Railway track damage detection method based on dogvessel squirt algorithm and temperature inversion theory

Abstract

The invention provides a railway track damage detection method based on a doliolaria algorithm and a temperature inversion theory, and the method comprises the steps: S1, building a track inversion model, and carrying out the analysis of a discrete track model through a finite difference method; s2, performing simulation calculation on the track model, and obtaining temperature distribution of the track two-dimensional I-shaped flat plate by using a finite difference method; s3, introducing a doliolaria algorithm, taking the orbit internal temperature data obtained by solving the forward problem as the input of an inverse problem, and performing inversion identification on the orbit internal temperature information; and S4, accurately detecting the damage condition of the track by using the internal temperature data obtained by inversion. The railway track damage detection method based on the dogvessel squirt algorithm and the temperature inversion theory has the beneficial effects that nondestructive detection of the internal temperature of the track is achieved based on the temperature inversion theory, and then the purpose of accurately evaluating the track damage is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of detection technology, and in particular relates to a railway track damage detection method based on a salp algorithm and temperature inversion theory. Background Art

[0002] Rail transit is the main mode of public transportation, and its track performance quality directly affects personal safety. Track inspection can timely discover defects and hidden dangers in the track, providing strong protection for the safety of transportation. Testing the compressive strength, flexural strength, and impermeability of railway tracks can provide a scientific basis for engineering design and construction.

[0003] As society pays more and more attention to sustainable development, the rail industry is also actively seeking a green and environmentally friendly development path. As an important part of the industry, railway track testing is of great significance to promoting sustainable development. Through testing, the environmental impact of railway tracks can be evaluated, the production process of railway tracks can be optimized, environmental pollution can be reduced, and the green development of the industry can be promoted.

[0004] With the continuous advancement of science and technology, railway track detection technology is also constantly innovating and developing. The emergence of new detection technologies has brought higher accuracy and efficiency to railway track detection, providing strong support for the technological progress of the industry. At the same time, technological progress has also promoted the innovative development of the railway track detection industry, providing a broader application prospect for railway track detection.

[0005] The development of the railway track inspection industry is inseparable from the formulation and implementation of industry standards and specifications. By formulating unified inspection standards and specifications, the accuracy and reliability of railway track inspection results can be ensured, providing a strong guarantee for the healthy development of the industry. At the same time, the implementation of industry standards and specifications can also promote the standardization and standardization of the railway track inspection industry.

[0006] Railway track testing has a positive impact on economic benefits and social development. Through testing, the quality and safety of the project can be ensured, and economic losses and social impacts caused by quality problems can be avoided. At the same time, railway track testing also provides technical support and services for the industry, promotes the healthy development of the industry, and contributes to the sustained and stable growth of the social economy. In addition, the accuracy and reliability of railway track testing can also enhance the international competitiveness of the industry and promote the industry to the world stage.

[0007] In summary, railway tracks and railway track detection have important significance and value in terms of material foundation, engineering structure safety, quality control requirements, sustainable development promotion, technological progress and innovation, industry standards and specifications, economic benefits and social development. Therefore, we should strengthen the attention and research on railway track detection, promote the continuous progress and development of railway track detection technology, and make greater contributions to the healthy development of the industry and the sustainable development of social economy. However, at present, my country's railway maintenance and maintenance work is mainly based on manual inspection. This detection method relies entirely on line patrol workers to observe through their own eyes. There are disadvantages such as high subjective dependence, large inspection workload, high risk factor of night work, and greater influence of weather. The detection accuracy and efficiency often fail to achieve ideal results. Although high-speed railways use comprehensive inspection vehicles to carry out automatic track inspections, the detection cycle is relatively long and cannot meet the actual needs of on-site inspections. Summary of the invention

[0008] In view of this, the present invention aims to propose a railway track damage detection method based on the salp algorithm and temperature inversion theory to at least solve one problem in the background technology.

[0009] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0010] Railway track damage detection method based on salp algorithm and temperature inversion theory includes:

[0011] S1. Establish an orbit inversion model and use the finite difference method to analyze the discrete orbit model;

[0012] S2. Simulate and calculate the track model, and use the finite difference method to obtain the temperature distribution of the two-dimensional I-shaped plate of the track;

[0013] S3, introduce the Salp algorithm, and use the internal temperature data of the track obtained by solving the forward problem as the input of the inverse problem to invert and identify the temperature information inside the track;

[0014] S4. Use the internal temperature data obtained by inversion to accurately detect the damage of the track.

[0015] Furthermore, in step S1, it is necessary to collect the thermophysical parameters of the track, including thermal conductivity, thermal diffusion coefficient, density, specific heat capacity and heat transfer coefficient.

