Fuel rod wire winding equipment roller intelligent regulation and control system and method based on edge calculation
By combining edge computing technology and multimodal sensors, high-precision and high-efficiency winding control of fuel rod winding equipment has been achieved, solving the problems of low mechanical adjustment efficiency and insufficient tension control accuracy, and improving the overall performance of the winding process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-21
- Publication Date
- 2026-03-31
AI Technical Summary
Existing fuel rod winding equipment suffers from low mechanical adjustment efficiency, insufficient tension control precision, and lagging intelligence, making it difficult to achieve efficient and precise winding processes.
An intelligent control system for the rollers of a fuel rod winding equipment based on edge computing is adopted. Through the collaborative design of multimodal sensors, edge computing controllers and intelligent drive modules, combined with adaptive control algorithms and digital twin simulation models, real-time data processing and high-precision winding control are achieved.
Significantly improves the precision, efficiency and reliability of the wire winding process, controls tension fluctuation within ±5%, optimizes the standard deviation of pitch consistency to 0.05mm, and achieves process parameter self-tuning adaptation time ≤3 minutes, enabling online diagnosis and wear warning.
Smart Images
Figure CN121763847A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of nuclear fuel element manufacturing technology, specifically relating to an intelligent control system and method for the rollers of a fuel rod winding equipment based on edge computing. Background Technology
[0002] Fuel rod wire winding is a core step in nuclear fuel assembly manufacturing. By spirally winding metal wire around the surface of the fuel rods and controlling the flow rate to promote structure, it directly affects the reactor coolant heat transfer efficiency and the mechanical properties of the assembly. Traditional wire winding equipment generally uses mechanical roller systems. Domestic experience in the use and research of fuel rod wire winding is limited, and existing technologies reveal the following key problems: 1. Low efficiency of mechanical adjustment. Existing roller systems mostly rely on manual adjustment of roller spacing and clamping force. In actual production, significant downtime for adjustments occurs when changing specifications, severely impacting equipment utilization.
[0003] 2. Insufficient tension control accuracy. The stability of the winding tension is a core factor determining pitch consistency. Traditional open-loop control schemes, lacking real-time feedback, result in tension fluctuations of ±15%, with measured pitch deviations exceeding 0.2mm. While subsequent improvements introduced closed-loop control with a servo motor, they failed to address the nonlinear disturbances caused by the time-varying friction coefficient (temperature, wear) at the roller-wire contact surface.
[0004] 3. Lagging level of intelligence. Current equipment generally lacks condition detection and adaptive capabilities: it cannot compensate for uneven wire wrapping caused by fuel rod diameter tolerance (±0.5mm); it does not consider the contact pressure attenuation caused by roller wear; it relies on manual experience to adjust process parameters and lacks data-driven optimization capabilities.
[0005] In recent years, some studies have attempted to improve system performance through sensor fusion and advanced control algorithms. For example, strain-tension feedback control cannot effectively suppress high-frequency disturbances; neural networks are used to predict roller wear, but the model update cycle is as long as 24 hours. These approaches fail to meet real-time control requirements. Although these solutions have improved system performance locally, none of them have achieved the synergistic optimization of minimizing mechanical modifications and maximizing control efficiency.
[0006] In summary, existing technologies suffer from multiple problems, including complex mechanical structure adjustments, insufficient dynamic control precision, and low levels of intelligence. Summary of the Invention
[0007] In view of this, this application provides an intelligent control system and method for the rollers of a fuel rod winding equipment based on edge computing. By adopting a collaborative design of traditional mechanical equipment and intelligent technology, it solves the multiple technical problems of existing technologies, such as complex mechanical structure adjustment, insufficient dynamic control accuracy, and low level of intelligence.
