Method for judging driving safety of axle system

Abstract

The invention relates to the technical field of railway bridge operation and maintenance, and provides an axle system driving safety evaluation method which comprises the following steps: establishing a train-bridge rail integrated subsystem, and establishing a vibration model of the train-bridge rail integrated subsystem based on a virtual excitation method and a deterministic time history method; solving the vibration model to obtain a mean value and a standard deviation of the wheel-rail force; calculating a driving safety index according to the mean value and the standard deviation of the wheel-rail force; establishing a probability distribution function and a density function of the driving safety index according to the driving safety index; a confidence coefficient is set, and a security threshold corresponding to the confidence coefficient is obtained on the probability distribution function and the density function; and judging the driving safety of the train according to the safety threshold. The technical problems that the probability distribution form of the driving safety indexes in the existing driving safety evaluation method is not accurate enough, and the driving safety cannot be accurately and objectively evaluated can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of railway bridge operation and maintenance, and particularly relates to a method for evaluating the running safety of a vehicle-bridge system. Background Art

[0002] With the rapid development of railway bridge construction, the operation quality and running safety of the bridge-track integrated system have received increasing attention. Current railway bridge design codes, such as the "Basic Code for Railway Bridge Design TB10002.1 - 2005", the "Code for High-Speed Railway Design TB10621 - 2014", and the "Code for Heavy-Haul Railway Design TB10625 - 2017", have stipulated the limit values of running safety indicators (such as derailment coefficient, wheel load reduction rate, wheel-axle lateral force, etc.), but the specific calculation methods are not clearly defined and usually need to rely on on-site test data to determine.

[0003] In recent years, some scholars have introduced the random vibration theory (virtual excitation method) into vehicle-bridge dynamic analysis, and established a new method for directly calculating vehicle-bridge dynamic responses from the power spectrum of track irregularities, avoiding the accidental factors brought by individual samples of track irregularities and effectively improving the objectivity of vehicle-bridge dynamic analysis. Since the virtual excitation method can only obtain the standard deviation of the structural random response, the structural reliability study often assumes that the random samples follow a Gaussian distribution and uses the 3-sigma rule for safety evaluation. However, according to the calculation requirements of the code, the running safety indicators (especially the derailment coefficient) do not necessarily follow a Gaussian distribution. Therefore, the probability distribution form of the existing running safety evaluation method still has defects and cannot accurately and objectively evaluate the running safety of the vehicle-bridge system. Summary of the Invention

[0004] The purpose of the present invention is to overcome the technical problem that the probability distribution form of the running safety indicators in the existing running safety evaluation method is not accurate enough to accurately and objectively evaluate the running safety, and provide a method for evaluating the running safety of a vehicle-bridge system.

[0005] In a first aspect, the present invention provides a method for evaluating the running safety of a vehicle-bridge system, including the following steps:

[0006] S1. Establish a train-bridge-track integrated subsystem through the multi-body dynamics theory;

[0007] S2. Based on the virtual excitation method and the deterministic time-history method, establish a vibration model of the train-bridge-track integrated subsystem, and the vibration model includes a random vibration model and a deterministic vibration model;

[0008] S3. Solve the vibration model to obtain the mean value and standard deviation of the wheel-rail force, and the wheel-rail force includes the lateral wheel-rail force and the vertical wheel-rail force;

[0009] S4. Calculate the train operation safety index based on the mean and standard deviation of the wheel-rail forces. The train operation safety index includes the mean and standard deviation of the wheel weight reduction rate, the mean and standard deviation of the axle lateral force, and the derailment coefficient.

[0010] S5. Establish the probability distribution function and density function of the train operation safety index based on the train operation safety index.

[0011] S6. Set the confidence level and obtain the safety threshold corresponding to the confidence level on the probability distribution function and density function.

[0012] S7. Evaluate the train operation safety according to the safety threshold.

[0013] Preferably, the train operation safety index includes the following expressions:

[0014]

[0015] u LF =2u Q ,σ LF =2σ Q

[0016] In the formula, u UL represents the mean of the wheel load reduction rate; u P represents the mean of the vertical wheel-rail force; P 0 represents the static axle load; σ UL represents the standard deviation of the wheel load reduction rate; σ P represents the standard deviation of the vertical wheel-rail force; u LF represents the mean of the axle lateral force; u Q represents the mean of the lateral wheel-rail force; σ LF represents the standard deviation of the axle lateral force; σ Q represents the standard deviation of the lateral wheel-rail force.

