Roll bearing cleaning and preheating device and preheating method

Abstract

According to the roller bearing cleaning and preheating device and method, the roller bearing cleaning and preheating device comprises a shell, a supporting rod arranged in the shell and used for supporting a bearing and a plurality of heating rods evenly arranged in the shell, liquid in the shell is evenly heated through the evenly-arranged heating rods, and therefore the interior and the exterior of the bearing are evenly preheated. In the cleaning process, the pressure and temperature of cleaning oil are monitored in real time, and the cleaning effect is ensured. After cleaning, the concentration of residual liquid is detected, and a drying procedure is started if necessary. And then uniform heating is carried out, thermal stress is eliminated, and new lubricating oil is injected. In the whole process, the temperature distribution and the lubrication state of the bearing are continuously monitored, and the cleaning and preheating effects are ensured.

Description

Technical Field

[0001] The invention relates to the field of bearing cleaning, in particular to a roller bearing cleaning and preheating device and a preheating method. Background Art

[0002] During the use of the roller bearing, due to long-term high-speed operation, the lubricating oil inside the bearing will gradually deteriorate, resulting in a decrease in lubrication effect, and a large amount of impurities and pollutants will accumulate inside the bearing. These factors will accelerate the wear of the bearing, shorten its service life, and even cause early failure of the bearing. Therefore, it is very necessary to clean and preheat the roller bearing regularly.

[0003] However, there are some problems with traditional bearing cleaning methods. First, manual cleaning is inefficient and it is difficult to completely remove contaminants inside the bearing. Second, commonly used chemical cleaning agents may corrode the bearing surface and affect its performance. In addition, if the bearings are not fully dried and preheated after cleaning, the residual cleaning liquid may dilute the newly added lubricating oil and affect the lubrication effect. In response to these problems, it is urgent to develop an efficient, non-destructive and controllable roller bearing cleaning and preheating device. Summary of the invention

[0004] The purpose of the present invention is to solve the above problems and provide a roller bearing cleaning and preheating device and a preheating method.

[0005] The technical solution adopted by the present invention to solve the technical problem is:

[0006] A roller bearing cleaning and preheating device comprises a shell, a support rod arranged in the shell for supporting the bearing, and a plurality of heating rods evenly arranged in the shell, a cover plate is provided at the upper end of the shell, a door-shaped frame is provided on the cover plate, rollers matching the bearing are provided at the lower ends of the legs of the door-shaped frame, a vibration sensor is provided on one of the legs of the door-shaped frame, and a buffer structure is provided on the other leg of the door-shaped frame.

[0007] Furthermore, the buffer structure includes a sleeve arranged at the lower end of the door-shaped frame leg and a lifting rod arranged at the upper end of the roller mounting frame. The upper end of the lifting rod penetrates into the sleeve and slides in the sleeve. A compression spring is provided in the sleeve. The upper end of the compression spring contacts the inner wall of the sleeve, and the compression spring contacts the upper end of the lifting rod.

[0008] A method for cleaning and preheating a roller bearing, characterized in that the method comprises the following steps:

[0009] S101, obtaining a bearing, placing the bearing in a cleaning device, injecting a specific cleaning oil into the bearing, dissolving and removing the lubricating oil and impurities remaining in the bearing;

[0010] S102, during the cleaning process, obtaining the pressure and temperature data of the cleaning oil, and determining whether the pressure and temperature meet the preset standards, and if so, determining that the cleaning effect meets the standards;

[0011] S103, after the cleaning is completed, obtaining the concentration data of the residual liquid inside the bearing, comparing the concentration data with a preset threshold, and if the concentration data is higher than the preset threshold, starting a drying procedure to remove the residual liquid by a high-temperature airflow;

[0012] S104, gradually heating the bearing, obtaining temperature data inside and outside the bearing, calculating the temperature difference inside and outside the bearing, and determining whether the temperature difference is less than a preset value. If so, determining that the temperature field inside and outside the bearing is uniform;

[0013] S105, acquiring the bearing temperature data at multiple moments, determining whether the bearing temperature is stable and evenly distributed, and if so, determining that the thermal stress has been eliminated, starting the lubrication program, and injecting lubricating oil into the bearing;

[0014] S106, obtaining lubrication status data of the bearing, comparing the lubrication status data with a preset standard, determining whether the lubrication effect meets the standard, and if not, analyzing the cause, adjusting the amount or type of lubricating oil, and re-executing the lubrication procedure; and if the standard is still not met after adjustment, restarting the cleaning procedure;

[0015] S107, during the operation of the cleaning and preheating device, obtaining and recording data such as cleaning oil pressure, temperature, residual liquid concentration, bearing temperature and lubrication status, generating a data log, and storing the data log in association with the lubrication effect judgment result;

[0016] S108, analyzing the data log, optimizing the cleaning oil formula, heating parameters and lubrication procedures, updating the preset thresholds, cleaning oil selection, temperature control parameters and lubricating oil selection, and improving the performance of the cleaning and preheating device.

[0017] The beneficial effects of the present invention are:

[0018] 1. The present invention discloses a roller bearing cleaning and preheating device, comprising a shell, a support rod arranged in the shell for supporting the bearing, and a plurality of heating rods evenly arranged in the shell. The evenly arranged heating rods are used to evenly heat the liquid in the shell, thereby evenly preheating the inside and outside of the bearing.