[0008] This application provides a smart control system for the rollers of a fuel rod winding equipment based on edge computing. This system includes a roller actuator, a multimodal sensor, an edge computing controller, a human-machine interface (HMI), and a smart drive module. The multimodal sensor, embedded within the roller actuator and connected to the edge computing controller and HMI, transmits real-time measured data to both the edge computing controller and HMI via a CAN bus. The edge computing controller, connected to the smart drive module and HMI, receives data from the multimodal sensor, performs calculations on the data, and outputs the calculated data to the smart drive module and HMI. The smart drive module, connected to the edge computing controller and roller actuator, converts the calculated data to drive the roller actuator. The HMI, transmitting data to the edge computing controller via TCP, allows browsing of the multimodal sensor, inputting process parameter settings into the sensor, and triggering alarms when data anomalies occur.
[0009] In one specific embodiment of this application, the edge computing controller includes an adaptive control algorithm and a digital twin simulation model. The digital twin model incorporates a feedforward-feedback composite control architecture. A human-machine interface is connected to the edge computing controller via TCP. The edge computing controller also performs Kalman filtering on data transmitted from multimodal sensors. After prediction and feedforward compensation calculations by the feedforward-feedback composite control architecture of the digital twin model, error calculation is performed. The calculated value is then subjected to adaptive fuzzy PID adjustment, and the adjusted output control quantity is updated to the digital twin parameters. If the error exceeds a threshold, the update continues; otherwise, the process terminates.
[0010] In one specific embodiment of this application, the adaptive control algorithm in the edge computing controller adopts Formula 1.
[0011] Fff=0.15( )2+0.08( +0.02 Formula 1 In Formula 1, The rate of change of wire diameter. This is due to the temperature rise of the roller.
[0012] In one specific embodiment of this application, the digital twin simulation model in the edge computing controller is constructed by building a parameterized roller-linear model, real-time data mapping, and a reduced-order contact mechanics model for real-time calculation and dynamic parameter correction.
[0013] In one specific embodiment of this application, the intelligent drive module includes a servo motor and an IGBT power module. The intelligent drive module generates six PWM signals through control commands, which drive the servo motor through the IGBT power circuit. The servo motor is installed inside the roller actuator. The intelligent drive module and the roller actuator transmit data to each other through the six PWM signals and an encoder.
[0014] In one specific embodiment of this application, the multimodal sensor includes a micro-resistivity sensor, a non-contact magnetoelastic torque sensor, and a temperature sensor. The micro-resistivity sensor is installed within the roller bearing housing in the roller actuator for pressure detection. The non-contact magnetoelastic torque sensor is installed at the end of the roller drive shaft in the roller actuator for torque detection. The temperature sensor is embedded in the roller surface in the roller actuator for temperature compensation.
[0015] In one specific embodiment of this application, the edge computing controller uses Formula 2 to calculate the data from the multimodal sensor.
[0016] Y=0.6*F_tension + 0.3*T_torque + 0.1 Formula 2.
[0017] In Formula 2, F_tension is the pressure detected by the micro-resistance sensor; T_torque is the torque detected by the non-contact magnetoelastic torque sensor. This is due to the temperature rise of the roller.
[0018] In one specific embodiment of this application, the edge computing-based intelligent control system for the fuel rod winding equipment rollers further includes a standardized control interface module. This standardized control interface module supports integration with PLC and MES systems.
[0019] A second aspect of this application provides an intelligent control method for the rollers of a fuel rod winding device based on edge computing. This intelligent control method for the rollers of a fuel rod winding device based on edge computing includes: Step S10: Receive data transmitted by the multimodal sensor embedded in the roller actuator; Step S20: Calculate the data transmitted by the multimodal sensor and output the calculated data to the intelligent drive module and the human-machine interface. The intelligent drive module is used to convert the calculated data to drive the roller actuator. The human-machine interface is used to browse the multimodal sensor, input process parameter settings into the multimodal sensor, and issue alarms when data is abnormal.
