Probe coupling control parameter optimization method based on particle swarm optimization algorithm
Through the method based on the particle swarm optimization algorithm, the key parameters of the probe are optimized, which solves the problem of poor coupling between the probe and the wheel surface in high-speed wheel pair detection, and improves detection accuracy and signal stability.
Patent Information
- Application Number
- CN202411527871.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-30
- Publication Date
- 2025-02-14
AI Technical Summary
In high-speed wheel pair detection, the problem of maintaining good coupling and signal transmission between the probe and the wheel surface, resulting in inaccurate detection results.
The key parameters of the probe (mass, spring stiffness coefficient, probe extension length) are optimized to ensure that the probe and wheel pair remain stable coupled under high-speed conditions.
By optimizing probe parameters, detection accuracy and reliability are improved, the contact time of the probe is extended, and the stability of ultrasonic signals is improved.
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Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of train automatic detection and control, and relates to a method for optimizing coupling control parameters during the contact process between an ultrasonic probe and a wheelset, and in particular to a method for optimizing probe coupling control parameters based on a particle swarm optimization algorithm. Background Art
[0002] With the rapid development of high-speed railways and EMUs, train driving safety has become one of the key issues in the railway system. As the core component of the train, the quality and operating status of the wheelset have an important impact on the kinematics, dynamics and safety of the train. In order to ensure the safe operation of the train, it is necessary to conduct regular flaw detection on the wheelset.
[0003] Currently, ultrasonic detection methods have been widely used in wheelset detection. However, due to the complex contact state between the high-speed moving wheel and the probe, it is difficult to ensure good coupling between the probe and the wheel tread, resulting in inaccurate detection results.
[0004] When the wheel is running at high speed, the probe needs to adjust its position quickly and stably to adapt to the changes in the wheel tread and maintain the stability of ultrasonic transmission and recovery. How to optimize the parameters of the probe (such as mass, spring stiffness, probe extension length, etc.) to achieve better coupling control has become a technical problem in the current field of automated testing. Therefore, it is necessary to optimize the key parameters of the probe to ensure that it can maintain stable contact with the wheel surface under high-speed running conditions. Summary of the invention
[0005] Another object of the present invention is to provide a coupling ultrasonic probe mechanism.
[0006] The purpose of the present invention is to provide a probe coupling control parameter optimization method based on particle swarm optimization (PSO).
[0007] The present invention optimizes key probe parameters such as probe mass, spring stiffness coefficient, and probe extension length to ensure stable coupling and signal transmission of the probe in high-speed wheelset detection, thereby improving the accuracy and reliability of flaw detection.
[0008] To achieve the above object, the technical solution of the present invention is as follows: A probe coupling control parameter optimization method based on a particle swarm optimization algorithm comprises the following steps: (1) Design a coupled ultrasonic probe mechanism with a single-chain structure and establish a coupled kinematic model of the ultrasonic probe mechanism and wheelset; (2) Using the particle swarm optimization algorithm (PSO) to optimize the design of key probe parameters such as probe mass, probe extension length, and spring stiffness coefficient, the optimal solution is found; (3) The key parameters of the probe are adjusted according to the optimization results to ensure that the probe and the wheelset maintain a stable coupling state when the wheel moves at high speed.
[0009] Furthermore, the step (2): Determining an optimization objective function, wherein the objective function is set according to the coupling characteristics of the ultrasonic probe and the effectiveness of signal transmission; Particle swarm initialization, including particle positions and velocities; The key parameters of the probe are iteratively optimized according to the PSO algorithm until predetermined optimization conditions are met, and finally the optimized key parameters of the probe are output.
[0010] Furthermore, the objective function is set based on the probe coupling efficiency, signal reflectivity and detection accuracy, and the objective function is: , where T is the maximum time for the probe spring to be steadily compressed, is the probe mass, is the probe extension length, is the spring stiffness coefficient.
[0011] Furthermore, the optimization process is combined with multi-objective optimization technology to simultaneously optimize multiple key parameters of the probe.
