Cold-rolling mill front tensioning roller intelligent lubricating and cooling structure based on pressure feedback and working method of cold-rolling mill front tensioning roller intelligent lubricating and cooling structure

Abstract

The invention discloses a cold-rolling mill front tensioning roller intelligent lubricating and cooling structure based on pressure feedback and a working method thereof, and relates to the technical field of cold-rolling mill equipment, the cold-rolling mill front tensioning roller intelligent lubricating and cooling structure comprises a pressure detection module, a lubricating module, a cooling module, a control module, a temperature detection module and an alarm module; the pressure detection module collects pressure signals of a front tensioning roller bearing seat in real time, and the temperature detection module collects temperature signals of a bearing and a lubricating medium; the control module adopts a fuzzy PID (Proportion Integration Differentiation) intelligent control algorithm to analyze and process the pressure and temperature signals, and generates a control instruction to adjust the lubricating medium supply quantity of the lubricating module and the cooling medium supply quantity and temperature of the cooling module; the alarm module sends out an alarm signal when the parameters are abnormal. Self-adaptive precise control over lubricating and cooling is achieved, resource waste is reduced, the maintenance cost is reduced, the service life of equipment is prolonged, and the production efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of cold rolling mill equipment technology, and more specifically to an intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback. Background Technology

[0002] In the cold rolling mill production process, the pretensioning roll, as a key equipment component, is mainly used to control the tension of the strip steel, ensuring its smooth operation during rolling. Its working condition directly affects the rolling quality and production efficiency of the strip steel. Under high-speed rotation and high tension, the bearing of the pretensioning roll generates a large amount of frictional heat. At the same time, the friction between the roll surface and the strip steel also leads to an increase in temperature. If effective lubrication and cooling are not carried out in a timely manner, it will cause abnormal changes in the viscosity of the lubricating oil and a decrease in the oil film carrying capacity, which will lead to failures such as bearing wear and roll surface damage. In severe cases, it may even cause equipment shutdown, increase maintenance costs, and affect production progress.

[0003] Existing lubrication and cooling systems for the front tension rolls of cold rolling mills mostly employ fixed-parameter control, meaning they continuously supply lubricating and cooling media according to preset supply volumes and temperatures, failing to adaptively adjust to changes in the actual working pressure of the front tension rolls. However, during actual rolling, variations in parameters such as strip thickness, material, and rolling speed cause fluctuations in the tension on the front tension rolls, leading to changes in bearing pressure and consequently altering the roll's heating characteristics. When strip thickness increases or rolling speed rises, the tension on the front tension rolls increases, bearing pressure rises, and frictional heat generation increases significantly. In this case, fixed lubrication and cooling parameters cannot meet the heat dissipation and lubrication requirements, leading to overheating and wear of the equipment. Conversely, when strip thickness decreases or rolling speed decreases, tension and pressure decrease, resulting in less heat generation. Fixed lubrication and cooling parameters lead to waste of lubricating and cooling media and may also cause increased lubricant viscosity due to overcooling, affecting lubrication effectiveness.

[0004] Therefore, it is necessary to propose an intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to address the problem that fixed lubrication and cooling parameters can lead to waste of lubricating and cooling media, and that excessive cooling can increase the viscosity of the lubricating oil, thus affecting the lubrication effect. This invention provides an intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback.

[0006] To achieve the above objectives, the present invention specifically adopts the following technical solution: A pressure feedback-based intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill, comprising: The pressure detection module is used to collect the pressure signal of the front tension roll bearing seat of the cold rolling mill in real time; The lubrication module is used to deliver lubricating medium to the lubrication parts of the forward tension roller; The cooling module is used to deliver cooling medium to the heat-generating parts of the forward tensioning roller; The control module is electrically connected to the pressure detection module, lubrication module, and cooling module respectively. The control module has a built-in preset pressure threshold range and control algorithm. It is used to receive the pressure signal transmitted by the pressure detection module, compare it with the preset pressure threshold range, generate corresponding control commands through the control algorithm, and send them to the lubrication module and cooling module to adjust the supply amount and supply temperature of the lubricating medium and the cooling medium.

