Silking machine rotor vibration suppression optimization method
By establishing a weighted approximate model and an optimization module for the actuator, the problem of increased vibration of the spinning machine rotor in a high-temperature environment was solved, and rapid, accurate and efficient vibration suppression of the spinning machine was achieved, reducing the risk of equipment damage and maintenance costs.
Patent Information
- Application Number
- CN202510355340.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-24
- Publication Date
- 2025-09-26
AI Technical Summary
The vibration of the spinning machine rotor intensifies in a high-temperature environment, affecting the normal operation of the equipment and increasing maintenance costs. Existing technology makes it difficult to obtain an optimization solution through experimental permutations and combinations.
An optimization module composed of multiple weighted approximate models was used to establish a simulation model by collecting and analyzing the operating parameters of the spinning head. An optimization strategy was formulated by combining the polynomial response surface model, Kriging model and neural network model. The optimization was implemented through the oil film bearing cooling system, piezoelectric actuator and MFC piezoelectric actuator.
It achieves fast, accurate and efficient vibration suppression of the laying head rotor, reduces the risk of equipment damage and lowers maintenance costs.
Smart Images
Figure CN120706033A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of laying head rotor vibration optimization, in particular to a laying head rotor vibration suppression optimization method. Background Art
[0002] The laying head is a key piece of equipment in a high-speed wire rod production line, located between the water-cooling section after the finishing mill and the bulk coil conveyor. The laying head primarily consists of a motor, a laying plate, a laying pipe, a transmission, a hollow shaft, and a bearing block. Its basic operating principle is to utilize the centrifugal force of high-speed rotation to reprocess the wire rod rolled by the finishing mill into a continuous coil product of a specified diameter. The wire rod from the high-speed finishing mill is fed into the laying head's hollow shaft via a guide tube and pinch rollers. The rapidly rotating hollow shaft simultaneously drives the laying plate and laying pipe, which are fixed to the hollow shaft, to rotate at high speed. Under the centrifugal force of the spinning laying plate and laying pipe, the wire rod is discharged along the tangential direction of the hollow shaft. Assisted by the spiral laying plate, the coiled wire is gradually pushed and dumped onto the moving conveyor, forming a continuous, uninterrupted coil.
[0003] During operation, a spinning machine's rotor heats up significantly due to contact with high-speed, hot wire. This high temperature environment has numerous adverse effects on the spinning machine's rotor system. These include a decrease in the rotor's elastic modulus, leading to dynamic balance issues, and changes in the oil viscosity of the oil film bearings, which reduces support stiffness and alters the rotor's natural frequency, further increasing rotor vibration. These vibration issues not only affect the spinning machine's normal operation and reduce production efficiency, but can also lead to premature equipment failure and increase repair costs. Therefore, optimizing and suppressing the vibration of the spinning machine's rotors following elevated temperatures is of great practical significance. However, due to the numerous factors influencing rotor vibration, it is not possible to obtain an optimal solution through experimental permutations and combinations. Summary of the Invention
[0004] The technical problem to be solved by the present invention is to provide a method for optimizing vibration suppression of a spinning machine rotor in view of the above shortcomings.
[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:
[0006] A method for optimizing vibration suppression of a spinning machine rotor comprises the following steps:
[0007] Step S1, collecting operating parameters of the laying head;
[0008] Step S2: Analyze the operating parameters and determine whether the collected operating parameters are within the corresponding preset ranges. If all operating parameters are within the preset ranges, proceed to step S1; if one or more operating parameters are not within the preset ranges, proceed to step S3.
[0009] Step S3, formulating an optimization strategy through an optimization module composed of multiple weighted approximation models;
[0010] Step S4: Implement the optimization strategy through the actuators related to the operating parameters that are not within the preset range, and execute step S1 after the optimization strategy is implemented.
[0011] Furthermore, before step S3, an approximate model in the optimization module is established, and the establishment of the approximate model includes the following steps:
[0012] Step S2.51: Design an optimization algorithm for optimizing the vibration of the laying head rotor, wherein parameters related to the rotor vibration are used as optimization target values, and operating parameters of the laying head are used as optimization variables;
[0013] Step S2.52: Set at least six sets of key points of operating parameters, conduct experiments on the laying head according to the key points of the multiple sets of operating parameters, collect parameters related to the vibration of the laying head rotor, and obtain measured data of the multiple sets of parameters related to the rotor vibration;
[0014] Step S2.53: Establish a simulation model of the laying head according to the optimization algorithm, using the operating parameters as input variables;
[0015] Step S2.54: regress the simulation model based on the measured data obtained in step S2.52 to verify the accuracy of the simulation model;
[0016] Step S2.55: Using the simulation model, taking the operating parameters as design variables, simulating the vibration of the laying head rotor, and obtaining simulation results of parameters related to the rotor vibration;
[0017] Step S2.56: Based on the simulation results obtained in step S2.55, multiple approximate model algorithms are combined to establish multiple approximate models.
