel axle. gx This refers to the lateral displacement of the framework.

[0066] In one possible implementation, the prediction estimation data includes lateral estimation data and vertical estimation data. Lateral estimation data is obtained by predicting the lateral state in the state vector based on the lateral acceleration data from the axle box acceleration data and the frame acceleration data; vertical estimation data is obtained by predicting the vertical state in the state vector based on the vertical acceleration data from the frame acceleration data.

[0067] In one possible implementation, the state observation data includes strain observations of the primary spring system and the rotor arm. Observation equations related to the displacement and velocity components in the state vector are established; based on the observation equations, the strain observations of the primary spring system are obtained from the strain data of the primary spring system, and the strain observations of the rotor arm are obtained from the strain data of the axle box rotor arm.

[0068] In one possible implementation, the state estimation data includes the axle box lateral displacement, axle box lateral velocity, axle box vertical displacement, and axle box vertical velocity of the wheelset, and the frame lateral displacement, frame lateral velocity, frame vertical displacement, and frame vertical velocity of the frame. A Kalman gain is calculated 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, and the frame lateral displacement, frame lateral velocity, frame vertical displacement, and frame vertical velocity of the frame.

[0069] Specifically, the state vector x is defined as: [vertical displacement d of the left axle box] wzL Vertical velocity v of the left axle box wzL Vertical displacement d of the right axle box wzR Vertical velocity v of the right axle box wzR Vertical displacement d of the left frame gzL Vertical velocity v of the left frame gzL Vertical displacement d of the right frame gzR Vertical velocity v of the right-side frame gzR Lateral displacement d of the axle box wy Lateral velocity v of the axle box wy Lateral displacement d of the structure gyHorizontal velocity of the structure v gy In the prediction process, the lateral acceleration of the wheelset axle box and frame is used to predict the lateral state (d). wy v wy d gy v gy The left-side vertical state (d) is predicted using the left-side vertical acceleration of the axle box and frame. wzL v wzL d gzL v gzL The right-side vertical acceleration of the axle box and frame is used to predict the right-side vertical state (d). wzR v wzR d gzR v gzR Specifically, the filter uses the optimal state estimate from the previous moment and the accelerometer data from the current moment to predict the current state based on the system's kinematic model. The calculations are: current displacement ≈ previous displacement + previous velocity × time step; current velocity ≈ previous velocity + current acceleration × time step. This stage relies on the accelerometer and can effectively capture high-frequency dynamic changes, but the prediction will drift over time due to integration, increasing uncertainty. In other words, the filter predicts the velocity and displacement values ​​using measured acceleration data and the physical model. It should be noted that the observation equation calculates the strain of the rotating arm and the strain of the primary spring based on the predicted displacement and velocity, compares this strain with the measured strain, and then adjusts the coefficient matrix in the prediction equation.

[0070] During the fusion process, the predicted and observed values ​​are input into a 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 gyThe filter compares the strain observation derived from the acceleration data with the measured strain value. The filter calculates a Kalman gain, which determines whether the predicted or observed value should be trusted more. If the strain sensor is very accurate (low observation noise), the filter will pull the final displacement estimate closer to the strain-derived value, thus strongly correcting the drift caused by acceleration integration. If the accelerometer is very stable over a short period (low process noise), the filter is more inclined to trust the predicted value to cope with potential transient interference in the strain signal. This update process corrects both displacement and velocity estimates simultaneously because they are correlated. The final output is the optimal estimate that integrates both information sources.

[0071] It should be noted that this application employs a state observer or advanced filtering algorithm to "reconstruct" or "estimate" the required state quantities (i.e., displacement and velocity) from acceleration and strain signals. Acceleration itself contains all the information about displacement and velocity (because acceleration is the derivative of velocity, and velocity is the derivative of displacement). The challenge lies in how to accurately reconstruct this information from noise through integration. Integration alone is not feasible; other constraints must be introduced.