[0017] Preferably, the probability distribution function and density function include the following expressions:

[0018] F UL (x)=Φ(x,u UL ,σ UL )

[0019] F LF (x)=Φ(x,u LF ,σ LF )

[0020]

[0021] In the formula, F UL (x) represents the probability distribution function of the wheel weight reduction rate; F LF (x) represents the probability distribution function of the axle lateral force; fDR (x) represents the density function of the derailment coefficient; x represents the integration variable; Φ represents the standard normal distribution function; represents following the standard normal distribution function; e represents the natural constant; π represents the pi.

[0022] Preferably, the establishment of the train-bridge-rail integrated subsystem includes the following steps:

[0023] Establish a train model using the rigid body dynamics method; establish a bridge model using the mode superposition method; couple the train model and the bridge model into a train-bridge-rail integrated subsystem through the wheel-rail force.

[0024] Preferably, the train-bridge-rail integrated subsystem includes the following expressions:

[0025]

[0026] In the formula, M v represents the mass matrix of the train; C v represents the resistance matrix of the train; K v represents the stiffness matrix of the train; F v represents the external load vector applied to the train; ξ b represents the frequency matrix of the bridge; Ω b represents the damping matrix of the bridge; represents the transposed matrix of the modal matrix of the bridge; F b represents the external load vector applied to the bridge; X v represents the dynamic response of the train; X b represents the dynamic response of the bridge.

[0027] Preferably, in step S3, the whole process iteration method is used to solve the vibration model, and the calculation converges when the difference in wheel-rail force between two adjacent iterations is within a predetermined interval.

[0028] Preferably, the predetermined interval is less than or equal to 100 N.

[0029] Preferably, the upper and lower limits of the confidence level are obtained according to the 3sigma rule.

[0030] Preferably, the safety threshold takes the absolute value.

[0031] In a second aspect, the present invention provides a vehicle-bridge system driving safety evaluation system, including:

[0032] A memory in which at least one instruction is stored;

[0033] A processor, the processor is communicatively connected to the memory, and the processor can read the instruction and execute a vehicle-bridge system driving safety evaluation method of the present invention.

[0034] Advantages of the present invention compared with the prior art:

[0035] 1. The present invention provides a method for evaluating the driving safety of a vehicle-bridge system. By solving vibration models including a random vibration model and a deterministic vibration model, the mean and standard deviation of wheel-rail forces are obtained. Then, the driving safety index and its probability distribution function and density function are calculated. Finally, by setting a confidence level, the corresponding thresholds on the probability distribution function and density function are obtained for the evaluation of driving safety. It can fully consider the randomness of external loads such as track irregularities, so that the probability distribution function and density function of the present invention match the random dynamic theory better than the prior art, and can more accurately and objectively evaluate the running safety of trains.

[0036] 2. The present invention provides a system for evaluating the driving safety of a vehicle-bridge system, which can execute the method for evaluating the driving safety of a vehicle-bridge system of the present invention by a processor executing instructions in a memory, and can accurately and objectively evaluate the running safety of trains, thereby providing an effective early warning mechanism for the running safety of trains on railway bridges. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a schematic flowchart of a method for evaluating the driving safety of a vehicle-bridge system in Embodiment 1;

[0038] Figure 2 is a schematic side view structure diagram of a train model of a method for evaluating the driving safety of a vehicle-bridge system in Embodiment 1;

[0039] Figure 3 is a schematic front view structure diagram of a train model of a method for evaluating the driving safety of a vehicle-bridge system in Embodiment 1;

[0040] Figure 4 is a schematic top view structure diagram of a train model of a method for evaluating the driving safety of a vehicle-bridge system in Embodiment 1;

[0041] Figure 5 is a schematic diagram of the calculation result of the standard deviation of the vertical wheel-rail force of a method for evaluating the driving safety of a vehicle-bridge system in Embodiment 1;

[0042] Figure 6 is a schematic diagram of the calculation result of the standard deviation of the lateral wheel-rail force of a method for evaluating the driving safety of a vehicle-bridge system in Embodiment 1;

[0043] Figure 7 is a schematic diagram of the calculation result of the mean value of the vertical wheel-rail force of a method for evaluating the driving safety of a vehicle-bridge system in Embodiment 1;

[0044] Figure 8It is a schematic diagram of the calculation result of the mean value of the lateral wheel-rail force of a vehicle-bridge system driving safety evaluation method in Embodiment 1;

[0045] Figure 9 It is a schematic diagram of the calculation result of the probability distribution function of the axle lateral force of a vehicle-bridge system driving safety evaluation method in Embodiment 1;