[0019] 2. The buffer structure of the present invention includes a sleeve arranged at the lower end of the leg of the door-shaped frame and a lifting rod arranged at the upper end of the roller mounting frame. The upper end of the lifting rod penetrates into the sleeve and slides in the sleeve. A compression spring is arranged in the sleeve. The upper end of the compression spring contacts the inner wall of the sleeve, and the compression spring contacts the upper end of the lifting rod. By setting the compression spring, the precision requirements of the processing can be reduced, and too much pressure will not be applied to the bearing. Secondly, the vibration generated when the corresponding roller rotates can be absorbed, and the signal collection of the vibration sensor 7) will not be interfered.

[0020] 3. The present invention discloses a method for cleaning and preheating a roller bearing. During the cleaning process, the pressure and temperature of the cleaning oil are monitored in real time to ensure the cleaning effect. After cleaning, the concentration of the residual liquid is detected, and the drying program is started if necessary. Subsequently, uniform heating is performed to eliminate thermal stress, and new lubricating oil is injected. During the entire process, the temperature distribution and lubrication status of the bearing are continuously monitored to ensure the cleaning and preheating effects. If the lubrication effect is not up to standard, the system will automatically adjust or restart the cleaning program. The present invention also optimizes the cleaning oil formula, heating parameters and lubrication program through data analysis to improve the performance of the device. This method can not only effectively remove residues in the bearing, but also perform uniform preheating before the roller works, significantly reduce the impact of thermal stress, and improve the service life and work efficiency of the roller. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 It is a schematic diagram of the structure of the present invention;

[0023] Figure 2 It is a top view of the internal structure of the present invention;

[0024] Figure 3 It is a left side view of the cover plate of the present invention;

[0025] Figure 4 It is a cross-sectional view of the buffer structure of the present invention;

[0026] Figure 5 It is a flow chart of the present invention.

[0027] In the figure: housing 1, support rod 2, heating rod 3, cover plate 4, door-shaped frame 5, roller 6, vibration sensor 7, sleeve 8, lifting rod 9, compression spring 10. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] like Figures 1 to 3 As shown, a roller bearing cleaning and preheating device comprises a shell 1, a support rod 2 arranged in the shell 1 for supporting the bearing, and a plurality of heating rods 3 evenly arranged in the shell 1. The evenly arranged heating rods are used to evenly heat the liquid in the shell, thereby evenly preheating the inside and outside of the bearing. An end plate for positioning the axial position of the bearing, which is matched with the inner ring of the bearing, is provided at the end of the support rod 2. The end plate is connected to the threaded hole at the end of the support rod through a screw. A cover plate 4 is provided at the upper end of the shell 1. A door-shaped frame 5 is provided on the cover plate 4. A bracket is provided on the cover plate 4. A lifting hydraulic cylinder is provided on the bracket. The piston rod end of the lifting hydraulic cylinder is connected to the door-shaped frame 5, thereby driving the door-shaped frame 5 to rise and fall. The lower ends of the legs of the door-shaped frame 5 are provided with a The roller 6 is combined, and a vibration sensor 7 is provided on one of the legs of the gate-shaped frame 5. The vibration sensor 7 collects the vibration acceleration signal of the bearing seat, periodically collects the vibration acceleration signal, and uses a high-pass digital filter to extract the signal, performs discrete Fourier transform on the signal, obtains the signal spectrum, and calculates the center frequency of the signal spectrum. The bearing lubrication state characterization index is calculated by the difference between the center frequencies of the signal spectrum, and the mean is calculated to determine whether it is less than the threshold to alarm, and the leg where the vibration sensor 7 is located is directly above the bearing, and a buffer structure is provided on the other leg of the gate-shaped frame 5. The roller corresponding to the buffer mechanism is the driving wheel, which drives the bearing to rotate, so as to facilitate the vibration sensor to collect the vibration acceleration signal and determine the lubrication effect.

[0030] like Figure 4 As shown, the buffer structure includes a sleeve 8 arranged at the lower end of the leg of the door-shaped frame 5 and a lifting rod 9 arranged at the upper end of the roller 6 mounting frame. The upper end of the lifting rod 9 penetrates into the sleeve 8 and slides in the sleeve 8. A compression spring 10 is provided in the sleeve 8. The upper end of the compression spring 10 contacts the inner wall of the sleeve 8. The compression spring 10 contacts the upper end of the lifting rod 9. By providing the compression spring 10, firstly, the processing accuracy requirement can be reduced without applying too much pressure to the bearing. Secondly, the vibration generated when the corresponding roller rotates can be absorbed without interfering with the signal collection of the vibration sensor 7.

[0031] like Figure 5 As shown, a method for cleaning and preheating a roller bearing mainly comprises the following steps:

[0032] S101, obtaining a bearing, placing the bearing in a cleaning device, injecting a specific cleaning oil into the bearing, dissolving and removing the lubricating oil and impurities remaining in the bearing;

[0033] S102, during the cleaning process, obtaining the pressure and temperature data of the cleaning oil, and determining whether the pressure and temperature meet the preset standards, and if so, determining that the cleaning effect meets the standards;

[0034] S103, after the cleaning is completed, obtaining the concentration data of the residual liquid inside the bearing, comparing the concentration data with a preset threshold, and if the concentration data is higher than the preset threshold, starting a drying procedure to remove the residual liquid by a high-temperature airflow;

[0035] S104, gradually heating the bearing, obtaining temperature data inside and outside the bearing, calculating the temperature difference inside and outside the bearing, and determining whether the temperature difference is less than a preset value. If so, determining that the temperature field inside and outside the bearing is uniform;