[0016] Furthermore, in the process of setting the internal parameters of the two-dimensional model in step S2 and introducing the salp algorithm in step S3, simulation calculations are performed by Matlab software.

[0017] Furthermore, in step S2, the Taylor series expansion is used to solve the first-order and second-order derivatives, and a partial differential equation is established for the steady-state heat conduction forward problem model, which is discretized into an explicit finite difference format in combination with boundary conditions.

[0018] Furthermore, in step S3, the objective function constructed is as follows:

[0019]

[0020] in, is the calculated temperature value at the measuring point k, B is the unknown parameter vector to be identified, and the calculated temperature value Solving the heat conduction direct problem model based on the guessed value of B yields: is the temperature measurement value at the measuring point k. When the above objective function J(B) reaches its minimum value, the vector B obtained by solving is the unknown parameter vector.

[0021] Further, in step S3, the step of using the salp algorithm for inversion includes:

[0022] Initialize the population: Generate a population of N×D salps based on the upper and lower bounds of the search space;

[0023] Calculate fitness: evaluate the fitness value of each individual and select the individual with the best fitness as the food location;

[0024] Update the positions of the leader and followers: By setting the convergence factor c1 and the random factors c2, c3, the positions of the leader and followers are dynamically adjusted to make them approach the target area;

[0025] Iterative optimization: Continuously update the fitness value until the termination condition is reached, and finally output the target parameter estimate.

[0026] Furthermore, the present solution discloses an electronic device, comprising a processor and a memory that is communicatively connected to the processor and is used to store executable instructions of the processor, wherein the processor is used to execute the above-mentioned railway track damage detection method based on the Salp Intense algorithm and temperature inversion theory.

[0027] Furthermore, the present solution discloses a server comprising at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor so that the at least one processor executes a railway track damage detection method based on the Salp Intense algorithm and temperature inversion theory.

[0028] Furthermore, the present solution discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a railway track damage detection method based on a Salp Intense algorithm and a temperature inversion theory.

[0029] Compared with the prior art, the railway track damage detection method based on the salp algorithm and temperature inversion theory described in the present invention has the following beneficial effects:

[0030] The railway track damage detection method based on the salp algorithm and temperature inversion theory described in the present invention achieves the purpose of non-destructive detection of the internal temperature of the track and then accurately evaluating the track damage based on the temperature inversion theory. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0032] Figure 1 It is a flowchart of the implementation process of the track damage detection method based on the salp algorithm and the temperature inversion theory of the present invention;

[0033] Figure 2 A schematic diagram of grid division in an experimental example of the present invention;

[0034] Figure 3 This is a flow chart of the present invention for inverting orbital temperature based on the salp algorithm;

[0035] Figure 4 It is a logic block diagram of the iterative algorithm of the experimental example of the present invention;

[0036] Figure 5 A comparison diagram of the track defect detection method in the practice example of the present invention;

[0037] Figure 6 This is a three-dimensional temperature change curve diagram of the experimental example of the present invention. DETAILED DESCRIPTION

[0038] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0039] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0040] A track damage detection method based on the salp algorithm and temperature inversion theory comprises the following steps:

[0041] S1. Establish an orbit inversion model and use the finite difference method to analyze the discrete orbit model;

[0042] S2. The model was simulated and the temperature distribution of the two-dimensional track I-shaped plate was obtained using the finite difference method;

[0043] S3, introduce the Salp Algorithm, use the internal temperature information of the track solved by the forward problem as the input value of the inverse problem, and perform inversion identification on the internal temperature information of the track;

[0044] S4. Use internal temperature data to accurately detect rail track damage.

[0045] Furthermore, the thermophysical parameters of the track collected in step S1 are mainly: thermal conductivity, thermal diffusion coefficient, density, specific heat capacity, and heat transfer coefficient;

[0046] Furthermore, the internal parameters of the two-dimensional model are set in step S2, and the conjugate gradient method and the salp algorithm are introduced in step S3, all of which are simulated using Matlab programming software.

[0047] The temperature of the outer wall of the track is obtained from the temperature field distribution obtained by the unsteady heat conduction forward problem model;

[0048] Constructing an objective function for measuring the temperature and solving the temperature of the outer wall of the track, introducing a salp algorithm for inversion and solving, and obtaining a temperature difference;

[0049] A convergence condition is preset, and when the temperature difference does not meet the convergence condition, the track temperature is iterated until the convergence condition is met, thereby obtaining a track temperature inversion result;

[0050] Furthermore, in step S2,

[0051] The finite difference procedure is as follows:

[0052] Taylor series can be used to establish the representation of first-order and second-order derivatives, which can not only be used as error estimates, but also to establish more accurate formulas. Suppose a continuous function f(x), where x -1 ,x0,x1 are equidistant discrete points, and x1-x0=x0-x -1 =Δx, f(x) represents the function value.