[0046] Figure 10 It is a schematic diagram of the calculation result of the probability distribution function of the wheel load reduction rate of a vehicle-bridge system driving safety evaluation method in Embodiment 1;

[0047] Figure 11 It is a schematic diagram of the calculation result of the density function of the derailment coefficient of a vehicle-bridge system driving safety evaluation method in Embodiment 1;

[0048] Figure 12 It is a schematic diagram of the threshold value of the axle lateral force of a vehicle-bridge system driving safety evaluation method in Embodiment 1;

[0049] Figure 13 It is a schematic diagram of the threshold value of the wheel load reduction rate of a vehicle-bridge system driving safety evaluation method in Embodiment 1;

[0050] Figure 14 It is a schematic diagram of the threshold value of the derailment coefficient of a vehicle-bridge system driving safety evaluation method in Embodiment 1. Detailed implementation manners

[0051] The present invention will be further described in detail below in conjunction with test examples and specific implementation manners. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments. Any technology implemented based on the content of the present invention belongs to the scope of the present invention.

[0052] Embodiment 1

[0053] Taking a certain suspension bridge as an example, the span of the suspension bridge is about 860 meters, the designed speed is 350 km / h, and the height of the bridge tower is about 270 meters; the train uses an 8-car CRH3 EMU train; as Figure 1 shown is a vehicle-bridge system driving safety evaluation method of this embodiment, including the following steps:

[0054] S1. Establish a train-bridge-rail integrated subsystem through multi-body dynamics theory.

[0055] In an optional implementation manner, the establishment of the train-bridge-rail integrated subsystem can adopt the following steps:

[0056] S1A. Establish a train model by using the rigid body dynamics method; the train model is as Figures 2 to 4 shown.

[0057] S1B. Establish a bridge model using the formation superposition method. Taking a super-large suspension bridge as an example, for a suspension bridge, plate elements can be used to simulate the bridge deck, tensile elements can be used to simulate the main cable and suspenders, and beam elements can be used to simulate structural components such as longitudinal beams, cross beams, upper and lower chord rods, and web members.

[0058] S1C. Couple the train model and the bridge model through the linear wheel-rail contact relationship to obtain the train-bridge-rail integrated subsystem.

[0059] It should be noted that in the above embodiments, S1A and S1B have no sequence. It can be either S1A first or S1B first.

[0060] In the above embodiments, the train-bridge-rail integrated subsystem includes the following expressions:

[0061]

[0062] In the formula, M v represents the mass matrix of the train; C v represents the resistance matrix of the train; K v represents the stiffness matrix of the train; F v represents the external load vector acting on the train; ξ b represents the frequency matrix of the bridge; Ω b represents the damping matrix of the bridge; represents the transposed matrix of the modal matrix of the bridge; F b represents the external load vector acting on the bridge; X v represents the dynamic response of the train. Correspondingly, and are the first derivative and the second derivative of X v respectively; X b represents the dynamic response of the bridge. Correspondingly, and are the first derivative and the second derivative of X b respectively.

[0063] S2. Establish a vibration model of the train-bridge-rail integrated subsystem based on the virtual excitation method and the deterministic time history method. Among them, the deterministic time history method means that it is assumed that the input excitation is known and determined, without considering uncertainty or randomness; the vibration model includes a random vibration model and a deterministic vibration model.

[0064] S3. Solve the vibration model to obtain the mean value and standard deviation of the wheel-rail force. The wheel-rail force includes the lateral wheel-rail force and the vertical wheel-rail force. That is, in this step, the mean value and standard deviation of the lateral wheel-rail force, and the mean value and standard deviation of the vertical wheel-rail force can be obtained. The calculation results are as Figures 5 to 8 shown.

[0065] In an alternative embodiment, in step S3, a full-process iterative method is used to solve the vibration model, and convergence is calculated when the difference in wheel-rail forces between two adjacent iterations lies within a predetermined range.

[0066] In an alternative embodiment, the predetermined range is less than or equal to 100 N.

[0067] S4. According to the mean and standard deviation of the wheel-rail forces, calculate the train operation safety index through a standardized calculation method. The train operation safety index includes the mean and standard deviation of the wheel load reduction rate, the mean and standard deviation of the axle lateral force, and the derailment coefficient (the derailment coefficient is the ratio of the lateral wheel-rail force to the vertical wheel-rail force, which is the ratio of two random processes, so there is no mean and standard deviation). The specific standardized calculation method can refer to the "Code for Design of High-Speed Railways" (TB10621-2014).

