A test method, device and storage medium for a maglev train

Abstract

This application discloses a testing method, apparatus, and storage medium for a maglev train, relating to the field of maglev train testing. This method first obtains an initial drive displacement signal and drives a vibration table, which simulates a bridge, based on the initial drive displacement signal. An actual displacement signal from the vibration table is then obtained. Convergence of the test results is determined based on the actual displacement signal and the initial drive displacement signal. This method can simulate a bridge model, facilitating analysis and testing of the operating conditions of a maglev train traveling on a bridge.

Description

Technical Field

[0001] The present application relates to the field of maglev train testing, and in particular to a maglev train testing method, device, and storage medium. Background Art

[0002] Hybrid testing is a method for studying structural vibration or dynamic performance. In the hybrid test of a maglev vehicle, the mechanism of the maglev vehicle as a physical test substructure is still unclear. To ensure the stability and accuracy of the test, it is very important to provide a test method to realize the actual test of the maglev vehicle driving on the bridge, so as to provide basic data reference for subsequent tests. Summary of the Invention

[0003] The purpose of this application is to provide a test method, device and storage medium for a maglev train, which can perform simulation tests on a bridge model to facilitate analysis and testing of the working conditions of a maglev train running on a bridge.

[0004] To solve the above technical problems, the present application provides a test method for a maglev train, comprising:

[0005] obtaining an initial driving displacement signal, and driving the vibration table according to the initial driving displacement signal, wherein the vibration table is used to simulate a bridge;

[0006] Acquiring an actual displacement signal of the vibration table;

[0007] Whether the test result converges is determined according to the actual displacement signal and the initial drive displacement signal.

[0008] Preferably, obtaining the initial driving displacement signal includes:

[0009] An initial driving displacement signal output by a simulation model of a test piece is obtained, wherein the test piece is used to simulate a maglev train.

[0010] Preferably, driving the vibration table according to the initial driving displacement signal includes:

[0011] ICS loading is performed on the vibration table according to the initial driving displacement signal.

[0012] Preferably, obtaining the actual displacement signal of the vibration table includes:

[0013] Obtaining the electromagnetic force exerted on the vibration table;

[0014] The actual displacement signal of the vibration table is calculated according to the electromagnetic force and the bridge numerical model.

[0015] Preferably, determining whether the test result converges according to the actual displacement signal and the initial drive displacement signal includes:

[0016] Determining whether a difference between the actual displacement signal and the initial drive displacement signal is within a preset range;

[0017] If it is within the preset range, the test result is determined to be convergent; otherwise, the test result is determined to be divergent.

[0018] Preferably, determining whether the difference between the actual displacement signal and the initial drive displacement signal is within a preset range includes:

[0019] Calculating whether a root mean square error between the actual displacement signal and the initial drive displacement signal is less than a limit;

[0020] If it is less than, it is determined to be within the preset range; otherwise, it is determined to be not within the preset range.

[0021] Preferably, before obtaining the initial driving displacement signal, the method further includes:

[0022] determining whether the vibration table is in a first ready state;

[0023] If yes, proceed to the step of obtaining the initial driving displacement signal.

[0024] Preferably, the first ready state includes the vibration table being in a standby state, a high pressure state, a state with no magnetic buoyancy input, a state with no speed input, a state with no external input, or a state with no alarm, or a combination of the two.

[0025] Preferably, before obtaining the initial driving displacement signal, the method further includes:

[0026] determining whether the test piece is in a second ready state, the test piece being used to simulate a maglev train;

[0027] If yes, proceed to the step of obtaining the initial driving displacement signal.

[0028] Preferably, the second ready state includes one or more combinations of the test piece being in a powered-on state, an electrostatically suspended and stable state, a normal train data state, a vibration table being in a monitoring state, and a state with no speed input.

[0029] Preferably, after determining that the test result does not converge, the method further includes:

[0030] Iteratively correcting the initial driving displacement signal according to the test result, the initial driving displacement signal, and the actual displacement signal to obtain an initial driving displacement signal for the next cycle;

[0031] The step of driving the vibration table is re-entered according to the initial driving displacement signal of the next cycle until the test results converge.

[0032] Preferably, after determining that the test result has converged, the method further includes:

[0033] End this experiment.

[0034] Preferably, iteratively correcting the initial driving displacement signal according to the test result, the initial driving displacement signal, and the actual displacement signal to obtain the initial driving displacement signal of the next cycle includes:

[0035] The initial driving displacement signal is iteratively corrected using a model identification iterative algorithm or a fixed point iterative algorithm according to the test result, the initial driving displacement signal, and the actual displacement signal to obtain an initial driving displacement signal for the next cycle.