[0053] First, expand the function f(x1)=f(x0+Δx) according to the Taylor series:

[0054]

[0055] In the formula, ο(x) 3 To truncate (x) 3 The following term is the truncation error term. The same principle applies to the function f(x -1 )=f(x0-Δx) and perform Taylor expansion to obtain:

[0056]

[0057] where ο(x) 3 is the corresponding truncation error term in the above formula. When the terms after Δx are truncated, the forward difference and backward difference formulas of the derivative f′(x0) of f(x0) are obtained:

[0058]

[0059] In the formula, f″(x 0,1 ) is the second-order derivative of f(x) somewhere in the interval (x0, x1), f″(x -1,0 ) is in the interval (x -1 ,x0), subtracting formula (2) from formula (3) yields the central difference formula:

[0060]

[0061] The formula has a truncation error term (Δx) 2 (f″′(x -1,0 )+f″′(x 0,1 )) / 3! , where f″′(x -1,0 ) is in the interval (x -1 ,x0), f″′(x 0,1 ) is the third-order derivative of f(x) in the interval (x0, x1). It is easy to find from the above formula that this central difference formula is more accurate than the forward difference formula and the backward difference formula. However, the application of this central difference formula with a second-order truncation error does not necessarily make it more accurate than the forward difference formula with a first-order truncation error and the backward difference formula with a first-order truncation error. This is because in practical applications, the time derivative applied to the unsteady heat transfer equation will always produce an unstable process. This unstable result is invalid, but the application of the forward difference formula can meet the needs of the solution. In order to obtain the corresponding second-order derivative at x = x0, add formula (2) and formula (3):

[0062]

[0063] The formula has a truncation error term (Δx) 2 (f (4) (x -1,0 )+f (4) (x 0,1 )) / 4! , where f (4) (x -1,0 ) is in the interval (x -1 ,x0), the fourth-order derivative, f (4) (x 0,1) is the fourth-order derivative of f(x) on the interval (x0,x1).

[0064] The system of equations for the steady-state heat conduction forward problem model is as follows:

[0065]

[0066] C1:T=f(x),y=L y (8)

[0067] C2:

[0068] C3:

[0069] C4:

[0070] Where C1 is the temperature distribution boundary, C3 and C4 are adiabatic boundaries, and C2 is the heat flow boundary q, which is fixed at 150 (w / m 2 ), λ is the thermal conductivity of 50w / (m·K).

[0071] The partial differential equation can be written as follows:

[0072]

[0073] Where T i,j is the temperature at the discrete point (i, j), removing the error term, we get:

[0074]

[0075] Let Δx=Δy, which can be rewritten as

[0076]

[0077] This formula is the internal node formula to be solved and is an explicit format of finite differences.

[0078] In the step (S3),

[0079] The objective function is constructed as follows:

[0080]

[0081] In the above formula is the calculated temperature value at the measuring point k, B is the unknown parameter vector to be identified, and the calculated temperature value The direct heat conduction problem model is solved according to the guessed value of B. is the temperature measurement value at the measuring point k. When the above objective function J(B) reaches its minimum value, the vector B obtained by solving is the unknown parameter vector.

[0082] The random walk process of the salp algorithm inversion is as follows:

[0083] X D×N =rand(D,N).(ub(D,N)-lb(D,N))+lb(D,N) (16)

[0084] The leader of the group Indicates that followers use Indicates that i = 2, 3, 4, ..., N; d = 1, 2, 3, ... D

[0085] During the movement and foraging of the salp chain, the leader's position update is expressed as:

[0086]

[0087] In the formula, and F d are the position of the first salp and the position of the food in the dth dimension, respectively, and ub and lb are the corresponding upper and lower bounds, respectively. c1, c2, c3 are control parameters.

[0088] The leader's position update is only related to the location of the food. c1 is the convergence factor in the optimization algorithm, which plays a role in balancing global exploration and local development. It is the most important control parameter in salps. The expression of c1 is:

[0089]

[0090] Where: l is the current iteration number; L is the maximum iteration number. The convergence factor is a decreasing function of 2-0. The control parameters c2 and c3 are random numbers in [0,1] to enhance The randomness of the chain group can improve the global search and individual diversity.