[0012] Furthermore, the particle swarm initialization based on the PSO algorithm includes randomly generated initial positions and velocities, and constraint processing is performed according to the key parameters of the probe and experimental data; each particle represents a combination of probe parameters, including probe mass, spring stiffness coefficient, and probe extension length.
[0013] Furthermore, the key parameters of the probe are iteratively optimized according to the PSO algorithm until the predetermined optimization conditions are met, and the optimized key parameters of the probe are finally outputted: The objective function calculation: for each particle, the coupled kinematic model of the ultrasonic probe mechanism and the wheelset is used to calculate its dynamic response, including key indicators such as contact time and signal stability, as the fitness value; Speed and position update: Use the objective function to update the speed and position of each particle, and gradually iterate to approach the global optimal parameter combination; Determination of the global optimal solution: When the algorithm reaches the set number of iterations, or the changes in all particles tend to be stable, the PSO is considered to have converged. The global optimal solution at this time is the optimal probe design combination parameter, that is, the probe key parameters.
[0014] Furthermore, in step (1), the coupled ultrasonic probe mechanism of the single-chain structure includes a static platform and a dynamic platform which are parallel to each other, and a single spring is used to support the static platform and the dynamic platform. The single spring makes it a floating structure, and during the detection process, the probe can perform real-time posture fine-tuning according to the change of the wheel tread shape, thereby ensuring good coupling of the contact surface.
[0015] Furthermore, the coupled ultrasonic probe mechanism of the single-chain structure: a mounting plate is arranged at the bottom of the probe mechanism, a cavity is arranged in the mounting plate for fixing a spring, and the spring is nested outside the bottom of the dual-crystal probe; a connecting frame is arranged at the top of the probe mechanism, and a hole is arranged on the connecting frame for placing the head of the dual-crystal probe; the dual-crystal probe is placed between the mounting plate and the connecting frame to form a coupled ultrasonic probe mechanism of the single-chain structure.
[0016] Furthermore, the optimization range of the ultrasonic probe mass is 30g to 40g, the optimization range of the probe extension length is 4mm to 8mm, and the optimization range of the spring stiffness coefficient is 1000 to 2000.
[0017] Furthermore, in the simulation experiment of the PSO algorithm, the wheel speed is 30 km / h.
[0018] Compared with the prior art, the present invention has the following beneficial effects: (1) The method of the present invention improves the global search capability of the optimization process and reduces the possibility of local optimality.
[0019] (2) The method of the present invention improves the optimization speed and accelerates the convergence of the algorithm.
[0020] (3) The method of the present invention provides a more stable coupling control scheme, which effectively prolongs the contact time of the probe and improves the reliability of the ultrasonic signal.
[0021] (4) The present invention is applicable to the probe design and parameter optimization in the automated flaw detection of train wheels, which can effectively improve the detection accuracy and is widely used in the driving safety assurance system of high-speed trains. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure 1 Schematic diagram of the structure of the coupled ultrasonic probe mechanism.
[0023] Figure 2 Schematic diagram of the probe spring compression change corresponding to the initial parameters.
[0024] Figure 3 Schematic diagram of the fitness evolution curve.
[0025] Figure 4 Schematic diagram of spring length change corresponding to the optimized parameters.
[0026] Among them, 1-dual crystal probe, 2-probe spring, 3-connecting frame, 4-mounting plate. DETAILED DESCRIPTION
[0027] The present invention is described in detail below with reference to the accompanying drawings and embodiments.
[0028] The probe coupling control parameter optimization method based on particle swarm optimization (PSO) algorithm of this embodiment includes the following steps: (1) A coupled ultrasonic probe mechanism with a single-chain structure is proposed, the kinematic model of the probe is established, and the dynamic response process of the probe at a fixed vehicle speed is determined.
[0029] In combination with the process flow and relevant standards of wheel ultrasonic testing, this embodiment proposes a new coupling ultrasonic probe mechanism, which adopts a single spring design to make it have floating characteristics and can achieve real-time posture adjustment according to the shape change of the wheel tread during the detection process. The mechanism consists of a static platform and a dynamic platform, which are parallel to each other and connected by a single spring 2, so that the probe can flexibly adapt to different contact conditions, ensuring a good coupling effect during the detection process, thereby improving the accuracy and reliability of the detection.