[0020] In one specific embodiment of this application, step S20 includes: performing Kalman filtering on the data transmitted by the multimodal sensor; calculating the error after prediction and feedforward compensation calculation by the feedforward-feedback composite control architecture of the digital twin model; performing adaptive fuzzy PID adjustment on the calculated value and updating the adjusted output control quantity to the digital twin parameters; if the error is greater than the threshold, the update continues, otherwise the process ends.
[0021] The beneficial effects of this technical solution are as follows: By embedding a multimodal sensor into the roller actuator and using an edge computing controller to calculate the data transmitted by the multimodal sensor and output the calculated data to the intelligent drive module and the human-machine interface, the intelligent drive module converts the calculated data to drive the roller actuator. This employs a collaborative design of traditional mechanical equipment and intelligent technology, achieving high-precision wire winding control and significantly improving the accuracy, efficiency, and reliability of the wire winding process. Furthermore, by setting up an alarm in the human-machine interface when data is abnormal, online diagnosis of roller wear and early warning of bearing wear are achieved, preventing contact pressure attenuation. The process parameter self-tuning adaptation time is ≤3 minutes. In addition, the process parameters can adaptively adapt to different wire specifications. Attached Figure Description
[0022] Figure 1 The diagram shown is a block diagram of an intelligent control system for the rollers of a fuel rod winding device based on edge computing, provided in an embodiment of this application.
[0023] Figure 2 The diagram shown is a flowchart illustrating an intelligent control method for the rollers of a fuel rod winding device based on edge computing, according to an embodiment of this application.
[0024] Figure 3 The diagram shown is a flowchart illustrating an intelligent control method for the rollers of a fuel rod winding device based on edge computing, according to another embodiment of this application. Detailed Implementation
[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0026] At least one embodiment of this application provides an intelligent control system for the rollers of a fuel rod winding device based on edge computing, see reference. Figure 1This edge computing-based intelligent control system for the rollers of a fuel rod winding equipment includes a roller actuator, multimodal sensors, an edge computing controller, a human-machine interface (HMI), and an intelligent drive module. The multimodal sensors, embedded within the roller actuator and connected to the edge computing controller and HMI, transmit real-time measured data via a CAN bus to both the edge computing controller and the HMI. The edge computing controller, connected to the intelligent drive module and HMI, receives data from the multimodal sensors, performs calculations on the data, and outputs the calculated data to the intelligent drive module and HMI. The intelligent drive module, connected to the edge computing controller and roller actuator, converts the calculated data to drive the roller actuator. The HMI, transmitting data to the edge computing controller via TCP, allows browsing of the multimodal sensors, inputs process parameter settings into the sensors, and triggers alarms when data anomalies occur.
[0027] For example, the real-time data measured includes, but is not limited to, tension, temperature, and visual data. It should be noted that an edge computing controller can also be called an edge computing control unit.
[0028] The technical solution provided in this application embeds a multimodal sensor into the roller actuator and uses an edge computing controller to calculate the data transmitted by the multimodal sensor. The calculated data is then output to an intelligent drive module and a human-machine interface. The intelligent drive module converts the calculated data to drive the roller actuator, thus employing a collaborative design of traditional mechanical equipment and intelligent technology to achieve high-precision wire winding control and significantly improve the accuracy, efficiency, and reliability of the wire winding process. Furthermore, by setting up an alarm in the human-machine interface when data is abnormal, online diagnosis of roller wear and early warning of bearing wear are achieved to prevent contact pressure attenuation. The process parameter self-tuning adaptation time is ≤3 minutes. In addition, the process parameters can adaptively adapt to different wire specifications.
[0029] In at least one embodiment of this application, the edge computing controller includes an adaptive control algorithm and a digital twin simulation model. The digital twin model incorporates a feedforward-feedback composite control architecture. A human-machine interface is connected to the edge computing controller via TCP. The edge computing controller further performs Kalman filtering on data transmitted from multimodal sensors. After prediction and feedforward compensation calculations by the feedforward-feedback composite control architecture of the digital twin model, error calculation is performed. The calculated value is then subjected to adaptive fuzzy PID adjustment, and the adjusted output control quantity is updated to the digital twin parameters. If the error exceeds a threshold, the update continues; otherwise, the process terminates.