[0036] To solve the above technical problems, the present application also provides a test device for a maglev train, comprising:

[0037] Memory for storing computer programs;

[0038] The processor is used to implement the steps of the above-mentioned maglev train testing method when storing the computer program.

[0039] In order to solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the maglev train testing method as described above are implemented.

[0040] This application provides a testing method, device, and storage medium for a maglev train, relating to the field of maglev train testing. This method first obtains an initial drive displacement signal and drives a vibration table based on the initial drive displacement signal, where the vibration table simulates a bridge. An actual displacement signal from the vibration table is then obtained. Convergence of the test results is determined based on the actual displacement signal and the initial drive displacement signal. This method can simulate a bridge model, facilitating analysis and testing of the operating conditions of a maglev train traveling on a bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0042] Figure 1 A schematic flow chart of a test method for a maglev train provided in this application;

[0043] Figure 2 A schematic diagram of a model identification iterative algorithm provided in this application;

[0044] Figure 3 A schematic diagram of a fixed point iteration algorithm provided in this application;

[0045] Figure 4 This is a structural block diagram of a test device for a maglev train provided in this application. DETAILED DESCRIPTION

[0046] The core of this application is to provide a test method, device and storage medium for a maglev train, which can perform simulation tests on a bridge model to facilitate analysis and testing of the working conditions of a maglev train running on a bridge.

[0047] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0048] Please refer to Figure 1 , Figure 1 This is a flow chart of a test method for a maglev train provided in this application, the method comprising:

[0049] S11: obtaining an initial driving displacement signal, and driving a vibration table according to the initial driving displacement signal, where the vibration table is used to simulate a bridge;

[0050] Specifically, in the present application, a model-vibration table for simulating a bridge and a model-test piece for simulating a maglev train are pre-constructed.

[0051] As a preferred embodiment, before obtaining the initial driving displacement signal, the method further includes:

[0052] Determine whether the vibration table is in the first ready state;

[0053] If yes, proceed to the step of obtaining the initial driving displacement signal.

[0054] Specifically, before the test begins, it should be ensured that the model used to simulate the bridge - the shaking table is in a ready state. Only when the shaking table is in the first ready state can the test begin.

[0055] As a preferred embodiment, the first ready state includes the vibration table being in a standby state, a high pressure state, a state with no magnetic buoyancy input, a state with no speed input, a state with no external input, or a state with no alarm, or a combination of the two.

[0056] In a specific embodiment, the first ready state must simultaneously satisfy the following conditions: the vibration platform is in a standby state, a high-pressure state, a no-magnetic-buoyancy-force input state, a no-speed-input state, a no-external-input state, and a no-alarm state. The standby state of the vibration platform means that the vibration platform has received a standby instruction, such as when the vibration platform is in position 0; the high-pressure state means that the hydraulic oil and support force in the system where the vibration platform is located are in a high-pressure state; the no-magnetic-buoyancy-force input state means that the maglev train (test piece) does not generate electromagnetic force on the vibration platform; the no-speed-input state means that the maglev train (test piece) is stopped and not in motion; the no-external-input state means that there is no other input to control or affect the vibration platform; and the no-alarm state means that the vibration platform is in a safe state with no alarms (such as no overcurrent or overvoltage).

[0057] In a specific embodiment, to ensure the accuracy and reliability of the test, the vibration table is determined to meet the first ready state only when all the above conditions are met. Of course, the conditions included in the first ready state can be adjusted according to actual conditions, and this application does not make any special restrictions here.

[0058] As a preferred embodiment, before obtaining the initial driving displacement signal, the method further includes:

[0059] determining whether the test piece is in a second ready state, the test piece being used to simulate a maglev train;

[0060] If yes, proceed to the step of obtaining the initial driving displacement signal.

[0061] Specifically, before the test begins, it should first be ensured that the model-test piece used to simulate the bridge is in a ready state. Only when the test piece is in the second ready state can the test begin.

[0062] As a preferred embodiment, the second ready state includes one or more combinations of the test piece being in a powered-on state, an electrostatically suspended and stable state, a normal train data state, a vibration table being in a monitoring state, and a state with no speed input.