[0036] S105, acquiring the bearing temperature data at multiple moments, determining whether the bearing temperature is stable and evenly distributed, and if so, determining that the thermal stress has been eliminated, starting the lubrication program, and injecting lubricating oil into the bearing;

[0037] S106, obtaining lubrication status data of the bearing, comparing the lubrication status data with a preset standard, determining whether the lubrication effect meets the standard, and if not, analyzing the cause, adjusting the amount or type of lubricating oil, and re-executing the lubrication procedure; and if the standard is still not met after adjustment, restarting the cleaning procedure;

[0038] S107, during the operation of the cleaning and preheating device, obtaining and recording data such as cleaning oil pressure, temperature, residual liquid concentration, bearing temperature and lubrication status, generating a data log, and storing the data log in association with the lubrication effect judgment result;

[0039] S108, analyzing the data log, optimizing the cleaning oil formula, heating parameters and lubrication procedures, updating the preset thresholds, cleaning oil selection, temperature control parameters and lubricating oil selection, and improving the performance of the cleaning and preheating device.

[0040] Step S101 mainly includes: obtaining the roller bearing to be cleaned, and automatically determining the specific model and size parameters of the bearing through the bearing model identification system; matching the optimal cleaning oil type and cleaning parameters from a preset cleaning oil database according to the bearing model and size parameters, and the cleaning parameters include cleaning oil temperature, pressure and flow; placing the bearing in a high-temperature and high-pressure cleaning device, and the cleaning device automatically adjusts and stabilizes in an optimal cleaning state according to the optimal cleaning parameters; injecting the optimal type of cleaning oil into the bearing, and utilizing the high-temperature and high-pressure characteristics of the cleaning oil to fully dissolve and remove the residual lubricating oil and impurities in the bearing; and real-time monitoring through a contamination sensor arranged in the cleaning device. The contamination degree of the cleaning oil is measured. When the contamination degree reaches a preset threshold, the cleaning process is automatically stopped. After cleaning, the drying function of the cleaning device is started, and the cleaning oil and moisture remaining inside the bearing are evaporated and discharged by high-temperature airflow. After drying, the cleaning device automatically switches to the preheating mode, and gradually heats the bearing through a precise temperature control system to make its internal and external temperature fields uniform. During the preheating process, the temperature distribution of various parts of the bearing is monitored in real time through a temperature sensor, and when the temperature difference is less than a preset value, the preheating is judged to be completed. The preheated bearing is taken out of the cleaning device, and its cleaning and preheating effects are evaluated through a bearing performance detection system, and the evaluation result data is uploaded to the bearing management database.

[0041] The bearing model identification system is an advanced automated equipment that can quickly and accurately identify bearing models and dimensional parameters through optical scanning, image processing and machine learning algorithms. For a common cylindrical roller bearing, the system can identify its key dimensions such as inner diameter, outer diameter, width, and match a specific model such as NU1020M. The cleaning oil database contains the optimal cleaning solutions for various types of bearings. Taking the NU1020M bearing as an example, the database may recommend the use of a low-viscosity synthetic ester-based cleaning oil with a cleaning temperature of 60°C, a pressure of 2MPa, and a flow rate of 20L / min. The selection of these parameters takes into account factors such as bearing materials, structural characteristics, and degree of contamination to ensure cleaning results and bearing safety. The bearing model identification system and cleaning oil database are existing technologies and will not be elaborated on here.

[0042] The high temperature and high pressure characteristics of the cleaning oil can effectively dissolve and remove the contaminants inside the bearing. The high temperature can reduce the viscosity of the oil and improve its solubility; the high pressure can make the cleaning oil penetrate into the tiny gaps of the bearing and wash away the stubborn dirt. This method is more efficient than traditional immersion cleaning.

[0043] The drying function uses a hot air circulation system to evaporate the cleaning oil and water remaining inside the bearing. For example, the system may use 100°C hot air to blow through the bearing at a speed of 5m / s for 10 minutes to ensure that the bearing is completely dry. The purpose of the preheating mode is to make the bearing temperature uniform to avoid stress caused by temperature gradients during subsequent use.

[0044] The precise temperature control system may use infrared heating technology to gradually heat the bearing to 50°C. The temperature sensor monitors the temperature of each part of the bearing in real time. When the maximum temperature difference is less than 5°C, the preheating is considered complete. The bearing performance detection system evaluates the cleaning and preheating effects through vibration analysis, noise testing and other methods.

[0045] Through this series of precision control and automated processes, the efficiency and quality of bearing cleaning can be significantly improved, the bearing service life can be extended, and the equipment failure rate can be reduced. At the same time, real-time data collection and analysis provides a scientific basis for bearing maintenance, helps to achieve predictive maintenance, and reduces overall operating costs.

[0046] Step S102 mainly includes: obtaining the model and size information of the roller bearing, determining the flow and pressure parameters of the cleaning oil according to the model and size information, and inputting the flow and pressure parameters into the control system of the cleaning device; starting the cleaning device, heating the cleaning oil to a preset temperature, and obtaining real-time temperature and pressure data of the cleaning oil; if the real-time temperature and pressure data reach the set value, opening the cleaning oil circulation valve to allow the cleaning oil to enter the bearing under high temperature and high pressure to dissolve and remove pollutants; obtaining the temperature, pressure and flow data of the cleaning oil during the cleaning process through sensors, and transmitting the data to the control system; the control system determines whether the temperature and pressure of the cleaning oil continue to reach the preset standard according to the obtained temperature, pressure and flow data. If it is accurate, the cleaning process continues; if not, the temperature and pressure parameters of the cleaning oil are adjusted; after the cleaning process continues for a preset time, the cleaning oil circulation valve is closed, the cleaning oil supply is stopped, and the oil drain valve is opened to discharge the cleaning oil and pollutants inside the bearing; after the oil is drained, the oil drain valve is closed, the bearing preheating system is started, and the bearing is heated by the heating element to make the internal and external temperature fields uniform; during the preheating process, the temperature data of various parts of the bearing are obtained by the temperature sensor, and the temperature data is transmitted to the control system; the control system determines whether the bearing temperature field reaches the uniform standard based on the obtained temperature data, and if so, it is determined that the preheating is completed and the heating is stopped, if not, the preheating process is continued until the preset standard is reached.