[0091] During the movement and foraging of the salp chain, the followers move forward in a chain through the mutual influence between the individuals in front and behind. Their displacement conforms to Newton's laws of motion, and the movement displacement of the followers is:

[0092]

[0093] Considering that in the optimization algorithm, t is iterative, let t = 1 and v0 = 0 during the iteration. It can be expressed as:

[0094]

[0095] Where: i ≥ 2; and are the positions of two salps that are closely connected to each other in the dth dimension. Therefore, the position of the follower is expressed as:

[0096]

[0097] Where: and are the positions of the followers after the update and before the update in the dth dimension, respectively.

[0098] Algorithm flow:

[0099] 1) Initialize the population. According to the upper and lower bounds of each dimension of the search space, initialize a population of salps of size N×D.

[0100] 2) Calculate the initial fitness. Calculate the fitness values ​​of N salps.

[0101] 3) Food selection. Since we do not know the location of the target (i.e., food) during actual positioning, the salps are sorted according to their fitness values, and the location of the salp with the best fitness ranked first is set as the current food location.

[0102] 4) Select leaders and followers. After the food location is selected, there are N-1 salps left in the group. According to the order of the salps group, the salps in the first half are regarded as leaders, and the rest are regarded as followers.

[0103] 5) Position update: First update the leader's position, then update the follower's position.

[0104] 6) Calculate fitness. Calculate the fitness of the updated group. Compare the fitness value of each salp individual after the update with the fitness value of the current food. If the fitness value of the updated salp is better than that of the food, the position of the salp with the better fitness value is used as the new food position.

[0105] 7) Repeat steps 4) to 6) until a certain number of iterations is reached or the fitness value reaches the termination threshold. After the termination condition is met, the current food position is output as the estimated position of the target.

[0106] The specific implementation process includes:

[0107] like Figures 1 to 6 As shown, in this experimental example, a track damage detection method based on the salp algorithm and temperature inversion theory is provided, and the process is as follows:

[0108] First, the humidity of the track is detected using a microwave humidity sensor, while the temperature is accurately measured using an infrared thermometer. In order to calculate the thermal conductivity, an adiabatic temperature rise meter is used to observe the surface temperature rise of the railway track at a specific temperature. In addition, the laser flash method is used to directly determine the thermal diffusivity of the material. In order to obtain the density of the railway track, the direct weighing method is used to measure the volume and weight of the sample.

[0109] In order to solve the heat transfer problem, a two-dimensional unsteady heat transfer model along the horizontal direction of the railway track was constructed based on Fourier's law of heat conduction and the principle of energy conservation. In order to make the model closer to the actual situation, we simplified it as follows:

[0110] (1) The railway track is considered as a composite material and a two-dimensional numerical model is established for it. In this model, we fit the temperature correlation of the thermal parameters of mortar and aggregate based on the existing experimental data in the literature. We assume that the railway track is homogeneous and its thermal parameters do not change with time. At the same time, there is no internal heat source inside the track. We ignore the contact thermal resistance and assume that the temperature on the interface is continuously distributed.

[0111] (2) The inner side of the railway track is in close contact with the metal wall, which maintains a constant temperature Tf, forming a third-type boundary condition, and the convective heat transfer coefficient between them is h1. The outer side of the railway track is directly exposed to the ambient temperature Ta, which also belongs to the third-type boundary condition, and its convective heat transfer coefficient is h2.

[0112] In order to solve the unsteady-state heat conduction problem, we set the model parameter values ​​and used the finite difference method for numerical calculation to obtain the temperature distribution on the outer wall of the railway track.

[0113] In the solution process, we first set an initial value (Tf)0 for the temperature inside the railway track, and then substitute this value into the direct problem equations. Through programming calculation, the temperature distribution of each point in the heat conduction direct problem area can be obtained.

[0114] Next, the temperature of the outer wall of the railway track obtained by solving the unsteady heat conduction problem is compared with the temperature data actually measured by the temperature probe. These data will be substituted into the objective function used to solve the inverse problem to determine whether the solution meets the convergence criteria. If so, we can output the inversion result of the internal temperature.

[0115] The essence of the inverse problem of heat conduction is to construct an objective function that aims to minimize the difference between known and unknown parameters. By adopting appropriate optimization algorithms, we can solve the inverse values ​​of these unknown parameters and thus gain a more accurate understanding of the temperature distribution inside the railway track.

[0116] When the value of the objective function J is lower than a pre-set sufficiently small number μ, it means that the internal temperature distribution obtained by the inverse heat conduction problem is very close to the true solution. To verify this, we substitute the calculated value of the outer wall temperature of the railway track at a given initial internal temperature (Tf)0 into the inverse problem objective function and evaluate the value of the objective function.