[0063] In one specific embodiment, the second ready state must simultaneously satisfy the following conditions: the test piece is powered on, in electrostatic suspension and stable state, the train data is normal, the vibration table is in a monitoring state, and there is no speed input. The power-on state of the test piece means that the system in which the test piece is located is connected to a power source; the electrostatic suspension and stable state means that the maglev train (test piece) generates electromagnetic force after power is applied, thereby levitating, and the levitation state is stable (for example, when the distance between the train and the vibration table is stable at a preset distance); the normal train data state means that the data generated by the operation of the maglev train (test piece) is within a normal range; the monitoring state of the vibration table means that the system in which the vibration table is located is in a monitorable state; and the no speed input state means that the maglev train (test piece) has not received any speed command and has not generated any travel speed.

[0064] In one embodiment, to ensure the accuracy and reliability of the test, the test piece is determined to have met the second ready state only when all of the above conditions are met. Of course, the conditions included in the second ready state can be adjusted according to actual conditions, and this application does not impose any specific restrictions here.

[0065] When the test piece and the vibration table are ready, the test can be started. When the test starts, an initial driving displacement signal is first obtained so that the vibration table can be driven according to the initial driving displacement signal.

[0066] As a preferred embodiment, obtaining the initial driving displacement signal includes:

[0067] An initial driving displacement signal output by a simulation model of a test piece is obtained, where the test piece is used to simulate a maglev train.

[0068] This embodiment aims to limit the source of the initial driving displacement signal, which can be but is not limited to being output from the simulation model of the test piece. For details on how the simulation module calculates the initial driving displacement signal, please refer to the prior art and this application will not elaborate on it here.

[0069] As a preferred embodiment, driving the vibration table according to the initial driving displacement signal includes:

[0070] The vibration table is loaded with ICS (Intelligent Control System) according to the initial driving displacement signal.

[0071] This embodiment aims to define a specific implementation method for driving the vibration table based on the initial drive displacement signal. Specifically, ICS loading is performed on the vibration table based on the initial drive displacement signal to cause the vibration table to generate a corresponding displacement. ICS loading is a multi-iteration process that allows the actuator itself to reduce the error generated. Compared with the direct loading process, the method in this application has a smaller error and can improve the accuracy of the test.

[0072] S12: Obtain the actual displacement signal of the vibration table;

[0073] After the vibration table is driven as described above, it is necessary to obtain an actual displacement signal of the vibration table in order to compare the actual displacement signal with the initial driving displacement signal to obtain the experimental result.

[0074] As a preferred embodiment, obtaining the actual displacement signal of the vibration table includes:

[0075] Obtain the electromagnetic force acting on the vibration table;

[0076] The actual displacement signal of the shaking table is calculated based on the electromagnetic force and the bridge numerical model.

[0077] Specifically, when the vibration table is driven or loaded, the actuator is usually controlled to generate electromagnetic force to act on the vibration table, so as to cause the vibration table to deform.

[0078] Therefore, the method of obtaining the actual displacement signal of the vibration table in this application is: it can be but not limited to obtaining the electromagnetic force of the vibration table through a force sensor, and applying this electromagnetic force to the numerical model of the bridge to calculate the actual displacement signal of the bridge (vibration table), thereby simulating the effect of the maglev train on the bridge when passing through the bridge.

[0079] S13: Determine whether the test result converges based on the actual displacement signal and the initial driving displacement signal.

[0080] Specifically, after the actual displacement signal and the initial driving displacement signal are known as described above, in this application, it is necessary to determine whether the test results converge. If converged, it means that the test is completed and the test can be ended. Otherwise, further verification is required through experiments.

[0081] As a preferred embodiment, determining whether the test result converges according to the actual displacement signal and the initial driving displacement signal includes:

[0082] Determine whether the difference between the actual displacement signal and the initial drive displacement signal is within a preset range;

[0083] If it is within the preset range, the test result is judged to be convergent, otherwise the test result is judged to be divergent.

[0084] Specifically, the above-mentioned method for determining whether the test results have converged is to determine whether the difference between the actual displacement signal and the initial drive displacement signal is large or small, that is, whether it is within a preset range. If the difference between the two signals is small, the drive accuracy in this test is determined to be good, that is, the test results have converged; otherwise, the drive accuracy in this test is determined to be poor, and when the vibration table is driven according to the initial drive displacement signal, the vibration table cannot be deformed to the target displacement (the displacement corresponding to the initial drive displacement signal). In other words, the test results are not converged, and further testing is required to correct this test.

[0085] As a preferred embodiment, after determining that the test results have converged, the method further includes:

[0086] End this experiment.