[0047] The cleaning and preheating process of the roller bearing is a key step to ensure its performance and life. First of all, the bearing model and size information is obtained in order to accurately control the cleaning parameters. For a tapered roller bearing with an outer diameter of 200mm and an inner diameter of 100mm, the cleaning oil flow rate may need to be set to 50L / min and the pressure to 5MPa. These parameters directly affect the cleaning effect. Too low a flow rate may lead to incomplete cleaning, and too high a pressure may damage the bearing.

[0048] The control system of the cleaning device is the core of the whole process. It needs to monitor and adjust the temperature, pressure and flow of the cleaning oil in real time. Assuming the preset cleaning temperature is 80℃, the system will heat the cleaning oil to this temperature through the heating device. During the cleaning process, if the temperature drops below 75℃, the control system will automatically increase the heating power; if the pressure drops to 4.8MPa, the system will adjust the pump output accordingly.

[0049] The principle of high temperature and high pressure cleaning is to use the combined effect of heat energy and mechanical energy to improve the dissolution and penetration ability of the cleaning oil. For example, under the conditions of 80℃ and 5MPa, the cleaning oil can effectively dissolve the grease and carbide inside the bearing, and the high pressure can make the cleaning oil penetrate into the tiny gaps of the bearing, thereby achieving a deep cleaning effect.

[0050] The duration of the cleaning process usually depends on the size and degree of contamination of the bearing, and may be between 15-30 minutes. During this period, sensors continue to collect data, such as recording temperature, pressure and flow data every 5 seconds. This data is not only used for real-time control, but also for subsequent analysis and optimization of the cleaning process.

[0051] The purpose of the preheating system is to eliminate the temperature gradient inside and outside the bearing and prevent deformation caused by thermal stress. The heating element may use resistance heating or induction heating, and the temperature control accuracy is usually within ±1°C. For large bearings, multiple heating elements may be required to work together to ensure uniform heating.

[0052] The criterion for judging the uniformity of the temperature field may be that the temperature difference between the inside and outside of the bearing does not exceed 5°C. The arrangement of the temperature sensors is also critical. Usually, several sensors are installed on the inner ring, outer ring and rolling element of the bearing to comprehensively monitor the temperature distribution. The preheating process may last 10-20 minutes, depending on the size and material of the bearing. The automated control of the entire cleaning and preheating process not only improves efficiency, but also ensures the consistency and repeatability of the process. This is crucial to ensure the performance of the bearing and extend its service life. By precisely controlling each link, the errors caused by human factors can be minimized, and the cleanliness and reliability of the bearing can be improved.

[0053] Step S103 mainly includes: obtaining a residual liquid sample after the bearing is cleaned, measuring the concentration of pollutants in the sample by spectral analysis, and obtaining concentration data; comparing the concentration data with a preset pollutant concentration threshold to determine whether the residual liquid reaches a preset cleaning standard; if the concentration data is higher than the preset threshold, starting the bearing drying program; if the concentration data is lower than or equal to the preset threshold, proceeding to the next step; in the drying program, obtaining the bearing material characteristics and the residual liquid properties, and automatically adjusting the temperature of the high-temperature airflow used to purge the inside of the bearing according to the bearing material characteristics and the residual liquid properties; obtaining the inner and outer surface temperatures of the bearing by infrared temperature measurement technology. The bearing temperature data is obtained, and whether the bearing temperature field is uniform is determined according to the temperature data; the air flow temperature and flow rate parameters are dynamically adjusted according to the temperature data to optimize the drying process; the bearing internal humidity data is obtained through a humidity sensor, and whether the residual liquid is completely removed is determined according to the humidity data; when the internal humidity of the bearing drops to a preset value, the drying program is stopped and the bearing cooling program is started; in the cooling program, the bearing size and material properties are obtained, and the cooling time and cooling gas flow parameters are automatically calculated according to the bearing size and material properties, and the bearing is temperature-controlled and cooled by using cooling gas until the bearing temperature drops steadily to room temperature, completing the drying and cooling process of the bearing after cleaning;

[0054] The time required for bearing cooling is calculated as follows:

[0055]

[0056] t represents the cooling time, Q represents the amount of heat to be released, h represents the heat transfer coefficient, A represents the bearing surface area, and ΔT represents the temperature difference between the bearing and the cooling gas;

[0057] The gas flow rate calculation formula to achieve the required cooling effect is as follows:

[0058]

[0059] represents the cooling gas mass flow rate, Q represents the heat to be removed, and ΔH represents the specific enthalpy change of the cooling gas.

[0060] Quality control of bearings after cleaning is a critical step to ensure their performance and life. First, the concentration of contaminants in the residual liquid sample is measured using spectroscopic analysis. This method can accurately detect trace contaminants such as metal particles, grease, etc. For example, for a cylindrical roller bearing with a diameter of 200 mm, the contaminant concentration threshold may be set at 10 ppm. If the measured concentration is 15 ppm, the drying process needs to be started.