[0018] Furthermore, the optimization algorithm is:
[0019] y=f(x1,x2······x n )
[0020] x id ≤x i ≤x iu , i∈N, 1≤i≤n
[0021] Among them, y is the parameter related to rotor vibration, x i is the i-th operating parameter, x id is the lower limit of the preset range of the i-th operating parameter, x iu is the upper limit of the preset range of the i-th operating parameter.
[0022] Furthermore, in step S2.52, the key points include at least the upper limit and the lower limit of the preset range of the operating parameter.
[0023] Furthermore, the optimization module includes a polynomial corresponding surface model, a Kriging model and a neural network model;
[0024] The optimization strategy includes a preliminary optimization stage, a local optimization stage and an action implementation stage. In the preliminary optimization stage, the weight of the polynomial corresponding surface model is greater than the weights of the Kriging model and the neural network model; in the local optimization stage, the weight of the Kriging model is greater than the weights of the polynomial corresponding surface model and the neural network model; in the action implementation stage, the weight of the neural network model is greater than the weights of the polynomial corresponding surface model and the Kriging model.
[0025] Furthermore, in the preliminary optimization stage, the weights of the polynomial corresponding surface model, the Kriging model, and the neural network model are 75%, 15%, and 10%, respectively;
[0026] In the local optimization stage, the weights of the polynomial corresponding surface model, the Kriging model, and the neural network model are 10%, 80%, and 10%, respectively;
[0027] In the action implementation stage, the weights of the polynomial corresponding surface model, the Kriging model and the neural network model are 10%, 10% and 80% respectively.
[0028] Furthermore, the operating parameters include the oil temperature of the oil film bearing, the oil film pressure and the acceleration of the rotor.
[0029] Furthermore, the actuator related to the oil temperature of the oil film bearing includes a cooling system, the actuator related to the oil film pressure of the oil film bearing includes a piezoelectric actuator, and the actuator related to the acceleration of the rotor includes an MFC piezoelectric actuator.
[0030] After adopting the above technical solution, the present invention has the following advantages compared with the prior art:
[0031] The present invention monitors the relevant parameters of the spinning machine, formulates an optimization strategy through a weighted approximation model according to the specific values of the relevant parameters, and executes the optimization strategy through an actuator. It can give full play to the advantages of different types of approximation models, take into account the advantages of real-time performance and accuracy, and does not require a large number of experiments to obtain a specific optimization plan.
[0032] The present invention is described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] Figure 1 This is a schematic diagram of the optimization process of Example 1 of the present invention;
[0034] Figure 2 This is a structural diagram of the spinning machine. DETAILED DESCRIPTION
[0035] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.
[0036] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", "clockwise" and "counterclockwise" and the like to indicate directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.
[0037] Example 1:
[0038] like Figure 1 As shown, in this embodiment, the oil temperature and oil film pressure of the oil film bearing of the laying head, as well as the rotor acceleration of the laying head are mainly detected, and optimization is performed according to abnormal conditions of the parameters. The specific optimization steps are as follows:
[0039] (1) The oil temperature and oil film pressure of the oil film bearing of the spinning machine, as well as the rotor acceleration of the spinning machine are collected through the spinning machine monitoring system, and the signal values of the three parameters are set as three variables, that is, the signal collection value of each physical quantity is one variable;
[0040] The speed of the middle position of the base of the rotor shaft end of the laying head is used as the optimization target value;
[0041] (2) According to the working scenario of the spinning machine, the ranges of the three variables are set, that is, the upper and lower limits of the variables (the upper and lower limits can be understood as the ranges of the three variables when the spinning machine is in normal working state, which can be measured through experiments);
[0042] (3) The design optimization model is:
[0043] y=f(x1,x2,x3)
[0044] x 1d ≤x1≤x 1u ,x 2d ≤x2≤x 2u ,x 3d ≤x3≤x 3u
[0045] Where y is the optimization target value, specifically the velocity value at the middle position of the base;
[0046] x1, x2, and x3 are all optimization variables, specifically: x1 is the oil temperature of the oil film bearing, x2 is the oil film pressure of the oil film bearing, and x3 is the rotor acceleration;
[0047] x id is the variable x i The lower limit of x iu is the variable x i The upper limit value of
[0048] (4) setting multiple sets of key points about the three variables, including upper limit values, lower limit values, and typical working scenarios. In this embodiment, seven sets of key point data are set, specifically including: upper limit value, typical working scenario 1, typical working scenario 2, typical working scenario 3, typical working scenario 4, typical working scenario 5, and lower limit value;
[0049] (5) Conduct field measurements on the three variables to obtain the measured data of the velocity value at the middle position of the base. The measured data shall be no less than 6 groups, and shall include field measurements of the three variables at the upper and lower limit positions.