[0030] For example, adaptive fuzzy PID dynamically adjusts parameters based on the error and the rate of change of the error. The code for adaptive fuzzy PID is as follows: def tune_pid(Ku,Tu): Kp = 0.6*Ku,Ki = 0.5*Tu, Kd = 0.125*Tu return Kp,Ki,Kd It should be noted that in the feedforward-feedback composite control architecture, the feedforward channel predicts the diameter / temperature disturbance, improving the response speed by 80%; the feedback channel uses a variable universe of discourse fuzzy PID, with a steady-state error of <0.5%.
[0031] Tests have verified that the edge computing-based intelligent control system for the rollers of the fuel rod winding equipment can improve control performance, reducing winding tension fluctuation from ±15% to ±5%; and optimizing the standard deviation of pitch consistency from 0.2mm to 0.05mm.
[0032] In the above embodiments, by integrating an adaptive fuzzy PID algorithm, a feedforward compensation module, and a digital twin model into the edge computing controller, the data transmitted by the multimodal sensors is input into the digital twin model after Kalman filtering. The control quantity is generated by combining feedforward prediction and feedback adjustment, ultimately driving the roller actuator to achieve precise control of the winding tension and pitch. Furthermore, by constructing a feedforward-feedback composite control architecture, combined with digital twin model prediction and the adaptive fuzzy PID algorithm, the winding tension fluctuation is controlled within 5%, and the pitch consistency is improved to ±0.05mm.
[0033] In at least one embodiment of this application, the adaptive control algorithm in the edge computing controller adopts Formula 1.
[0034] Fff=0.15( )2+0.08( +0.02 Formula 1 In Formula 1, The rate of change of wire diameter. This is due to the temperature rise of the roller.
[0035] In at least one embodiment of this application, the digital twin simulation model in the edge computing controller is constructed by building a parameterized roller-linear model, real-time data mapping, and a reduced-order contact mechanics model for real-time calculation and dynamic parameter correction. Thus, by establishing a parameterized roller-linear model and a reduced-order contact mechanics model during the construction process of the digital twin simulation model, the model supports online self-calibration of model parameters, achieving an accuracy error of <2%, and realizing a lightweight digital twin.
[0036] In at least one embodiment of this application, the intelligent drive module includes a servo motor and an IGBT (Insulated Gate Bipolar Transistor) power module. The intelligent drive module generates six PWM signals through control commands, which drive the servo motor via the IGBT power circuit. The servo motor is installed inside the roller actuator. The intelligent drive module and the roller actuator transmit data to each other through the six PWM signals and an encoder. Thus, the control commands are converted into precise roller movements through the high-dynamic servo motor and IGBT power module.
[0037] In at least one embodiment of this application, the multimodal sensor includes a micro-resistivity sensor, a non-contact magnetoelastic torque sensor, and a temperature sensor. The micro-resistivity sensor is installed within the roller bearing housing in the roller actuator for pressure detection. The non-contact magnetoelastic torque sensor is installed at the end of the roller drive shaft in the roller actuator for torque detection. The temperature sensor is embedded in the roller surface in the roller actuator for temperature compensation. Thus, by embedding sensors for measuring pressure, torque, and temperature in a conventional roller, real-time acquisition and monitoring of contact pressure, friction torque, and temperature data are achieved while using a conventional mechanical structure, significantly improving the accuracy, efficiency, and reliability of the wire winding process.
[0038] In at least one embodiment of this application, the edge computing controller uses Formula 2 to calculate the data from the multimodal sensor.
[0039] Y=0.6*F_tension + 0.3*T_torque + 0.1 Formula 2.
[0040] In Formula 2, F_tension is the pressure detected by the micro-resistance sensor; T_torque is the torque detected by the non-contact magnetoelastic torque sensor. This is due to the temperature rise of the roller.