[0007] Furthermore, the pressure detection module includes at least two pressure sensors, symmetrically installed on both sides of the front tension roller bearing housing. The pressure sensors are piezoresistive sensors with a measurement range of 0-50 MPa.

[0008] Furthermore, the lubrication module includes a lubricating medium storage tank, a lubrication pump, a flow regulating valve, and a lubrication pipeline. The input end of the lubrication pump is connected to the lubricating medium storage tank, and the output end is connected to the lubrication part of the front tension roller through the lubrication pipeline. The flow regulating valve is installed on the lubrication pipeline and is electrically connected to the control module.

[0009] Furthermore, the cooling module includes a cooling medium storage tank, a cooling pump, a cooler, a temperature regulating valve, and cooling pipes. The input end of the cooling pump is connected to the cooling medium storage tank, and the output end is connected to the cooler and the temperature regulating valve in sequence. It is then connected to the heating part of the front tensioning roller through the cooling pipes. Both the cooler and the temperature regulating valve are electrically connected to the control module. The cooling medium is industrial pure water, and the cooler is a plate heat exchanger.

[0010] Furthermore, the control module adopts a Siemens S7-1200 series PLC, configured with an analog input module and an analog output module. The analog input module is used to receive the 4-20mA signal from the pressure sensor, and the analog output module is used to output a 0-10V control signal to the actuator. The control algorithm is a fuzzy PID control algorithm, which uses fuzzy logic to fuzzify the pressure deviation and the rate of change of deviation, generates fuzzy control rules, and dynamically adjusts the PID parameters.

[0011] Furthermore, it also includes a temperature detection module, which is a temperature sensor installed on the front tension roller bearing housing and the lubrication medium output end. It is used to collect bearing temperature and lubrication medium temperature signals in real time and transmit them to the control module. The control module combines the pressure signal and the temperature signal to generate control commands.

[0012] Furthermore, it also includes an alarm module, which is electrically connected to the control module. When the pressure signal collected by the pressure detection module exceeds the preset pressure threshold range, or the temperature signal collected by the temperature detection module exceeds the preset temperature threshold range, the control module controls the alarm module to issue an audible and visual alarm signal.

[0013] A method for operating a pressure feedback-based intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill, characterized by the following steps: S1: System initialization, the control module sets the preset pressure threshold range, preset temperature threshold range, parameters of the fuzzy PID control algorithm, and the initial lubricating medium supply, initial cooling medium supply, and supply temperature; S2: The pressure detection module collects the pressure signal of the front tension roller bearing seat in real time, and the temperature detection module collects the bearing temperature and lubricating medium temperature signals in real time, and transmits the collected signals to the control module. S3: The control module performs preprocessing such as filtering and amplification on the received pressure and temperature signals to remove interference signals; S4: The control module compares the pre-processed pressure signal with the preset pressure threshold range and the temperature signal with the preset temperature threshold range. It then uses a fuzzy PID control algorithm to analyze and calculate the pressure deviation, pressure deviation rate of change, temperature deviation, and temperature deviation rate of change, and generates lubrication adjustment commands and cooling adjustment commands. S5: The control module sends a lubrication adjustment command to the lubrication module to adjust the opening of the flow regulating valve, thereby changing the supply of lubricating medium; and sends a cooling adjustment command to the cooling module to adjust the operating power of the cooler and the opening of the temperature regulating valve, thereby changing the supply of cooling medium and the supply temperature. S6: Repeat steps S2-S5 to achieve real-time closed-loop intelligent control of lubrication and cooling of the front tension roll of the cold rolling mill; if the pressure signal or temperature signal exceeds the preset threshold range, the control module controls the alarm module to issue an alarm signal.