[0050] (6) A spinning machine simulation model based on the optimization model was established, and three variables were used as input variables of the simulation model. The simulation model can reduce the workload of multi-parameter and multi-scenario testing. Through Python program control, the simulation analysis of multiple input parameters can be automatically realized, which can effectively reduce the workload of the experiment.
[0051] (7) regressing the measured data obtained in step (5) into the simulation model of step (6) to verify the accuracy of the simulation model;
[0052] (8) Using the three variables as design variables, the simulation model is used to simulate the vibration conditions of the three variables at the seven key points set in step (4) to obtain the velocity value data of the middle position of the base obtained by the simulation model;
[0053] (9) Based on the simulation results of step (8), a correspondence table of three variables (optimization variables) and target values (optimization target values) is listed, and three approximate models (in this embodiment, the approximate models include polynomial response surface, Kriging and neural network) algorithms are combined, and the correspondence table of variables and target values is used as training data to establish three approximate models respectively; the approximate model established by the simulation analysis output result can quickly establish the optimization control equation in the working scene of the spinning machine, and realize the rapid, accurate and efficient formulation of the optimization control plan;
[0054] (10) Establishing the mathematical equation of optimization strategy:
[0055] z=aw1+bw2+cw3
[0056] Where: z is the control parameter of the spinning machine, specifically the change value of the corresponding variable;
[0057] w1, w2, and w3 are three approximate optimization models;
[0058] a, b, and c are the weights of the three approximate models, and a≥0, b≥0, c≥0, a+b+c=1;
[0059] (11) In the process of optimizing the spinning machine, this embodiment adopts a phased optimization strategy, which specifically includes a preliminary optimization phase, a local optimization phase, and an action implementation phase, specifically including the following steps:
[0060] (11.1) In the initial optimization phase, the optimization direction strategy is formulated. The optimization direction needs to be determined quickly, which places a high time requirement. Therefore, the polynomial response surface method, kriging method, and neural network weights are set to a = 75%, b = 15%, and c = 10%. That is, the optimization direction is mainly determined by the polynomial response surface method, which takes advantage of its simplicity and speed.
[0061] (11.2) In the local optimization stage, accurate optimization parameters are required. Therefore, the polynomial response surface, kriging, and neural network weights are set to a = 10%, b = 80%, and c = 10%. That is, the exact solution of the optimization parameters is mainly determined by the kriging model. The kriging method accurately processes the local optimum and can provide a variance estimate of the predicted value, which helps to identify the uncertainty and confidence interval of the prediction. This allows the modeling process to incorporate spatial variability, making the interpolation results more reasonable and accurate.
[0062] (11.3) In the action implementation phase, this phase is the implementation of the optimization strategy. During the implementation process, due to errors and other reasons, fine-tuning is required based on the established optimization strategy. Therefore, the weights of the polynomial response surface, kriging, and neural network are set to a = 10%, b = 10%, and c = 80%, respectively. That is, the fine-tuning results are mainly determined by the neural network model, using the neural network's superior processing ability for highly nonlinear problems, and at the same time verifying the accuracy of the kriging approximate model optimization results. At this point, the optimization action is already being implemented. If the parameters need to be fine-tuned based on the results calculated by the neural network approximate model, the kriging model results will be corrected;
[0063] (12) After the optimization strategy is formulated, the optimization strategy is implemented through the corresponding actors to change the values of the corresponding variables;
[0064] (13) After completing the optimization strategy, the relevant variables are tested through the spinning machine monitoring system. If the relevant variables do not meet the target value requirements, step (11) is re-executed to enter a new round of optimization.