[0072] The quantity to be estimated is taken as the state and 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 the transpose, where d wzL and v wzL It is the vertical displacement and velocity of the left axle box of the wheelset, d wzR and v wzR It is the vertical displacement and velocity of the right axle box of the wheelset, d gzL and v gzL The vertical displacement and velocity on the left side of the frame, d gzR and v gzR It represents the vertical displacement and velocity on the right side of the frame, d wy and v wy It refers to the lateral displacement and velocity of the wheelset axle box, d gy and v gy These are the lateral displacements and velocities of the framework. Based on the principles of mechanics, a system state equation is established to describe how the state evolves: X k =AX k-1 +Bu k-1(Discrete form), u can be a known excitation or an unknown input, where X k Let X be the internal state at the current time k. k-1 Let A be the internal state at the previous time step k-1, and let B be the state transition matrix and U be the input matrix. k-1 The input vector is defined as follows. Based on the measurement description of the relationship between the measured values ​​and the state variables, a Kalman filter observer Z = HX + v is used, where H is the observation matrix, v is the measurement noise, and the observed values ​​Z = [ε]. h , ε b ] T , ε h and ε b It involves the strain of a series of springs and a swingarm. Working principle: At each time step, the evolution of the state is first predicted based on the model (prediction step), and then this prediction is corrected using the actual measured strain (correction step). By continuously minimizing the error between the predicted and measured values, the optimal estimate of the displacement and velocity of the wheelset and frame is finally output.

[0073] In step S103, mechanical data of the railway wheel-rail interaction force are obtained based on the state estimation data, the inertial sensing data, and the strain sensing data.

[0074] In one possible implementation, the mechanical data includes the lateral force of the wheel axle and the vertical force of the wheel-rail system. The lateral force of the wheel axle is calculated based on the axle box acceleration data and the axle box swing arm strain data; the vertical force of the wheel-rail system is calculated based on the lateral force of the wheel axle and the vertical acceleration, vertical velocity, and vertical displacement data of the wheelset axle box and frame.

[0075] In one possible implementation, the strain difference is calculated based on the strain data of the axle box arm, and the initial lateral force is calculated based on the strain difference; the inertial force is calculated based on the lateral acceleration data of the axle box; and the wheel axle lateral force is obtained based on the initial lateral force and the inertial force.

[0076] It should be noted that the calculation of the lateral force of the wheel axle is based on the coupling relationship between the strain and lateral acceleration of the axle box swing arm. The axle box swing arm undergoes bending deformation under the action of lateral force. The difference between the outer strain (tensile stress) and the inner strain (compressive stress) directly reflects the magnitude of the lateral force, while the lateral acceleration provides dynamic correction and eliminates the influence of inertia.

[0077] Specifically, the strain difference Δε is calculated as follows: Δε = ε1 - (-ε2) = ε 1+ ε2, through bench tests or finite element analysis, establish the linear relationship between strain difference and lateral force; combined with lateral acceleration a wx Correcting for the influence of inertial forces, F x =k(ε1+ ε2)-ma wx m is the wheelset mass.

[0078] Specifically, in calculating the vertical force between the wheel and rail, the vertical dynamic equation of the wheelset (basic physical model) is established, and the wheelset is subjected to a vertical force P from the left wheel and rail in the vertical direction. L Vertical force P on the right wheel-rail R Vertical force F of the steel spring on the left side L Vertical force F of the steel spring on the right side R Due to the interaction of its own gravity Mg, its motion follows Newton's second law. wz =P L +P R Mg-F L -F R Among them: a wz Let M be the vertical acceleration of the center of mass of the wheelset axle box (directly measured), M be the mass of the wheelset (known parameter), and g be the acceleration due to gravity (known). This equation has only one equation, but two unknowns (P). L and P R Therefore, it cannot be solved directly, and thus another equation is required.

[0079] 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.