[0014] Furthermore, the specific implementation process of the fuzzy PID control algorithm in step S4 includes: S41: Determine the input and output variables. The input variables are pressure deviation e1, pressure deviation change rate ec1, temperature deviation e2, and temperature deviation change rate ec2. The output variables are the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller. S42: Fuzzyize the input and output variables, divide their universe of discourse into five fuzzy subsets: "negative large", "negative small", "zero", "positive small" and "positive large", and use triangular membership functions for fuzzy mapping; S43: Construct a fuzzy control rule base based on expert experience and actual field operation data; S44: The Mamdani inference algorithm is used to infer the fuzzy control rules and obtain the fuzzy set of output variables; S45: Use the centroid method to defuzzify the fuzzy set of output variables to obtain accurate Kp, Ki, and Kd parameter values, and update the parameters of the PID controller.

[0015] The beneficial effects of this invention are as follows: 1. This invention acquires the pressure signal of the front tension roll in real time through a pressure detection module, combines it with the temperature signal, and uses a fuzzy PID intelligent control algorithm to dynamically adjust the lubrication and cooling parameters. This achieves adaptive control of lubrication and cooling, accurately matching the lubrication and cooling needs of the front tension roll under different working conditions, effectively solving the problems of insufficient or excessive lubrication and cooling in traditional fixed parameter control methods. When changes in working conditions cause pressure and temperature fluctuations, the system can quickly respond and adjust the parameters to ensure the stability of the lubrication and cooling effect.

[0016] 2. This invention can monitor the pressure and temperature of the front tensioning roller in real time, and issue an alarm signal in a timely manner when an abnormality occurs, so as to facilitate timely maintenance by the staff, avoid the escalation of the fault, improve the operational reliability of the equipment, extend the service life of the front tensioning roller and related components, reduce downtime, and improve production efficiency. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please refer to Figure 1-2, a smart lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback, comprising: The pressure detection module is used to collect the pressure signal of the front tension roll bearing seat of the cold rolling mill in real time; The lubrication module is used to deliver lubricating medium to the lubrication parts of the forward tension roller; The cooling module is used to deliver cooling medium to the heat-generating parts of the forward tensioning roller; The control module is electrically connected to the pressure detection module, lubrication module, and cooling module respectively. The control module has a built-in preset pressure threshold range and control algorithm. It receives the pressure signal transmitted by the pressure detection module, compares it with the preset pressure threshold range, generates corresponding control commands through the control algorithm, and sends them to the lubrication module and cooling module to adjust the supply amount and supply temperature of the lubricating medium and the cooling medium.

[0020] The pressure detection module includes at least two pressure sensors, which are symmetrically installed on both sides of the front tension roller bearing housing. The pressure sensors are piezoresistive sensors with a measurement range of 0-50 MPa.

[0021] The lubrication module includes a lubricating medium storage tank, a lubrication pump, a flow regulating valve, and a lubrication pipeline. The input end of the lubrication pump is connected to the lubricating medium storage tank, and the output end is connected to the lubrication part of the front tension roller through the lubrication pipeline. The flow regulating valve is installed on the lubrication pipeline and is electrically connected to the control module.

[0022] The cooling module includes a cooling medium storage tank, a cooling pump, a cooler, a temperature regulating valve, and cooling pipes. The input end of the cooling pump is connected to the cooling medium storage tank, and the output end is connected to the cooler and the temperature regulating valve in sequence. It is then connected to the heating part of the front tensioning roller through the cooling pipes. Both the cooler and the temperature regulating valve are electrically connected to the control module. The cooling medium is industrial pure water, and the cooler is a plate heat exchanger.

[0023] The control module uses a Siemens S7-1200 series PLC, equipped with an analog input module and an analog output module. The analog input module is used to receive 4-20mA signals from the pressure sensor, and the analog output module is used to output 0-10V control signals to the actuator. The control algorithm is a fuzzy PID control algorithm, which uses fuzzy logic to fuzzify the pressure deviation and the rate of change of deviation, generate fuzzy control rules, and dynamically adjust the PID parameters.

[0024] It also includes a temperature detection module, which is a temperature sensor installed on the front tension roller bearing housing and the lubrication medium output end. It is used to collect bearing temperature and lubrication medium temperature signals in real time and transmit them to the control module. The control module combines the pressure signal and the temperature signal to generate control commands.