[0068] In an alternative embodiment, the train operation safety index includes the following expressions:

[0069]

[0070] u LF =2u Q ,σ LF =2σ Q

[0071] In the formula, u UL represents the mean of the wheel load reduction rate; u P represents the mean of the vertical wheel-rail force; P 0 represents the static axle load; σ UL represents the standard deviation of the wheel load reduction rate; σ P represents the standard deviation of the vertical wheel-rail force; u LF represents the mean of the axle lateral force; u Q represents the mean of the lateral wheel-rail force; σ LF represents the standard deviation of the axle lateral force; σ Q represents the standard deviation of the lateral wheel-rail force.

[0072] S5. According to the train operation safety index and probability theory, establish the probability distribution function and density function of the train operation safety index.

[0073] In an alternative embodiment, the probability distribution function and density function include the following expressions:

[0074] F UL (x)=Φ(x,u UL ,σ UL )

[0075] F LF (x)=Φ(x,u LF ,σ LF )

[0076]

[0077] In the formula, F UL (x) represents the probability distribution function of the wheel load reduction rate; F LF (x) represents the probability distribution function of the lateral force of the wheel axle; f DR (x) represents the density function of the derailment coefficient; x represents the integration variable; Φ represents the standard normal distribution function; represents following the standard normal distribution function; e represents the natural constant; π represents the pi.

[0078] The calculation results of the probability distribution function and the density function are as Figures 9 to 11 shown, and it can be seen that the probability distribution function and the density function calculated in this embodiment can well fit the calculated time-domain samples.

[0079] S6. Set the confidence level, and obtain the safety threshold corresponding to the confidence level on the probability distribution function and the density function; that is, on the probability distribution function of the lateral force of the wheel axle, the probability distribution function of the wheel load reduction rate, and the density function of the derailment coefficient, respectively obtain the corresponding lateral force threshold of the wheel axle, the wheel load reduction rate threshold, and the derailment coefficient threshold through the confidence level.

[0080] In an optional implementation manner, since there is a direction for the train operation safety index, the train operation safety index may be negative; therefore, the absolute value can be taken for the safety threshold for judgment to exclude the influence of negative values on the evaluation.

[0081] In an optional implementation manner, obtain the upper and lower limits of the confidence level according to the 3sigma rule. For example, the upper limit of the confidence level is less than or equal to 99.87% and greater than or equal to 99.5%, and the lower limit of the confidence level is greater than or equal to 0.13% and less than or equal to 0.5%. When the upper limit of the confidence level is 99.87% and the lower limit of the confidence level is 0.13%, the calculation results are as Figures 12 to 14 shown, Figures 12 to 14 The black dots in it successively represent the calculation samples of the lateral force of the wheel axle, the wheel load reduction rate, and the derailment coefficient, while the red line and the blue line respectively represent the upper and lower limits of the confidence level. Determine the maximum absolute values of the lateral force of the wheel axle, the wheel load reduction rate, and the derailment coefficient through the upper and lower limits of the confidence level. For example, obtain the maximum values of the lateral force of the wheel axle, the wheel load reduction rate, and the derailment coefficient respectively through the upper limit of the confidence level, and obtain the minimum values (the maximum value of negative numbers) of the lateral force of the wheel axle, the wheel load reduction rate, and the derailment coefficient respectively through the lower limit of the confidence level. Compare the absolute values of the maximum and minimum values to obtain that the lateral force threshold of the wheel axle, the wheel load reduction rate threshold, and the derailment coefficient threshold are 32.454 KN, 0.5667, and 0.211 respectively.

[0082] S7. Compare the safety threshold with the high-speed rail specification limits to evaluate the train's running safety. For example, the limit of the axle lateral force is 10 KN + static axle load / 3, the limit of the wheel load reduction rate is 0.6, and the limit value of the derailment coefficient is 0.8. From the comparison results, it can be seen that it is safe for the train to run at 350 km / h on the suspension bridge.

[0083] Embodiment 2

[0084] A judging system for the running safety of a car-body and axle system includes a memory and a processor; at least one instruction is stored in the memory; the processor is communicatively connected to the memory, and the processor can read the instruction and execute a judging method for the running safety of a car-body and axle system in Embodiment 1. The communicative connection between the processor and the memory includes but is not limited to fiber optic connection, wireless local area network connection or Bluetooth connection.