[0061] The core of the drying process is to adjust the high-temperature airflow according to the characteristics of the bearing material and the properties of the residual liquid. Taking 52100 bearing steel as an example, its thermal expansion coefficient is about 11.9×10^-6 / ℃. Taking this characteristic into account, the initial airflow temperature can be set to 80℃ to avoid bearing deformation. At the same time, if the residual liquid is a water-based cleaning agent with a low boiling point, the airflow temperature can be gradually increased to 110℃ to accelerate the evaporation process.

[0062] Infrared temperature measurement technology plays an important role in the drying process. Through multi-point temperature measurement, the temperature distribution of the inner and outer surfaces of the bearing can be monitored in real time. For example, for a bearing with an outer diameter of 300mm, 8 temperature measurement points can be evenly arranged on its surface. If it is found that the temperature at a certain point is too high, such as reaching 130℃ while other points are only 100℃, the airflow direction needs to be adjusted to ensure the uniformity of the temperature field.

[0063] The application of humidity sensors further optimizes the drying process. When the relative humidity inside the bearing drops below 20%, it can be considered that the residual liquid has been basically removed. At this time, the air flow temperature can be gradually reduced to 60°C to prepare for the cooling process.

[0064] This precise control can prevent stress concentration or micro cracks in the bearings due to rapid cooling. The automated control of the entire cleaning, drying and cooling process not only improves efficiency, but also ensures the consistency and reliability of bearing processing. Through real-time data collection and analysis, the system can perform personalized processing according to the characteristics of different bearings, maximizing the cleanliness and integrity of the bearings and laying a solid foundation for their subsequent use.

[0065] Step S104 mainly includes: determining the temperature measurement point positions of the inner and outer rings of the bearing according to the model and size parameters of the bearing, installing temperature sensors at the temperature measurement points, and obtaining real-time temperature data of the inner and outer rings of the bearing; transmitting the temperature data of the inner and outer rings of the bearing to a data processing unit, filtering the temperature data using a data processing algorithm, and obtaining filtered temperature data; calculating the temperature difference between the inner and outer rings of the bearing according to the filtered temperature data, and establishing a relationship model between the temperature difference and the uniformity of the temperature field inside and outside the bearing by a mathematical modeling method; judging whether the temperature difference is less than a preset temperature difference threshold, and if so, determining that the temperature field inside and outside the bearing has reached a uniform state at this time, and setting the temperature field inside the bearing uniform. The external temperature field state is marked as uniform; if the temperature difference is greater than the preset threshold, the heating device is controlled to continue heating the bearing, and the temperature changes of the inner and outer rings of the bearing are monitored in real time, and the heating power and heating time are dynamically adjusted according to the temperature change trend; during the bearing heating process, the temperature distribution inside the bearing is simulated and calculated using thermal simulation software, and the temperature cloud map of each point inside the bearing is obtained to provide real-time visual monitoring for the heating process; the temperature field uniformity evaluation index is used to quantitatively evaluate the uniformity of the temperature field inside and outside the bearing, and the numerical result of the temperature field uniformity is obtained, which provides a quantitative basis for judging whether the bearing has reached the ideal preheating state; the temperature field uniformity calculation formula is as follows:

[0066]

[0067] U represents the uniformity of the temperature field, Ti represents the temperature of the i-th measuring point, Tavg represents the average temperature of all measuring points, and n represents the total number of measuring points. This index reflects the degree of discreteness of the temperature distribution. The closer the value is to 1, the more uniform the temperature field is. According to the thermophysical property parameters of the bearing material, the thermal stress distribution of the bearing at different preheating temperatures is calculated by the finite element analysis method to determine the optimal bearing preheating temperature to avoid thermal deformation and thermal damage caused by excessive preheating temperature. The thermal stress calculation formula generated by the material under temperature changes is as follows:

[0068] σ th =αEΔT

[0069] σth represents thermal stress, α represents linear expansion coefficient, E represents elastic modulus, and ΔT represents temperature change.

[0070] Controlling the uniformity of the temperature field during the bearing preheating process is crucial to ensuring the performance and life of the bearing. First, determine the location of the temperature measurement points based on the bearing model and size parameters, which usually include key parts of the inner and outer rings. For example, for a medium-sized deep groove ball bearing, a temperature sensor can be installed on each side of the inner ring, and four sensors can be evenly distributed around the outer ring. The real-time temperature data collected by these sensors is filtered to effectively eliminate noise interference and improve data reliability.

[0071] Temperature difference calculation and mathematical modeling are the key to judging the uniformity of the temperature field. Assuming that the temperature difference threshold of the inner and outer rings is set to 5°C, when the actual temperature difference is less than this value, it can be considered that the temperature field has reached a uniform state. In actual operation, temperature difference fluctuations may be encountered. For example, in the initial heating stage, the temperature of the outer ring may rise rapidly to 80°C, while the inner ring only reaches 60°C. At this time, the heating power needs to be adjusted dynamically, and the power may be reduced by 30% to allow heat to be transferred to the inner ring and gradually achieve uniform heating.

[0072] The application of thermal simulation software provides visual support for the heating process. By constructing a three-dimensional model of the bearing and inputting material properties and boundary conditions, the temperature distribution of various parts of the bearing can be simulated. This method is particularly suitable for bearings with complex structures, such as tapered roller bearings, where the temperature distribution of the internal structure may vary greatly. The simulation results can guide the adjustment of the heating strategy. For example, if the temperature in a certain area of ​​the bearing is significantly lower, the heating intensity of that area can be increased accordingly.