[0117] If the value of the objective function is too large and does not meet our convergence criteria, we need to iteratively update Tf. In each iteration, we use the Salp algorithm to determine the new Tf value, and then substitute this new value into the direct problem for calculation to obtain the new temperature distribution. Then, we recalculate the value of the objective function. This iterative process will continue until the value of the objective function meets the convergence criteria. In this way, we can gradually approach the true internal temperature distribution.

[0118] Table 1 Inversion effects of different algorithms for inversion temperature

[0119]

[0120] Table 1 shows the analysis of the trap identification results of the two algorithms. It can be seen from the table that the Salp algorithm is superior to the Archimedes algorithm in terms of convergence standard, convergence speed and robustness. The algorithm has a small relative error and strong robustness. This advantage shows that the Salp inversion algorithm has broad application prospects in the field of thermal nondestructive testing.

[0121] The temperature field is the main factor that causes damage to the track interlayer interface and even separation. The periodic temperature effect causes repeated warping and deformation of the structural layer, causing the gap between the structural layers, resulting in local support of the track structure, high temperature arching of the longitudinal plate structure and other unfavorable conditions. It will also generate large tensile stress on the top or bottom surface of the track, causing concrete cracking, which will have an adverse effect on the durability and service safety of the ballastless track. Therefore, we can detect the damage of the track by obtaining the temperature field distribution.

[0122] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0123] In the several embodiments provided in the present application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the division of the units described above is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The above-mentioned units may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

[0125] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A railway track damage detection method based on the salp algorithm and temperature inversion theory is characterized by: include: S1. Establish an orbit inversion model and use the finite difference method to analyze the discrete orbit model; S2. Simulate and calculate the track model, and use the finite difference method to obtain the temperature distribution of the two-dimensional I-shaped plate of the track; S3, introduce the Salp algorithm, and use the internal temperature data of the track obtained by solving the forward problem as the input of the inverse problem to invert and identify the temperature information inside the track; S4. Use the internal temperature data obtained by inversion to accurately detect the damage of the track.

2. The railway track damage detection method based on the salp algorithm and temperature inversion theory according to claim 1 is characterized in that: In step S1 , it is necessary to collect the thermophysical parameters of the track, including thermal conductivity, thermal diffusion coefficient, density, specific heat capacity and heat transfer coefficient.

3. The railway track damage detection method based on the salp algorithm and temperature inversion theory according to claim 1 is characterized in that: In step S2, the internal parameters of the two-dimensional model are set, and in step S3, the salp algorithm is introduced, both of which are simulated and calculated by Matlab software.

4. The railway track damage detection method based on the salp algorithm and temperature inversion theory according to claim 1 is characterized in that: In step S2, the Taylor series expansion is used to solve the first-order and second-order derivatives, and the partial differential equation is established for the steady-state heat conduction forward problem model. Combined with the boundary conditions, it is discretized into an explicit finite difference format.

5. The railway track damage detection method based on the salp algorithm and temperature inversion theory according to claim 1 is characterized in that: In step S3, the objective function constructed is as follows: in, is the calculated temperature value at the measuring point k, B is the unknown parameter vector to be identified, and the calculated temperature value Solving the heat conduction direct problem model based on the guessed value of B yields: is the temperature measurement value at the measuring point k. When the above objective function J(B) reaches its minimum value, the vector B obtained by solving is the unknown parameter vector.

6. The railway track damage detection method based on the salp algorithm and temperature inversion theory according to claim 5 is characterized in that: In step S3, the steps of using the salp algorithm for inversion include: Initialize the population: Generate a population of N×D salps based on the upper and lower bounds of the search space; Calculate fitness: evaluate the fitness value of each individual and select the individual with the best fitness as the food location; Update the positions of the leader and followers: By setting the convergence factor c1 and the random factors c2, c3, the positions of the leader and followers are dynamically adjusted to make them approach the target area; Iterative optimization: Continuously update the fitness value until the termination condition is reached, and finally output the target parameter estimate.

7. An electronic device, comprising a processor and a memory connected to the processor for storing instructions executable by the processor, characterized in that: The processor is used to execute the railway track damage detection method based on the Salp Intense algorithm and temperature inversion theory as described in any one of claims 1-6 above.

8. A server, characterized in that: It includes at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor so that the at least one processor executes the railway track damage detection method based on the Salp Intense algorithm and the temperature inversion theory as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the railway track damage detection method based on the salp algorithm and temperature inversion theory as described in any one of claims 1 to 6 is implemented.

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