[0030] Figure 1 The schematic diagram of the operation of the coupled ultrasonic probe mechanism of the single chain structure is shown. The connecting frame 3 in the ultrasonic probe mechanism is used to arrange multiple dual crystal probes 1. Figure 1 As shown, two dual crystal probes 1 are connected and arranged in the ultrasonic probe mechanism.
[0031] Specifically, a mounting plate 4 is provided at the bottom of the probe mechanism, and a cavity is provided in the mounting plate 4 for fixing a spring 2, and the spring 2 is nested outside the bottom of the dual-crystal probe 1; a connecting frame 3 is provided on the upper part of the probe mechanism, and a plurality of holes are provided on the connecting frame 3 for placing the head of the dual-crystal probe 1, and the dual-crystal probe 1 is placed between the mounting plate 4 and the connecting frame 3, thereby forming a coupled ultrasonic probe mechanism with a single-chain structure.
[0032] The wheel moves in the AB direction, contacts the probe from the A end and leaves the probe from the B end. Here, represents the part of the dual crystal probe 1 that exceeds the connecting frame 3, that is, the height of the probe array or the extended length of the probe; since the wheel moves in only one direction and is subjected to force, and the changes at both ends of the spring 2 are not completely equivalent, the two ends of the spring 2 are considered separately, and the spring 2 is equivalent to two rigid rods. The two end joints of the rigid rod are equipped with a space torsion spring 2 to simulate the ability of the spring 2 to resist deformation. The entire mechanism can be regarded as a pseudo-rigid body.
[0033] The contact surface between the top of the probe and the wheel is called the dynamic platform, and the connection surface between the bottom of the probe and the mounting plate is called the static platform. Coordinate systems are established on the two platforms respectively, and the rotation transformation matrix between the two coordinate systems is: (1) in, Respectively represent the axis, axis, The angle of rotation of the shaft, represents the distance between the moving platform and the stationary platform. Assuming that the wheel is facing Axis movement, then the rotation transformation matrix is equivalent to: (2) Assume that the lengths of the two ends of the spring are and , the change values are and In the moving coordinate system, The coordinates of the point are expressed as , The coordinates of the point are expressed as ; In the static coordinate system, The coordinates of the point are expressed as , The coordinates of the point are expressed as , then (3) (4) According to the position relationship between the dynamic and static platforms, the lengths of both ends of the spring can be obtained by inverse position solution. and , which is expressed as follows: (5) (6) (2) Apply PSO to optimize the ultrasonic probe coupling control parameters: The PSO algorithm is a global optimization algorithm based on swarm intelligence, proposed by Kennedy and Eberhart in 1995. PSO searches for the optimal solution in the solution space by simulating the foraging behavior of bird flocks and using the cooperation of individuals in the group (called particles). The PSO algorithm initializes a group of particles in a search space, and each particle represents a possible solution. Each particle has its position and velocity, and updates its position and velocity based on its own experience and the experience of the group. The particles will remember the best position they have found (individual optimality), and each particle will also track the global best position found in the group (group optimality). The speed update formula for each particle is as follows: (7) in, It is Particles in time speed; is the inertia weight, which controls the influence of the previous moment’s velocity on the current velocity; and They are the acceleration constants, which are used to balance the individual optimum and the global optimum; and yes The random number between ensures that the particles have a certain randomness; It is The historical optimal position found by each particle is the individual optimal position, and the group optimal position found by all particles is the global optimal position. It is Particles in time The position update formula of each particle is as follows: (8) By continuously adjusting the speed and position of the particles, the entire group will eventually converge to the optimal solution. The PSO algorithm has the following characteristics: (a) Simple and easy to implement: The PSO algorithm has a simple mathematical formula and is easy to implement. (b) A small number of parameter adjustments: PSO only has a few parameters that need to be adjusted, such as inertia weight, acceleration constant, number of particles, etc. (c) Global search capability: By combining individual optimality with group optimality, the PSO algorithm can avoid falling into local optimality and has the ability of global search. (d) Fast convergence: PSO has a faster convergence speed in the early stage and is suitable for handling large-scale optimization problems.