[0073] The application of temperature field uniformity evaluation index makes the bearing preheating process more accurate and controllable. For example, when the calculated uniformity index U value is greater than 0.95, it can be considered that the bearing temperature distribution is quite uniform.

[0074] Through finite element analysis, the stress distribution of various parts of the bearing under different preheating temperatures can be further simulated to determine an optimal preheating temperature that can ensure uniform heating without causing excessive thermal stress. Usually, this temperature is slightly higher than the operating temperature of the bearing by 10-20°C. This method is especially important for precision bearings, because excessive thermal stress may cause slight deformation of the bearing ring, affecting its accuracy and service life.

[0075] Step S105 mainly includes: obtaining real-time temperature data of the roller bearing, the real-time temperature data is collected by multiple temperature sensors distributed at different positions of the bearing; preprocessing and filtering the real-time temperature data to remove noise and interference therein, and obtain stable and reliable bearing temperature measurement values; judging whether the bearing temperature measurement value reaches a stable and uniformly distributed state according to a preset temperature threshold and temperature uniformity index, and if so, determining that the bearing thermal stress has been eliminated; if the bearing temperature measurement value does not reach a stable and uniformly distributed state, adjusting the heating power and time of the bearing through a precise temperature control system until the bearing temperature meets the requirements; when it is determined that the bearing temperature is stable and uniform, controlling the lubricating oil injection device to inject an appropriate amount of lubricating oil into the bearing, and monitoring the bearing temperature and lubricating oil pressure in real time; through a closed-loop control algorithm, dynamically adjusting the lubricating oil injection rate and amount according to the monitoring values ​​of the bearing temperature and lubricating oil pressure, obtaining the bearing temperature and vibration data, judging whether the bearing lubrication state is normal through data analysis and pattern recognition, and generating a warning signal if an abnormality is found;

[0076] R(t)=R0exp(-β|T(t)-T0|-γ|P(t)-P0|)

[0077] R(t) represents the lubricant injection rate adjustment factor, R_0 represents the initial adjustment factor, T_0 and P_0 represent the ideal operating points of temperature and pressure respectively, β and γ represent the weight coefficients of temperature and pressure deviation respectively; this formula describes an adaptive adjustment strategy based on temperature and pressure deviation; according to the working status and usage time of the bearing, a machine learning algorithm is used to adaptively adjust the lubrication cycle and lubricant dosage, continuously optimize the lubrication strategy, and extend the service life of the bearing; the bearing temperature, vibration and lubrication related data are uploaded to the cloud platform, and through big data analysis and remote diagnosis technology, real-time monitoring and predictive maintenance of the health status of the roller bearing are realized.

[0078] Roller bearing temperature monitoring is a key link to ensure the stable operation of the rolling mill. By installing thermocouples, thermal resistors or infrared sensors at different positions of the bearing, the bearing temperature data can be collected in real time. For example, multiple temperature measurement points are arranged on the inner and outer rings and rolling elements of the bearing to form a temperature monitoring network. The raw data collected by these sensors often contain noise and interference, and need to be preprocessed and filtered. Common filtering methods include median filtering, Kalman filtering, etc., which can effectively remove sudden interference and random noise and improve the accuracy of temperature measurement.

[0079] The stability and uniformity of bearing temperature directly affect its performance and life. By setting the temperature threshold (such as not exceeding 80°C) and the temperature uniformity index (such as the maximum temperature difference not exceeding 5°C), it can be determined whether the bearing has reached the ideal thermal balance state. If it does not meet the standard, the precise temperature control system will dynamically adjust the heating power and time based on real-time temperature feedback. For example, using the PID control algorithm, when the temperature approaches the target value, the heating power is gradually reduced to avoid temperature overshoot.

[0080] After the bearing temperature stabilizes, the injection of lubricating oil is an important step to ensure its normal operation. The lubricating oil pressure is monitored by a pressure sensor, and closed-loop control can be achieved in combination with temperature data. For example, when the bearing temperature rises, the system will appropriately increase the lubricating oil flow rate to enhance heat dissipation; conversely, if the temperature is too low, the flow rate will be reduced to prevent overcooling. This dynamic adjustment strategy can maintain the bearing in the best working condition.

[0081] The health status monitoring of bearings not only depends on temperature, but also needs to be combined with vibration data for comprehensive analysis. By installing vibration sensors, the vibration signals of bearings can be collected. Using signal processing technologies such as fast Fourier transform (FFT), the characteristic frequencies of bearings can be identified from the vibration spectrum. If certain frequency components increase abnormally, it may indicate that the bearing is faulty, and the system will issue a warning signal in time.

[0082] Machine learning algorithms play an important role in optimizing bearing lubrication strategies. By collecting a large amount of historical data, including bearing temperature, vibration, lubricant consumption, etc., a predictive model can be trained. The model can automatically adjust the lubrication cycle and dosage according to the real-time working status of the bearing. For example, under high-load conditions, the system may recommend increasing the lubrication frequency; while under light-load operation, the lubrication interval is appropriately extended to ensure bearing performance and save lubricant consumption. Uploading bearing-related data to the cloud platform can achieve remote monitoring and predictive maintenance. Through big data analysis technology, early signs of bearing performance degradation can be mined from massive data. For example, by comparing the historical data of similar bearings, if the temperature rise rate of a bearing is found to be abnormal, even if the alarm threshold has not been reached, the system will issue maintenance recommendations in advance to effectively prevent unexpected downtime. This intelligent management method based on cloud computing not only improves equipment reliability, but also optimizes maintenance strategies and reduces operating costs.