[0025] It also includes an alarm module, which is electrically connected to the control module. When the pressure signal collected by the pressure detection module exceeds the preset pressure threshold range, or the temperature signal collected by the temperature detection module exceeds the preset temperature threshold range, the control module controls the alarm module to issue an audible and visual alarm signal.

[0026] The working method of the intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback includes the following steps: S1: System initialization, the control module sets the preset pressure threshold range, preset temperature threshold range, parameters of the fuzzy PID control algorithm, and the initial lubricating medium supply, initial cooling medium supply, and supply temperature; S2: The pressure detection module collects the pressure signal of the front tension roller bearing seat in real time, and the temperature detection module collects the bearing temperature and lubricating medium temperature signals in real time, and transmits the collected signals to the control module. S3: The control module performs preprocessing such as filtering and amplification on the received pressure and temperature signals to remove interference signals; S4: The control module compares the pre-processed pressure signal with the preset pressure threshold range and the temperature signal with the preset temperature threshold range. It then uses a fuzzy PID control algorithm to analyze and calculate the pressure deviation, pressure deviation rate of change, temperature deviation, and temperature deviation rate of change, and generates lubrication adjustment commands and cooling adjustment commands. S5: The control module sends a lubrication adjustment command to the lubrication module to adjust the opening of the flow regulating valve, thereby changing the supply of lubricating medium; and sends a cooling adjustment command to the cooling module to adjust the operating power of the cooler and the opening of the temperature regulating valve, thereby changing the supply of cooling medium and the supply temperature. S6: Repeat steps S2-S5 to achieve real-time closed-loop intelligent control of lubrication and cooling of the front tension roll of the cold rolling mill; if the pressure signal or temperature signal exceeds the preset threshold range, the control module controls the alarm module to issue an alarm signal.

[0027] The specific implementation process of the fuzzy PID control algorithm in step S4 includes: S41: Determine the input and output variables. The input variables are pressure deviation e1, pressure deviation change rate ec1, temperature deviation e2, and temperature deviation change rate ec2. The output variables are the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller. S42: Fuzzyize the input and output variables, divide their universe of discourse into five fuzzy subsets: "negative large", "negative small", "zero", "positive small" and "positive large", and use triangular membership functions for fuzzy mapping; S43: Construct a fuzzy control rule base based on expert experience and actual field operation data; S44: The Mamdani inference algorithm is used to infer the fuzzy control rules and obtain the fuzzy set of output variables; S45: Use the centroid method to defuzzify the fuzzy set of output variables to obtain accurate Kp, Ki, and Kd parameter values, and update the parameters of the PID controller. Example

[0028] The pressure detection module employs two PT124G-210 piezoresistive pressure sensors, symmetrically mounted on both sides of the front tension roll bearing housing of the cold rolling mill. These sensors have a measurement range of 0-50 MPa, an accuracy class of 0.1, an output signal of 4-20 mA, and temperature compensation capabilities with a temperature coefficient of ±0.015% / ℃. They can operate stably within an ambient temperature range of -40℃ to 125℃. The sensor installation positions have been optimized to avoid areas of severe vibration and are encapsulated in stainless steel, providing excellent corrosion resistance and dustproofing. A sealing gasket is placed between the sensor and the bearing housing during installation to ensure a tight seal, and the sensors are further secured with brackets to minimize the impact of vibration on measurement accuracy.

[0029] The lubrication module includes a lubricating medium storage tank, a CB-B50 gear lubrication pump, a ZDLP-16 electric flow control valve, and stainless steel lubrication piping. The lubricating medium storage tank has a volume of 500L and is equipped with a level sensor to monitor the remaining amount of lubricating medium. The lubrication pump has a rated pressure of 2.5MPa and a rated flow rate of 50L / min. It is connected to a three-phase asynchronous motor with a power of 4kW via a coupling. The flow control valve has an adjustment range of 0-50L / min, a control signal of 0-10V, and is connected to the analog output terminal of the control module. The lubrication piping has a diameter of φ25mm, is made of stainless steel, and has a corrosion-resistant surface treatment. Flange connections are used at pipe joints to ensure reliable sealing.