[0034] In this embodiment, in order to optimize the coupling performance between the ultrasonic probe and the wheelset during high-speed detection, the PSO algorithm can be used to optimize the key physical parameters of the probe, such as the mass of the probe, the stiffness coefficient of the spring, the extension length of the probe, etc. The following is a detailed description of how to apply PSO to this optimization process: (a) Definition of optimization objective function: The optimization objective function (Formula (12) is to ensure that the probe maintains a good coupling contact state and stable ultrasonic signal transmission when the wheel passes at high speed.
[0035] Furthermore, the optimization objective function is set based on probe coupling efficiency, signal reflectivity and detection accuracy.
[0036] The maximum time for which the probe spring is stably compressed is defined as , the time It indicates the duration during which the spring compression remains within the set acceptable range during the probe detection process. The length of this time is directly affected by the quality of the probe. , Probe extension length and spring constant The influence of parameters such as , and these parameters jointly determine the change of the probe rod length during dynamic operation. The specific mathematical expressions are shown in formulas (5) and (6), which describe the change of the spring rod length over time. In order to optimize the coupling system so that the probe maintains stable contact under high-speed detection conditions, the problem can be simplified to the optimization objective function as follows to maximize For the purpose, subject to the above parameters , and Constraints: (12) (b) Particle initialization: Each particle represents a possible combination of probe parameters (probe mass, spring stiffness coefficient, probe extension length). Particles are randomly initialized in parameter space and their velocities are also randomly set.
[0037] Furthermore, the particle initialization of the PSO algorithm includes randomly generated initial positions and velocities, and constraint processing is performed according to physical parameters of the ultrasonic probe (probe mass, spring stiffness coefficient, probe extension length) and experimental data.
[0038] (c) Objective function calculation: For each particle, the probe dynamics model (Equation (5) and Equation (6)) is used to calculate its dynamic response, including key indicators such as contact time and signal stability, as the fitness value.
[0039] (d) Speed and position update: Use the above formula (Formula (12) to update the speed and position of each particle, and gradually iterate to approach the global optimal parameter combination.
[0040] (e) Determination of the global optimal solution: When the algorithm reaches the set number of iterations, or the changes in all particles tend to be stable, the PSO is considered to have converged. The global optimal solution at this time is the optimal probe design combination parameter.
[0041] Figure 2 The corresponding compression change of the probe spring 2 obtained by the initial value before the PSO algorithm is performed is given. After the particle population is initialized by the PSO algorithm, the fitness function (the optimization objective function) is used to evaluate the quality of each particle, update the position and speed of the particle, and iteratively optimize the key physical parameters of the probe until the global optimal solution is found.
[0042] The optimized key physical parameters of the probe are brought into the simulation environment to verify the probe's response stability, maximum acceleration and signal stability at a vehicle speed of 30 km / h, ensuring that the optimized design can meet actual needs.
[0043] Figure 3 The change of fitness value during the iteration of PSO algorithm is given.
[0044] Figure 4 The change in spring length corresponding to the optimized parameters is given. The corresponding optimal spring mass is 31.15g, the probe extension length is 5.84mm, the spring stiffness coefficient is 1947.02, and the optimal fitness value is 57.47.
[0045] The above description is only a preferred embodiment of the present invention and does not constitute a limitation on the protection scope of the present invention. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.
Claims
1. A probe coupling control parameter optimization method based on particle swarm optimization algorithm, characterized in that: The method comprises the following steps: (1) Design a coupled ultrasonic probe mechanism with a single-chain structure and establish a coupled kinematic model of the ultrasonic probe mechanism and wheelset; (2) Using the particle swarm optimization algorithm (PSO) to optimize the design of key probe parameters such as probe mass, probe extension length, and spring stiffness coefficient, the optimal solution is found; (3) The key parameters of the probe are adjusted according to the optimization results to ensure that the probe and the wheelset maintain a stable coupling state when the wheel moves at high speed.