[0085] In an optional implementation manner, the memory includes but is not limited to a mechanical hard disk, a solid-state drive, an optical disc or a magnetic tape.

[0086] In an optional implementation manner, the processor includes but is not limited to a central processing unit (CPU) or a graphics processing unit (GPU).

[0087] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating the driving safety of a vehicle-bridge system, characterized in that: The steps include: S1. Establish the train-bridge-track integrated subsystem through multi-body dynamics theory; S2. A vibration model of the train-bridge-rail integrated subsystem is established based on a virtual excitation method and a deterministic time history method. The vibration model includes a random vibration model and a deterministic vibration model. S3, solve the vibration model to obtain the mean and standard deviation of the wheel-rail force, which includes the lateral wheel-rail force and the vertical wheel-rail force; S4. Calculate the driving safety index based on the mean and standard deviation of the wheel-rail force. The driving safety index includes the mean and standard deviation of the wheel load reduction rate, the mean and standard deviation of the wheel axle lateral force and the derailment coefficient; S5. establishing a probability distribution function and a density function of the driving safety index according to the driving safety index; S6. Set the confidence level, and obtain a security threshold corresponding to the confidence level on the probability distribution function and the density function; S7. Evaluate the train's running safety based on the safety threshold.

2. A method for evaluating driving safety of a vehicle bridge system according to claim 1, characterized in that: Driving safety indicators include the following expressions: you LF =2u Q ,s LF =2σ Q In the formula, u UL Represents the mean value of wheel load reduction rate; u P represents the mean value of the vertical wheel-rail force; P0 represents the static axle weight; σ UL Represents the standard deviation of wheel load reduction rate; σ P represents the standard deviation of the vertical wheel-rail force; u LF Represents the mean value of the lateral force on the wheel axle; u Q represents the mean value of the lateral wheel-rail force; σ LF Represents the standard deviation of the lateral force on the wheel axle; σ Q Represents the standard deviation of the lateral wheel-rail force.

3. A method for evaluating driving safety of a vehicle bridge system according to claim 2, characterized in that: The probability distribution function and density function include the following expressions: F UL (x)=Φ(x,u UL ,s UL ) F LF (x)=Φ(x,u LF ,s LF ) In the formula, F UL (x) represents the probability distribution function of wheel load reduction rate; F LF (x) represents the probability distribution function of the lateral force on the wheel axle; f DR (x) represents the density function of the derailment coefficient; x represents the integral variable; Φ represents the standard normal distribution function; represents the standard normal distribution function; e represents a natural constant; π represents the circumference of a circle.

4. A method for evaluating driving safety of a vehicle bridge system according to any one of claims 1 to 3, characterized in that: The establishment of the train-bridge-rail integrated subsystem includes the following steps: The rigid body dynamics method is used to establish the train model; the formation superposition method is used to establish the bridge model; the train model and the bridge model are coupled into a train-bridge-rail integrated subsystem through wheel-rail force.

5. A method for evaluating driving safety of a vehicle bridge system according to claim 4, characterized in that: The train-bridge-track integrated subsystem includes the following expressions: Where M v Represents the mass matrix of the train; C v Represents the resistance matrix of the train; K v represents the stiffness matrix of the train; F v Represents the external load vector acting on the train; ξ b represents the frequency matrix of the bridge; Ω b represents the damping matrix of the bridge; represents the transposed matrix of the bridge's modal matrix; F b Represents the external load vector of the bridge; X v Represents the dynamic response of the train; X b Represents the dynamic response of the bridge.

6. A method for evaluating driving safety of a vehicle bridge system according to any one of claims 1 to 3, characterized in that: In step S3, the vibration model is solved by a full-process iteration method, and the calculation converges when the difference between the wheel-rail forces in two adjacent iterations is within a predetermined interval.

7. A method for evaluating driving safety of a vehicle bridge system according to claim 6, characterized in that: The predetermined interval is less than or equal to 100N.

8. A method for evaluating driving safety of a vehicle bridge system according to any one of claims 1 to 3, characterized in that: Get the upper and lower confidence limits based on the 3 sigma rule.

9. A method for evaluating driving safety of a vehicle bridge system according to any one of claims 1 to 3, characterized in that: The safety threshold is an absolute value.

10. A vehicle-bridge system driving safety evaluation system, characterized in that: include: A memory, wherein at least one instruction is stored in the memory; A processor is communicatively connected to the memory, and the processor is capable of reading the instruction and executing a method for evaluating driving safety of a vehicle bridge system as claimed in any one of claims 1 to 9.

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