[0083] Step S106 mainly includes: obtaining the real-time operating parameters of the bearing, the real-time operating parameters including speed, temperature and vibration data; judging whether the lubrication state of the bearing is normal according to the real-time operating parameters by using a preset data analysis algorithm; if the lubrication state of the bearing is abnormal, determining the specific cause of the lubrication abnormality according to a preset library of causes of poor lubrication by using a pattern matching algorithm; according to the specific cause, extracting the cleaning oil and contaminants from the bearing by using a vacuum suction technology, and drying the bearing by a hot air circulation system.

[0084] Real-time monitoring of the operating parameters of the bearing is the key to ensuring the normal operation of the equipment. The speed data can be collected by the Hall sensor, the temperature data can be obtained by the thermocouple or infrared thermometer, and the vibration data can be measured by the acceleration sensor. These data together reflect the working status of the bearing and provide a basis for subsequent analysis. The choice of data analysis algorithm directly affects the accuracy of the judgment result. Commonly used algorithms include time domain analysis, frequency domain analysis and time-frequency analysis. For example, by performing a fast Fourier transform on the vibration signal, a spectrum diagram can be obtained, and then the characteristic frequency of the bearing can be identified. If the amplitude of a certain characteristic frequency increases abnormally, it may mean that the bearing has poor lubrication. Pattern matching algorithms play an important role in determining the cause of lubrication abnormalities. A common method is to establish a fault feature library and compare the current bearing operating parameters with the patterns in the library. For example, if the temperature rises sharply and the vibration amplitude increases, it may be caused by insufficient lubricating oil. If the high-frequency vibration increases significantly, it may be caused by lubricating oil contamination. Vacuum suction technology is an efficient bearing cleaning method. By creating a negative pressure environment around the bearing, impurities and contaminants in the lubricating oil can be effectively removed. This process not only removes visible contaminants, but also extracts tiny particles and moisture, creating good conditions for bearing relubrication. The hot air circulation system plays a key role in the bearing drying process. By controlling the hot air temperature, wind speed and cycle time, it can ensure that all parts of the bearing are dried evenly. For example, for large bearings, a staged heating method can be used to dry the outer ring first and then gradually advance to the inner ring to avoid thermal stress caused by excessive temperature gradients.

[0085] Step S107 mainly includes: determining the type and amount of the cleaning oil, as well as the temperature and pressure range during the cleaning process according to the model and size parameters of the roller bearing; collecting the pressure and temperature data of the cleaning oil in real time through a sensor, and transmitting the data to a data acquisition module for processing and storage; measuring the concentration of the residual liquid in the cleaning oil with an optical concentration sensor to obtain residual liquid concentration data; obtaining the surface temperature data of the bearing through an infrared thermometer to determine whether the bearing temperature is uniform; controlling the heating device to accurately control the temperature of the bearing according to the bearing temperature data and a preset temperature threshold to eliminate thermal stress ; Use a vibration sensor to monitor the vibration signal of the bearing, and determine the lubrication status of the bearing through spectrum analysis; Aggregate the cleaning oil pressure, temperature, residual liquid concentration, bearing temperature and lubrication status data to generate a data log, and store the data log in a database; Analyze the data log through a data analysis algorithm to obtain a quality assessment result of the bearing cleaning and preheating process; Based on the quality assessment result, determine whether the bearing has achieved the expected cleaning and preheating effect. If the expected effect is not achieved, adjust the cleaning oil parameters and temperature control strategy, and re-perform the cleaning and preheating process until a satisfactory effect is achieved.

[0086] Real-time data acquisition is essential for process monitoring. The pressure sensor and temperature sensor can collect the pressure and temperature data of the cleaning oil respectively, with a sampling frequency of 10Hz, and the data is transmitted to the data acquisition module via industrial Ethernet. The optical concentration sensor uses the principle of light scattering to measure the concentration of residual liquid in the cleaning oil. When the concentration is less than 0.1%, it can be considered that the cleaning is complete. The uniformity of the bearing surface temperature directly affects its performance. The infrared thermometer can scan the bearing surface and generate a temperature distribution map. If the difference between the highest and lowest temperatures exceeds 5°C, the heating strategy needs to be adjusted. Accurate temperature control can be achieved through the PID control algorithm. The heating power is adjusted in real time according to the temperature feedback to stabilize the bearing temperature within the target value ±1°C. Vibration signal analysis is an effective method to evaluate the lubrication status of the bearing. The vibration signal is collected by the acceleration sensor, and the spectrum is obtained by fast Fourier transform. If an abnormal peak appears at the characteristic frequency, it may indicate insufficient lubrication or the presence of impurities.

[0087] The generation and analysis of data logs provide a basis for optimizing the cleaning and preheating process. The various parameters are integrated by timestamp and stored in a relational database. Through data mining algorithms, such as cluster analysis, potential correlations between parameters can be discovered. For example, it may be found that the cleaning oil temperature is negatively correlated with the residual liquid concentration, based on which the temperature control strategy can be optimized. The quality assessment results determine whether the process parameters need to be adjusted. If the bearing surface cleanliness does not meet the standard, the cleaning time can be appropriately extended or the cleaning oil pressure can be increased. If the preheating effect is not good, the heating power curve can be adjusted to achieve a more uniform heat distribution. Through this closed-loop optimization, the cleaning and preheating effect is continuously improved to ensure that the bearing is put into use in the best condition, extend its service life, and improve production efficiency.