2. According to the particle swarm optimization algorithm-based probe coupling control parameter optimization method of claim 1, characterized in that: The step (2): Determining an optimization objective function, wherein the objective function is set according to the coupling characteristics of the ultrasonic probe and the effectiveness of signal transmission; Particle swarm initialization, including particle positions and velocities; The key parameters of the probe are iteratively optimized according to the PSO algorithm until predetermined optimization conditions are met, and finally the optimized key parameters of the probe are output.
3. The method for optimizing probe coupling control parameters based on particle swarm optimization algorithm according to claim 2, characterized in that: The objective function is set based on the probe coupling efficiency, signal reflectivity and detection accuracy. The objective function is: , where T is the maximum time for the probe spring to be steadily compressed, is the probe mass, is the probe extension length, is the spring stiffness coefficient.
4. A probe coupling control parameter optimization method based on particle swarm optimization algorithm according to claim 2 or 3, characterized in that: The optimization process combines multi-objective optimization technology to simultaneously optimize multiple key parameters of the probe.
5. The method for optimizing probe coupling control parameters based on particle swarm optimization algorithm according to claim 2 or 3, characterized in that: The particle swarm initialization based on the PSO algorithm includes randomly generated initial positions and velocities, and constraint processing is performed according to the key parameters of the probe and experimental data; each particle represents a combination of probe parameters, including probe mass, spring stiffness coefficient, and probe extension length.
6. The method for optimizing probe coupling control parameters based on particle swarm optimization algorithm according to claim 2 or 3, characterized in that: The key parameters of the probe are iteratively optimized according to the PSO algorithm until the predetermined optimization conditions are met, and the optimized key parameters of the probe are finally outputted: The objective function calculation: for each particle, the coupled kinematic model of the ultrasonic probe mechanism and the wheelset is used to calculate its dynamic response, including key indicators such as contact time and signal stability, as fitness values; Speed and position update: Use the objective function to update the speed and position of each particle, and gradually iterate to approach the global optimal parameter combination; Determination of the global optimal solution: When the algorithm reaches the set number of iterations, or the changes in all particles tend to be stable, the PSO is considered to have converged. The global optimal solution at this time is the optimal probe design combination parameter, that is, the probe key parameters.
7. A probe coupling control parameter optimization method based on particle swarm optimization algorithm according to any one of claims 1 to 6, characterized in that: The step (1): the coupled ultrasonic probe mechanism of the single-chain structure comprises a static platform and a dynamic platform which are parallel to each other, and a single spring is used to support the static platform and the dynamic platform, so that the static platform and the dynamic platform become a floating structure through the single spring. During the detection process, the probe can perform real-time posture fine-tuning according to the change of the wheel tread shape, thereby ensuring good coupling of the contact surface.
8. The method for optimizing probe coupling control parameters based on particle swarm optimization algorithm according to claim 7, characterized in that: The coupled ultrasonic probe mechanism of the single-chain structure: a mounting plate is arranged at the bottom of the probe mechanism, a cavity is arranged in the mounting plate for fixing a spring, and the spring is nested outside the bottom of the dual-crystal probe; a connecting frame is arranged at the top of the probe mechanism, and a hole is arranged on the connecting frame for placing the head of the dual-crystal probe; the dual-crystal probe is placed between the mounting plate and the connecting frame to form a coupled ultrasonic probe mechanism of the single-chain structure.
9. A probe coupling control parameter optimization method based on particle swarm optimization algorithm according to any one of claims 1 to 6, characterized in that: The optimization range of the ultrasonic probe mass is 30g to 40g, the optimization range of the probe extension length is 4mm to 8mm, and the optimization range of the spring stiffness coefficient is 1000 to 2000.
10. A probe coupling control parameter optimization method based on particle swarm optimization algorithm according to any one of claims 1 to 6, characterized in that: In the simulation experiment of the PSO algorithm, the wheel set speed is 30 km / h.