[0030] The cooling module includes a cooling medium storage tank, an ISG50-160 pipeline cooling pump, a BR0.3-1.0 plate heat exchanger, a ZDLP-16 electric temperature regulating valve, and stainless steel cooling piping. The cooling pump has a rated pressure of 1.6 MPa, a rated flow rate of 25 m³ / h, and a motor power of 4 kW. The plate heat exchanger has a heat transfer area of ​​3 m², a heat transfer coefficient of 3000 W / (m²·℃), and uses stainless steel plates, providing excellent heat transfer performance and corrosion resistance. The temperature regulating valve has an adjustment range of 0-100℃, a control signal of 0-10V, and connects to the analog output terminal of the control module. The cooling piping has a diameter of φ50 mm, is made of stainless steel, and features a rational layout to reduce resistance loss. The cooling medium is industrial pure water with added anti-corrosion and antifreeze additives.

[0031] The control module uses a Siemens S7-1214C DC / DC / DC PLC, equipped with an SM 1231 analog input module (8 channels, 12-bit resolution) and an SM 1232 analog output module (4 channels, 12-bit resolution). The PLC's CPU has a clock speed of 100MHz and 1MB of memory, providing high-speed data processing capabilities and abundant communication interfaces, enabling communication with a host computer for remote monitoring and data management. The control module incorporates a fuzzy PID control algorithm, implemented through TIA Portal software.

[0032] The temperature detection module uses two PT100 platinum resistance temperature sensors, which are installed in the front tension roller bearing housing and the lubrication medium output pipeline, respectively. The measurement range is -20℃ to 200℃, the accuracy class is A, and the output signal is 4-20mA, which is connected to the analog input terminal of the control module.

[0033] The alarm module uses an LTE-1101 audible and visual alarm, operating at 220V AC, and is connected to the digital output of the control module. When the pressure signal exceeds the preset threshold range (e.g., 0.5MPa-10MPa) or the temperature signal exceeds the preset threshold range (e.g., 30℃-80℃), the PLC outputs a high-level signal, controlling the alarm to emit an audible and visual alarm signal. The alarm sound intensity is not less than 85dB, the alarm light is red, and the flashing frequency is 1Hz.

[0034] This embodiment also provides a method for intelligent lubrication and cooling of the front tension roll of a cold rolling mill based on pressure feedback, applied to the above system. The specific steps are as follows: S1: System initialization. Using TIA Portal software, set the preset pressure threshold range to 0.5MPa-10MPa and the preset temperature threshold range to 30℃-80℃ in the PLC. Set the initial parameters of the fuzzy PID control algorithm to Kp=5.0, Ki=0.1, Kd=0.5, the initial lubricating medium supply to 20L / min, the initial cooling medium supply to 10m³ / h, and the initial cooling medium supply temperature to 45℃.

[0035] S2: The pressure sensor and temperature sensor collect the pressure signal, bearing temperature signal and lubricating medium temperature signal of the front tension roller bearing seat in real time at a sampling frequency of 10Hz, and transmit the collected 4-20mA analog signal to the analog input module of the PLC.

[0036] S3: The PLC performs A / D conversion on the received analog signal, converting it into a digital signal. Then, it uses a moving average filtering algorithm to filter the digital signal with a filter window size of 5 to remove random interference from the signal. Simultaneously, the filtered signal is amplified to meet the calculation requirements of the control algorithm.

[0037] S4: The PLC compares the filtered and amplified pressure signal with the preset pressure threshold range, and calculates the pressure deviation e1 (e1 = actual pressure - preset upper pressure limit; if the actual pressure is lower than the preset lower pressure limit, then e1 = actual pressure - preset lower pressure limit) and the pressure deviation change rate ec1 (ec1 = current e1 - previous e1); it compares the temperature signal with the preset temperature threshold range, and calculates the temperature deviation e2 (e2 = actual temperature - preset upper temperature limit; if the actual temperature is lower than the preset lower temperature limit, then e2 = actual temperature - preset lower temperature limit) and the temperature deviation change rate ec2 (ec2 = current e2 - previous e2).