[0041] In the above embodiments, multi-source data fusion and real-time decision-making are achieved by using a weighted fusion formula (i.e., Formula 2).
[0042] In at least one embodiment of this application, the edge computing-based intelligent control system for the fuel rod winding equipment rollers further includes a standardized control interface module. This standardized control interface module supports integration with PLC and MES systems. Thus, by developing a standardized control interface module, seamless integration with existing PLC and MES systems is supported, ensuring rapid integration of the equipment with the factory's digital system.
[0043] At least one embodiment of this application also provides a method for intelligent control of rollers in a fuel rod winding device based on edge computing. The executing entity of this method can be an edge computing controller or processor, etc., in the edge computing-based intelligent control system for the fuel rod winding device rollers described above. The following example uses an edge computing controller as the executing entity for illustration. (Reference) Figure 2 The intelligent control method for the rollers of the fuel rod winding equipment based on edge computing includes the following steps S10 and S20.
[0044] Step S10: Receive data transmitted by the multimodal sensor embedded in the roller actuator.
[0045] For example, a multimodal sensor collects data and transmits it to an edge computing controller, which in turn receives the data transmitted by the multimodal sensor.
[0046] Step S20: Calculate the data transmitted by the multimodal sensor and output the calculated data to the intelligent drive module and the human-machine interface. The intelligent drive module is used to convert the calculated data to drive the roller actuator. The human-machine interface is used to browse the multimodal sensor, input process parameter settings into the multimodal sensor, and issue alarms when data is abnormal.
[0047] In at least one embodiment of this application, reference is made to Figure 3 The above step S20 includes: performing Kalman filtering on the data transmitted by the multimodal sensor; performing error calculation after prediction and feedforward compensation calculation by the feedforward-feedback composite control architecture of the digital twin model; performing adaptive fuzzy PID adjustment on the calculated value and updating the adjusted output control quantity to the digital twin parameters; if the error is greater than the threshold, the update continues, otherwise the process ends.
[0048] The edge computing-based intelligent control method for the rollers of the fuel rod winding equipment is a method embodiment corresponding to the edge computing-based intelligent control system for the rollers of the fuel rod winding equipment described in the above embodiments. It includes all the technical features of the edge computing-based intelligent control system for the rollers of the fuel rod winding equipment described in the above embodiments and can achieve the corresponding technical effects, which will not be repeated here.
[0049] It should be noted that the combination of the technical features in the embodiments of this application is not limited to the combination methods described in the embodiments of this application or the combination methods described in specific embodiments. All technical features described in this application can be freely combined or combined in any way, unless they contradict each other.
[0050] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the term "comprising" only indicates that it includes the explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.
[0051] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. An edge computing-based fuel rod wire-winding equipment roller intelligent regulation system, characterized in that, The edge computing controller is connected with the intelligent driving module and the human-computer interaction interface, is used for receiving the data transmitted by the multi-modal sensor, and performs calculation on the data transmitted by the multi-modal sensor, and outputs the calculated data to the intelligent driving module and the human-computer interaction interface. The edge computing controller is connected with the intelligent driving module and the human-computer interaction interface, is used for receiving the data transmitted by the multi-modal sensor, and performs calculation on the data transmitted by the multi-modal sensor, and outputs the calculated data to the intelligent driving module and the human-computer interaction interface. The intelligent driving module is connected with the edge computing controller and the roller executing mechanism, is used for converting the calculated data to drive the roller executing mechanism to act. The human-computer interaction interface realizes data transmission with the edge computing controller through TCP, is used for realizing browsing of the multi-modal sensor, and setting process parameters into the multi-modal sensor, and alarming when data is abnormal. The edge computing controller includes an adaptive control algorithm and a digital twin simulation model, the digital twin model is provided with a feedforward-feedback composite control architecture, and the human-computer interaction interface is connected with the edge computing controller through TCP; the edge computing controller is also used for Kalman filtering on the data transmitted by the multi-modal sensor, error calculation after prediction and feedforward compensation calculation of the feedforward-feedback composite control architecture of the digital twin model, adaptive fuzzy PID adjustment on the calculated value, updating of the adjusted output control quantity to the digital twin parameter, and continuous updating if the error is greater than a threshold, and ending otherwise.