[0065] Since the vibration factors of the rotor in the actual system are mutually influential and dependent, it is rare for the three monitoring values to exceed the preset range individually. However, since each monitoring value has continuous working state points, it is too many to obtain the most strategic permutations and combinations in this scenario through experiments. Therefore, the invention adopts an optimization strategy method based on a weighted approximation model. The specific method is as follows: a simulation model of the rotor system coupling field is established through simulation, and the bearing stiffness of the oil film field and the vibration assignment of the rotor are analyzed. However, since the coupling field simulation time is long, the delay is too long and is not suitable for real-time optimization control. Therefore, the optimization strategy method based on a weighted approximation model is adopted.
[0066] Example 2:
[0067] This embodiment is a laying head monitoring system for implementing the optimization method of the first embodiment.
[0068] like Figure 2 As shown, the spinning head includes a shell 1, a hollow shaft 2 and a spinning disk 3. The hollow shaft 2 (i.e., the rotor part of the spinning head) is rotatably arranged on the shell 1 through a ball bearing 4 and an oil film bearing 5. The spinning disk 3 is arranged at the first end of the hollow shaft 2. A spinning pipe 5 is arranged in the hollow shaft 3, and the spinning pipe 5 extends from the first end of the hollow shaft 2 to the spinning disk 3.
[0069] The laying head monitoring system includes hardware and software parts, wherein the hardware includes: temperature sensor, pressure sensor, speed sensor and signal acquisition instrument;
[0070] In this embodiment, there are two temperature sensors, which are respectively arranged at the oil pipe inlet of the oil film bearing of the laying head and the oil film of the oil film bearing. The temperature sensors are used to detect the oil temperature of the oil film bearing.
[0071] The number of acceleration sensors is one, and the acceleration sensor is arranged on the shell of the spinning machine near the spinning disk, specifically on the shell 1 of the spinning machine (such as Figure 2 The acceleration sensor is used to detect the rotor acceleration of the spinning machine;
[0072] There is one pressure sensor, which is arranged at the oil film of the oil film bearing and is used to detect the oil film pressure of the oil film bearing.
[0073] The software includes: signal acquisition system, signal analysis system, rotor vibration evaluation system and rotor vibration optimization system;
[0074] The signal acquisition system is used to monitor the oil temperature and oil film pressure of the oil film bearing of the spinning machine, as well as the rotor acceleration in real time through sensors;
[0075] The signal analysis system is used to analyze the collected signals to obtain oil temperature, oil film pressure and rotor frequency data;
[0076] The rotor vibration assessment system is used to analyze whether the current oil temperature, oil film pressure, and rotor frequency are within the preset range. If they are all within the pre-qualified range, monitoring will continue. If there is an abnormality in the three monitored values (i.e., at least one value exceeds the pre-qualified range), the optimization module will be activated.
[0077] The optimization module is used to determine the optimization strategy based on the corresponding numerical value of the abnormal quantity. For example, if only the oil temperature increases, the cooling system is activated to adjust the oil temperature to a preset range. If only the oil film pressure changes, the piezoelectric actuator is activated to dynamically adjust the oil film gap, causing the oil film pressure to change, thereby optimizing the support stiffness of the oil film bearing. If only the rotor frequency changes, the MFC piezoelectric actuator is used to adjust the stiffness of the rotor system while regulating the speed.
[0078] The oil temperature and viscosity of the oil film bearing have a significant impact on the vibration characteristics of the rotor system. An increase in temperature will cause the viscosity of the lubricating oil to decrease, thereby reducing the supporting stiffness of the oil film. Therefore, by optimizing the temperature and viscosity of the lubricating oil, the rotor vibration can be effectively suppressed. For example, using a cooling system to keep the lubricating oil temperature within the appropriate range, or selecting the appropriate lubricating oil viscosity grade, can significantly improve the dynamic response of the rotor system.
[0079] The oil film pressure of the oil film bearing is regulated by a piezoelectric actuator, that is, an actuator of piezoelectric material, which is made of stacked piezoelectric material. By applying a certain voltage to it, its own length can be changed. This piezoelectric actuator is installed at the junction of the oil film bearing and the bearing seat. By adjusting the piezoelectric actuator, the oil pressure and oil film thickness can be changed.
[0080] The rotor is attached with MFC piezoelectric fibers (piezoelectric actuators), which can actively control the rotor vibration by inputting characteristic voltage and frequency.