[0038] The fuzzy PID control algorithm is used to analyze and calculate e1, ec1, e2, and ec2. The specific process is as follows: S41: Determine that the universe of discourse of input variables e1, ec1, e2, and ec2 is [-10, 10], and the universe of discourse of output variables Kp, Ki, and Kd are [0, 10], [0, 1], and [0, 1], respectively; S42: Divide both input and output variables into five fuzzy subsets: “Negative Large (NB)”, “Negative Small (NS)”, “Zero (ZO)”, “Positive Small (PS)”, and “Positive Large (PB)”, and use triangular membership functions for fuzzy mapping; S43: Construct a fuzzy control rule base, some rules are as follows: Rule 1: If e1=PB, ec1=PS, e2=PB, ec2=PS, then Kp=PB, Ki=NS, Kd=PB; Rule 2: If e1=ZO, ec1=ZO, e2=ZO, ec2=ZO, then Kp=ZO, Ki=ZO, Kd=ZO; Rule 3: If e1=NB, ec1=NS, e2=NB, ec2=NS, then Kp=NB, Ki=PB, Kd=NB; S44: The Mamdani inference algorithm is used to infer the fuzzy control rules and obtain the fuzzy set of output variables Kp, Ki, and Kd; S45: Use the centroid method to defuzzify the fuzzy set of output variables to obtain accurate Kp, Ki, and Kd parameter values, and update the parameters of the PID controller.

[0039] S5: The PLC calculates lubrication and cooling adjustment commands based on the updated PID parameters. It then sends 0-10V control signals to the flow control valve and temperature control valve via the analog output module, and simultaneously sends control signals to the cooler's control terminal. For example, when the actual pressure is 12MPa (e1=PB), the pressure change rate is 2MPa / s (ec1=PS), the actual temperature is 85℃ (e2=PB), and the temperature change rate is 3℃ / s (ec2=PS), after calculation by the fuzzy PID algorithm, the outputs are Kp=8.5, Ki=0.05, and Kd=0.8. This controls the flow control valve to increase its opening to 80%, increasing the lubricating medium supply to 40L / min; it controls the cooler to increase its operating power to 90% of its rated power; and it controls the temperature control valve to increase its opening to 75%, increasing the cooling medium supply to 20m³ / h and decreasing the supply temperature to 35℃.

[0040] S6: Repeat steps S2-S5 to achieve real-time closed-loop control. If the PLC detects a pressure signal greater than 10MPa or less than 0.5MPa, or a temperature signal greater than 80℃ or less than 30℃, it immediately outputs a high-level signal to the audible and visual alarm. The alarm then emits an audible and visual alarm signal. At the same time, the PLC uploads the alarm information to the host computer, allowing staff to monitor the equipment status in real time and perform timely maintenance.

[0041] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback, characterized in that: include: The pressure detection module is used to collect the pressure signal of the front tension roll bearing seat of the cold rolling mill in real time; The lubrication module is used to deliver lubricating medium to the lubrication parts of the forward tension roller; The cooling module is used to deliver cooling medium to the heat-generating parts of the forward tensioning roller; The control module is electrically connected to the pressure detection module, lubrication module, and cooling module respectively. The control module has a built-in preset pressure threshold range and control algorithm. It is used to receive the pressure signal transmitted by the pressure detection module, compare it with the preset pressure threshold range, generate corresponding control commands through the control algorithm, and send them to the lubrication module and cooling module to adjust the supply amount and supply temperature of the lubricating medium and the cooling medium.

2. The intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback as described in claim 1, characterized in that: The pressure detection module includes at least two pressure sensors, which are symmetrically installed on both sides of the front tension roller bearing seat. The pressure sensors are piezoresistive sensors with a measurement range of 0-50 MPa.

3. The intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback as described in claim 1, characterized in that: The lubrication module includes a lubricating medium storage tank, a lubrication pump, a flow regulating valve, and a lubrication pipeline. The input end of the lubrication pump is connected to the lubricating medium storage tank, and the output end is connected to the lubrication part of the front tension roller through the lubrication pipeline. The flow regulating valve is installed on the lubrication pipeline and is electrically connected to the control module.