[0080] 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 (which can be obtained 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 v is the vertical velocity on the left side of the frame. wyL Let d be the vertical velocity of the left axle box of the wheelset. gyR For the vertical displacement on the right side of the frame, d wyR v represents the vertical displacement of the right axle box of the wheelset. gyR v is the vertical velocity on the right side of the frame. wyR The vertical velocity of the right axle box of the wheelset.

[0081] This application provides dynamic excitation information through inertial sensors (accelerometers), but simple integration will result in drift. While strain sensors provide direct measurement of structural deformation and can invert forces, they are insensitive to high-frequency vibrations. By fusing Kalman filtering and state estimation, and leveraging the high-frequency performance of the accelerometer and the low-frequency stability of the strain gauge, accurate and drift-free displacement and velocity states (d) are output. wzL v wzL d wzR v wzR d gzL v gzL d gzR v gzR d wy v wy d gy v gyThese high-quality state data provide a solid foundation for accurately calculating the vertical forces on the left and right sides of the wheel and rail, and the lateral forces on the wheel and axle. In other words, the lateral and vertical state parameters are crucial for solving the wheel-rail vertical force P. L and P R The necessary inputs ensure that the final force calculation results reflect both dynamic excitation and accurately account for the geometric deformation of the suspension system.

[0082] See Figure 4 and Figure 5 The following specific embodiments further illustrate the above-described indirect measurement method of railway wheel-rail force according to this application:

[0083] Step K1: The wheel-rail force is calculated using the equation of motion. During vibration signal testing: a triaxial accelerometer measures the longitudinal, lateral, and vertical accelerations of the wheelset axle box and frame online; strain gauges on the inner and outer sides of the axle box swing arm measure the strain on the inner and outer sides of the axle box swing arm, respectively; and strain gauges on the primary spring measure the strain of the primary spring.

[0084] Step K2, digitization of vibration signals: The acceleration and strain are amplified and filtered by a signal conditioner and converted into digital signals;

[0085] Step K3: Solving for the wheel-rail force using the equations of motion requires knowing the motion velocities and displacements of the frame and wheel-rail, as well as the stress on the swing arm. In the calculation of the vibration displacement and velocity of the wheel axle and bogie frame: based on the collected acceleration and strain signals, data fusion and Kalman filtering methods are used to calculate the lateral displacement d of the wheel axle. wx and lateral velocity v wx Lateral displacement d of the frame gx and lateral velocity v gx ;

[0086] 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 ;

[0087] 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 .

[0088] 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).

[0089] 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.

[0090] 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.

[0091] In the description of this application, “N” means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.

[0092] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0093] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable storage medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable storage medium could be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0094] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0095] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0096] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0097] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

[0098] It should be understood that the application of this application is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this 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 steel 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 railway wheel-rail interaction force are obtained; The inertial sensing data includes axle box acceleration data and frame acceleration data, and the strain sensing data includes primary steel 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 steel 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 strain data of a series of steel springs sent by spring strain sensors on railway vehicles, and also receives strain data of axle box arm strain gauges on railway vehicles. 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 primary steel spring 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. The prediction and estimation data include 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; Based on the vertical acceleration in the frame acceleration data, the vertical state in the state vector is predicted to obtain vertical estimation data; The condition observation data includes the strain observation values ​​of the primary steel spring and the strain observation values ​​of the rotating arm; The process of obtaining state observation data based on the strain data of the primary steel 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 strain values ​​of the first series steel springs are obtained according to the strain data of the first series steel springs, and the observed strain values ​​of the swing arm are obtained according to the strain data of the axle box swing arm. 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.

2. The indirect measurement method for railway wheel-rail force according to claim 1, 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.

3. The indirect measurement method for railway wheel-rail force according to claim 2, 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.

4. 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 3, characterized in that, The railway wheel and rail measuring device includes a wheelset, an axle box, a primary steel spring, a frame and an axle box 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 axle box 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 axle box 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 steel 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.

5. The railway wheel and rail measuring device according to claim 4, 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 axle box rotating arm, and the other is located on the inside of the axle box rotating arm.

Images