2. The edge computing-based fuel rod wire-winding equipment roller intelligent regulation system of claim 1, wherein The adaptive control algorithm in the edge computing controller adopts formula one, 3. The edge computing-based fuel rod wire-winding equipment roller intelligent regulation system of claim 2, wherein, The digital twin simulation model in the edge computing controller is constructed by constructing a parameterized roller-linear model, real-time data mapping, and constructing a reduced-order contact mechanics model for real-time calculation, dynamic parameter correction, and construction. Fff = 0.15 )2+0.08( )+0.02 Formula One In formula one, is the wire diameter change rate, is the roller temperature rise.
4. The edge computing-based fuel rod wire-winding equipment roller intelligent regulation system of claim 1, wherein, The intelligent driving module includes a servo motor and an IGBT power module, generates 6 PWM signals through control instructions, drives the servo motor through the IGBT power circuit, and is installed in the roller executing mechanism; the intelligent driving module and the roller executing mechanism realize mutual data transmission through 6 PWM signals and an encoder.
5. The edge computing-based fuel rod wire-winding equipment roller intelligent regulation system of claim 1, wherein, The multi-modal sensor includes a micro-resistance sensor, a non-contact magnetic elastomer torque sensor, and a temperature sensor, wherein the micro-resistance sensor is installed in the roller bearing seat in the roller executing mechanism and is used for pressure detection; the non-contact magnetic elastomer torque sensor is installed at the end of the roller driving shaft in the roller executing mechanism and is used for torque detection; and the temperature sensor is embedded in the roller surface in the roller executing mechanism and is used for temperature compensation.
6. The edge computing-based fuel rod wire-winding equipment roller intelligent regulation system of claim 1, wherein The edge computing controller calculates the data of the multi-modal sensor by formula two, 7. The edge computing-based fuel rod wire-winding equipment roller intelligent regulation system of claim 6, wherein, The system further includes a standardized control interface module, wherein the standardized control interface module is used for supporting integration with PLC and MES system. Y = 0.6 * F_tension + 0.3 * T_torque + 0.1 Equation Two In the formula two, F tension is the pressure detected by the micro-resistance sensor; T torque is the torque detected by the non-contact magnetic elastomeric torque sensor; is the roller temperature rise.
8. The edge computing-based fuel rod wire-winding equipment roller intelligent regulation system according to any one of claims 1 to 7, characterized in that, The system further includes a standardized control interface module, wherein the standardized control interface module is used for supporting integration with PLC and MES system.
9. An edge computing-based fuel rod wire-winding equipment roller intelligent regulation method, characterized in that, Step S10, receiving data transmitted by the multi-modal sensor embedded in the roller execution mechanism; Step S20, calculating the data transmitted by the multi-modal sensor, and outputting the calculated data to an intelligent driving module and a human-computer interaction interface, the intelligent driving module being used to transform the calculated data to drive the roller execution mechanism to act, and the human-computer interaction interface being used to realize the browsing of the multi-modal sensor, to transmit the setting of the process parameters into the multi-modal sensor, and to alarm when the data is abnormal.
10. The edge computing-based fuel rod wire-winding equipment roller intelligent regulation system of claim 9, wherein, The step S20 comprises: The data transmitted by the multi-modal sensor is subjected to Kalman filtering, error calculation is performed after the prediction and feedforward compensation calculation of the feedforward-feedback composite control architecture of the digital twin model, the self-adaptive fuzzy PID adjustment is performed on the calculated value, the updated output control quantity after the adjustment is updated to the digital twin parameter, and if the error is greater than the threshold, the updating is continuously performed, otherwise the updating is ended.