4. The intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback as described in claim 1, characterized in that: The cooling module includes a cooling medium storage tank, a cooling pump, a cooler, a temperature regulating valve, and cooling pipes. The input end of the cooling pump is connected to the cooling medium storage tank, and the output end is connected to the cooler and the temperature regulating valve in sequence. It is then connected to the heating part of the front tensioning roller through the cooling pipes. Both the cooler and the temperature regulating valve are electrically connected to the control module. The cooling medium is industrial pure water, and the cooler is a plate heat exchanger.

5. The intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback according to claim 1, characterized in that: The control module is equipped with an analog input module and an analog output module. The analog input module is used to receive the 4-20mA signal from the pressure sensor, and the analog output module is used to output a 0-10V control signal to the actuator. The control algorithm is a fuzzy PID control algorithm. This algorithm uses fuzzy logic to fuzzify the pressure deviation and the rate of change of deviation, generates fuzzy control rules, and dynamically adjusts the PID parameters.

6. The intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback according to claim 1, characterized in that: It also includes a temperature detection module, which is a temperature sensor installed on the front tension roller bearing housing and the lubrication medium output end. It is used to collect bearing temperature and lubrication medium temperature signals in real time and transmit them to the control module. The control module combines the pressure signal and the temperature signal to generate control commands.

7. The intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback according to claim 1, characterized in that: It also includes an alarm module, which is electrically connected to the control module. When the pressure signal collected by the pressure detection module exceeds the preset pressure threshold range, or the temperature signal collected by the temperature detection module exceeds the preset temperature threshold range, the control module controls the alarm module to issue an audible and visual alarm signal.

8. A system operation method based on any one of claims 1-7 according to claim 1, characterized in that, Includes the following steps: S1: System initialization, the control module sets the preset pressure threshold range, preset temperature threshold range, parameters of the fuzzy PID control algorithm, and the initial lubricating medium supply, initial cooling medium supply, and supply temperature; S2: The pressure detection module collects the pressure signal of the front tension roller bearing seat in real time, and the temperature detection module collects the bearing temperature and lubricating medium temperature signals in real time, and transmits the collected signals to the control module. S3: The control module performs preprocessing such as filtering and amplification on the received pressure and temperature signals to remove interference signals; S4: The control module compares the pre-processed pressure signal with the preset pressure threshold range and the temperature signal with the preset temperature threshold range. It then uses a fuzzy PID control algorithm to analyze and calculate the pressure deviation, pressure deviation rate of change, temperature deviation, and temperature deviation rate of change, and generates lubrication adjustment commands and cooling adjustment commands. S5: The control module sends a lubrication adjustment command to the lubrication module to adjust the opening of the flow regulating valve, thereby changing the supply of lubricating medium; and sends a cooling adjustment command to the cooling module to adjust the operating power of the cooler and the opening of the temperature regulating valve, thereby changing the supply of cooling medium and the supply temperature. S6: Repeat steps S2-S5 to achieve real-time closed-loop intelligent control of lubrication and cooling of the front tension roll of the cold rolling mill; if the pressure signal or temperature signal exceeds the preset threshold range, the control module controls the alarm module to issue an alarm signal.

9. The intelligent lubrication and cooling structure for the front tension roll of a cold rolling mill based on pressure feedback according to claim 1, characterized in that: According to the method described in claim 8, the specific implementation process of the fuzzy PID control algorithm in step S4 includes: S41: Determine the input and output variables. The input variables are pressure deviation e1, pressure deviation change rate ec1, temperature deviation e2, and temperature deviation change rate ec2. The output variables are the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller. S42: Fuzzyize the input and output variables, divide their universe of discourse into five fuzzy subsets: "negative large", "negative small", "zero", "positive small" and "positive large", and use triangular membership functions for fuzzy mapping; S43: Construct a fuzzy control rule base based on expert experience and actual field operation data; S44: The Mamdani inference algorithm is used to infer the fuzzy control rules and obtain the fuzzy set of output variables; S45: Use the centroid method to defuzzify the fuzzy set of output variables to obtain accurate Kp, Ki, and Kd parameter values, and update the parameters of the PID controller.

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