[0087] As a preferred embodiment, determining whether the difference between the actual displacement signal and the initial driving displacement signal is within a preset range includes:

[0088] Calculate whether the root mean square error of the actual displacement signal and the initial drive displacement signal is less than the limit;

[0089] If it is less than, it is determined to be within the preset range; otherwise, it is determined to be not within the preset range.

[0090] Specifically, the specific method for determining whether the difference between the actual displacement signal and the initial drive displacement signal is within a preset range is: calculating the root mean square of the actual displacement signal and the initial drive displacement signal, and determining whether the error of this root mean square is less than the limit value; if so, the test result is determined to be convergent; otherwise, the test result is determined to be divergent.

[0091] As a preferred embodiment, after determining that the test result does not converge, the method further includes:

[0092] Iteratively correct the initial driving displacement signal according to the test results, the initial driving displacement signal, and the actual displacement signal to obtain the initial driving displacement signal of the next cycle;

[0093] The step of driving the vibration table is re-entered according to the initial driving displacement signal of the next cycle until the test results converge.

[0094] Furthermore, after the above-mentioned test results are determined to be divergent, the processing method of the present application is: iteratively correct the initial driving displacement signal, and re-enter the test based on the corrected (i.e., the next cycle) initial driving displacement signal until the test results converge.

[0095] As a preferred embodiment, the initial driving displacement signal is iteratively corrected according to the test results, the initial driving displacement signal, and the actual displacement signal to obtain the initial driving displacement signal of the next cycle, including:

[0096] According to the test results, the initial driving displacement signal and the actual displacement signal, the initial driving displacement signal is iteratively corrected using a model identification iterative algorithm or a fixed point iterative algorithm to obtain the initial driving displacement signal of the next cycle.

[0097] Specifically, this embodiment aims to provide a specific embodiment for iteratively correcting the initial driving displacement signal, which can adopt a model identification iterative algorithm or a fixed point iterative algorithm, etc. Figure 2 and Figure 2 , Figure 2 A schematic diagram of a model identification iterative algorithm provided in this application, Figure 3 A schematic diagram of a fixed point iteration algorithm provided in this application.

[0098] The model iteration algorithm is: using the frequency response function (FRF) to identify the relationship between the system input and the error, and then using the inverse transform of the FRF to obtain the correction value for the next iteration.

[0099] Define the relationship between n inputs and n outputs in the system as follows:

[0100] Y=H·U

[0101] in:

[0102]

[0103] Y=[y1,…,y i ,…,y n ]′ is the error response of the system, H is the matrix of the system frequency response transfer function, U=[u1,…,u j ,…,u n ]′ is the input matrix of the system. When doing system identification, in order to evaluate and solve the j-th column in the frequency response function matrix H, it is necessary to measure the j-th driving signal u j Get all responses Y while ensuring that other input signals are zero, and repeat this step n times until every column in H is determined.

[0104] After obtaining the transfer function H between the error and the system input, it is necessary to perform an inverse transformation on the frequency response function H to obtain the matrix G:

[0105]

[0106] After the inverse transform operation, the conjugate frequency response matrix needs to be subjected to information elimination in a specific frequency range to remove the frequency range of no interest to ensure the normal response of the system.

[0107] G=filter(G)

[0108] Applying the above model identification algorithm to the maglev axle system, the calculated inverse transfer function matrix can represent the relationship between the drive signal and the response error. Therefore, in each iteration, the correction amount of the drive signal can be obtained based on the response error:

[0109] U K′ =G·Y k

[0110] Among them U K′ is the correction amount input in the kth iteration, Y k is the error between the numerical bridge model response and the driving file in the kth iteration. The calculated system input correction is used to correct the driving signal:

[0111] U k+1 =U k +ω·U′,k=0,1,2,…

[0112] Among them, U k+1 is the driving signal in the k+1th iteration, U k is the driving signal in the kth iteration, and ω is the iterative gain of the system input signal correction. This process is repeated until the response results of two adjacent iterations meet the error requirements, that is, until the test results converge.

[0113] The fixed point iteration algorithm uses the response of the numerical bridge model in each iteration as the driving signal for the next iteration until the response error is within the allowable range. The specific implementation process is as follows:

[0114] The maglev axle system is expressed by the following formula:

[0115] y=H(u)

[0116] Where y is the response of the numerical bridge, H is the transfer function of the maglev bridge system, and u is the drive signal of the system. The objective of the offline iterative hybrid test can be expressed as follows:

[0117] error=uy=uH(u)=0

[0118] Considering only mathematical expressions, the ultimate goal of offline hybrid testing can be converted into finding the solution to the above equation, that is, finding the only solution u that makes the above equation valid.