[0081] The foregoing is an example of the best mode of carrying out the present invention. Any portion not described in detail herein is common knowledge within the skill of one of ordinary skill in the art. The scope of protection of the present invention is determined by the claims. Any equivalent transformation based on the technical teachings of the present invention is also within the scope of protection of the present invention.
Claims
1. A method for optimizing vibration suppression of a laying head rotor, characterized in that: The following steps are involved: Step S1, collecting operating parameters of the laying head; Step S2: Analyze the operating parameters and determine whether the collected operating parameters are within the corresponding preset ranges. If all operating parameters are within the preset ranges, proceed to step S1; if one or more operating parameters are not within the preset ranges, proceed to step S3. Step S3, formulating an optimization strategy through an optimization module composed of multiple weighted approximation models; Step S4: Implement the optimization strategy through the actuators related to the operating parameters that are not within the preset range, and execute step S1 after the optimization strategy is implemented.
2. The laying head rotor vibration suppression optimization method according to claim 1, characterized in that: Before step S3, the method further includes establishing an approximate model in the optimization module, wherein establishing the approximate model includes the following steps: Step S2.51: Design an optimization algorithm for optimizing the vibration of the laying head rotor, wherein parameters related to the rotor vibration are used as optimization target values, and operating parameters of the laying head are used as optimization variables; Step S2.52: Set at least six sets of key points of operating parameters, conduct experiments on the laying head according to the key points of the multiple sets of operating parameters, collect parameters related to the vibration of the laying head rotor, and obtain measured data of the multiple sets of parameters related to the rotor vibration; Step S2.53: Establish a simulation model of the laying head according to the optimization algorithm, using the operating parameters as input variables; Step S2.54: regress the simulation model based on the measured data obtained in step S2.52 to verify the accuracy of the simulation model; Step S2.55: Using the simulation model, taking the operating parameters as design variables, simulating the vibration of the laying head rotor, and obtaining simulation results of parameters related to the rotor vibration; Step S2.56: Based on the simulation results obtained in step S2.55, multiple approximate model algorithms are combined to establish multiple approximate models.
3. The laying head rotor vibration suppression optimization method according to claim 2, characterized in that: The optimization algorithm is: y=f(x1,x2······x n ) x id ≤x i ≤x iu ,i∈N,1≤i≤n Among them, y is the parameter related to rotor vibration, x i is the i-th operating parameter, x id is the lower limit of the preset range of the i-th operating parameter, x iu is the upper limit of the preset range of the i-th operating parameter.
4. The method for optimizing vibration suppression of a laying head rotor according to claim 2, characterized in that: In the step S2.52, the key points include at least the upper limit and the lower limit of the preset range of the operating parameter.
5. The laying head rotor vibration suppression optimization method according to claim 1, characterized in that: The optimization module includes a polynomial corresponding surface model, a Kriging model and a neural network model; The optimization strategy includes a preliminary optimization stage, a local optimization stage, and an action implementation stage. In the preliminary optimization stage, the weight of the polynomial corresponding surface model is greater than the weight of the Kriging model and the neural network model; In the local optimization stage, the weight of the Kriging model is greater than the weights of the polynomial corresponding surface model and the neural network model; in the action implementation stage, the weight of the neural network model is greater than the weights of the polynomial corresponding surface model and the Kriging model.
6. The laying head rotor vibration suppression optimization method according to claim 5, characterized in that: In the preliminary optimization stage, the weights of the polynomial corresponding surface model, the Kriging model, and the neural network model are 75%, 15%, and 10%, respectively; In the local optimization stage, the weights of the polynomial corresponding surface model, the Kriging model, and the neural network model are 10%, 80%, and 10%, respectively; In the action implementation stage, the weights of the polynomial corresponding surface model, the Kriging model and the neural network model are 10%, 10% and 80% respectively.
7. The method for optimizing vibration suppression of a laying head rotor according to any one of claims 1 to 6, characterized in that: The operating parameters include the oil temperature of the oil film bearing, the oil film pressure and the acceleration of the rotor.
8. The laying head rotor vibration suppression optimization method according to claim 7, characterized in that: The actuator related to the oil temperature of the oil film bearing includes a cooling system, the actuator related to the oil film pressure of the oil film bearing includes a piezoelectric actuator, and the actuator related to the acceleration of the rotor includes an MFC piezoelectric actuator.