[0119] u=H(u)

[0120] Take any initial value u0 and substitute it into the right side of the above equation to get

[0121] u1=H(u0)

[0122] Repeating this process yields:

[0123] u2=H(u1) ...

[0125] u k+1 =H(u k ), k = 0, 1, 2, ...

[0126] In the above formula, u k is the driving signal of step k, u k+1 is the response of the k-th step numerical bridge and the driving signal of the k+1-th step. * , so that the iterative sequence {u k}satisfy

[0127] lim k→∞ u k =u *

[0128] The iterative method is said to converge, u * To find the solution of the equation, otherwise it is called divergence.

[0129] In summary, the test method in the present application can perform simulation tests on a bridge model, so as to analyze and test the working conditions of a maglev train running on the bridge.

[0130] Please refer to Figure 4 , Figure 4 This is a structural block diagram of a test device for a maglev train provided in this application, which includes:

[0131] Memory 41, for storing computer programs;

[0132] The processor 42 is configured to implement the steps of the above-mentioned maglev train test method when storing the computer program. For the introduction of the maglev train test device, please refer to the above-mentioned embodiment, and this application will not elaborate on it here.

[0133] To address the above technical issues, this application further provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the steps of the aforementioned maglev train testing method. For an introduction to the computer-readable storage medium, please refer to the above embodiments, and this application will not elaborate further here.

[0134] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0135] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A test method for a maglev train, characterized in that: include: obtaining an initial driving displacement signal output by a simulation model of a test piece, wherein the test piece is used to simulate a maglev train; driving a vibration table according to the initial driving displacement signal, wherein the vibration table is used to simulate a bridge; Acquiring an actual displacement signal of the vibration table; Determining whether a difference between the actual displacement signal and the initial drive displacement signal is within a preset range; If it is within the preset range, the test result is determined to be converged and the test is terminated; Otherwise, the test result is determined to be divergent, and the initial drive displacement signal is iteratively corrected according to the test result, the initial drive displacement signal, and the actual displacement signal to obtain the initial drive displacement signal of the next cycle; and the step of driving the vibration table is re-entered according to the initial drive displacement signal of the next cycle until the test result converges.

2. The maglev train testing method according to claim 1, wherein: Driving the vibration table according to the initial driving displacement signal includes: ICS loading is performed on the vibration table according to the initial driving displacement signal.

3. The maglev train testing method according to claim 2, wherein: Obtaining an actual displacement signal of the vibration table, including: Obtaining the electromagnetic force exerted on the vibration table; The actual displacement signal of the vibration table is calculated according to the electromagnetic force and the bridge numerical model.

4. The maglev train testing method according to claim 1, wherein: Determining whether a difference between the actual displacement signal and the initial drive displacement signal is within a preset range includes: Calculating whether a root mean square error between the actual displacement signal and the initial drive displacement signal is less than a limit; If it is less than, it is determined to be within the preset range; otherwise, it is determined to be not within the preset range.

5. The maglev train testing method according to claim 1, wherein: Before obtaining the initial driving displacement signal, the method further includes: Determining whether the vibration table is in a first ready state; the first ready state includes the vibration table being in a standby state, a high pressure state, a no magnetic buoyancy input state, a no speed input state, a no external input state, and a no alarm state, or a combination thereof; If yes, proceed to the step of obtaining the initial driving displacement signal.

6. The maglev train testing method according to claim 1, wherein: Before obtaining the initial driving displacement signal, the method further includes: determining whether the test piece is in a second ready state, the test piece being used to simulate a maglev train; the second ready state comprising one or more of the following: the test piece being powered on, being in an electrostatically suspended and stable state, having normal train data, having a vibration table in a monitoring state, and having no speed input; If yes, proceed to the step of obtaining the initial driving displacement signal.

7. The maglev train testing method according to any one of claims 1 to 6, characterized in that: Iteratively correcting the initial driving displacement signal according to the test result, the initial driving displacement signal, and the actual displacement signal to obtain an initial driving displacement signal for the next cycle includes: The initial driving displacement signal is iteratively corrected using a model identification iterative algorithm or a fixed point iterative algorithm according to the test result, the initial driving displacement signal, and the actual displacement signal to obtain an initial driving displacement signal for the next cycle.

8. A test device for a maglev train, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the maglev train testing method according to any one of claims 1 to 7 when storing a computer program.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the maglev train testing method according to any one of claims 1 to 7 are implemented.

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