[0088] Step S108 mainly includes: obtaining the operation data of the roller bearing cleaning and preheating device, the operation data including the cleaning oil temperature, pressure, flow rate and cleaning time parameters, and the temperature distribution and vibration frequency state data of the bearing; preprocessing the operation data, eliminating abnormal values ​​and noise data, and converting data of different dimensions into a unified scale through data normalization and standardization processing; establishing a mathematical model between the cleaning oil formula and heating parameters and the bearing cleaning quality based on the preprocessed data, and using a machine learning algorithm to train and optimize the parameters of the mathematical model; applying the optimized mathematical model to the roller bearing cleaning and preheating process, monitoring various parameters in real time, and dynamically adjusting the cleaning oil formula and heating parameters according to the prediction results of the mathematical model to optimize the cleaning effect; obtaining the internal data after the bearing cleaning is completed. The external temperature field distribution data is used to calculate the thermal stress distribution inside the bearing through finite element analysis; if there is thermal stress concentration or excessive temperature gradient, the heating curve and holding time are adjusted according to the finite element analysis results to make the temperature field inside and outside the bearing uniform and eliminate thermal stress; the cleaning fluid remaining on the bearing surface is detected, and the residual amount is determined by infrared spectroscopy or gas chromatography to determine whether it is lower than the preset threshold; according to the material, size and operating conditions of the bearing, the most suitable lubricant model and viscosity are selected from the lubricant database, and the lubricant is accurately injected into the bearing through the automated lubrication system; a knowledge base for the cleaning and preheating device of the rolling mill bearing is established to record the optimal cleaning oil formula, heating parameters and lubrication schemes for various types of bearings, and the knowledge base content is continuously updated and optimized according to the actual use effect to improve the intelligence level of the device.

[0089] The operation data collection of the roller bearing cleaning and preheating device is the key to optimizing the cleaning effect. Taking the cleaning oil temperature as an example, it can be monitored in real time by thermocouples or infrared thermometers, and the temperature range is usually between 40-80°C. Pressure data can be obtained through a pressure sensor, which is generally controlled at 0.5-2MPa. The flow rate can be measured by a turbine flowmeter, which is adjusted according to the bearing size. The common flow rate is 50-200L / min. The cleaning time parameter is usually set to 15-30 minutes, depending on the degree of bearing contamination. The acquisition of bearing temperature distribution data is crucial to evaluating the uniformity of cleaning. A thermal imager can be used to scan the bearing surface to generate a temperature distribution heat map. Ideally, the temperature difference should be controlled within ±5°C.

[0090] The vibration frequency status data is collected by the acceleration sensor, and the vibration characteristic frequency is analyzed by fast Fourier transform (FFT) to determine the operating status of the bearing after cleaning. In the data preprocessing stage, the 3σ criterion can be used to eliminate outliers, and data points that exceed the mean ±3 times the standard deviation are considered outliers. Noise data can be filtered out by methods such as sliding average or wavelet transform. The min-max method is commonly used for data normalization to map data of different dimensions to the [0,1] interval to facilitate subsequent modeling and analysis.

[0091] When establishing a mathematical model of cleaning oil formula, heating parameters and bearing cleaning quality, you can consider using algorithms such as multiple linear regression or support vector machine (SVM). Taking multiple linear regression as an example, the cleaning oil temperature, pressure, flow rate, etc. can be used as independent variables, and the bearing cleanliness can be used as the dependent variable, and the model parameters can be estimated by the least squares method.

[0092] During the model optimization process, the cross-validation method can be used to evaluate the model performance, and the model generalization ability can be improved by adjusting the hyperparameters (such as the regularization coefficient). The optimized model can predict the cleaning effect in real time and dynamically adjust the parameters according to the prediction results. For example, if the predicted cleanliness is insufficient, the cleaning oil temperature can be appropriately increased or the cleaning time can be extended.

[0093] Finite element analysis of the temperature field distribution data inside and outside the bearing can be performed using software such as ANSYS. By setting the bearing geometry model, material properties and boundary conditions, the heat conduction process is simulated, and the temperature distribution and thermal stress are calculated. If stress concentration areas are found, the temperature field can be made more uniform by adjusting the heating curve, such as reducing the heating rate or increasing the holding time.

[0094] The detection of residual cleaning fluid on the bearing surface can be done by Fourier transform infrared spectroscopy (FTIR). By comparing the spectra before and after cleaning, the residual content can be quantitatively analyzed. If the residual amount exceeds the preset threshold (such as 0.1 mg / cm 2 ), you need to extend the cleaning time or adjust the cleaning parameters.

[0095] When selecting lubricants, suitable models can be selected from the database based on parameters such as bearing size, speed and operating temperature. For example, low-viscosity synthetic oil can be used for high-speed bearings, while high-viscosity mineral oil is suitable for heavy-duty bearings. The automated lubrication system can inject lubricants into the bearings through a precision metering pump to ensure accurate dosage. The establishment and updating of the knowledge base is an important means to improve the intelligence level of the device. A relational database can be used to store the optimal parameters of each type of bearing, and combined with machine learning algorithms such as decision trees or random forests, optimization suggestions can be automatically generated based on historical data to continuously improve the cleaning and preheating plan.

[0096] In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "left", "right", "up", "down", etc. 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, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0097] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection, it can be a direct connection, it can be an indirect connection through an intermediate medium, and it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

Claims

1. A roller bearing cleaning and preheating device, characterized in that: The invention comprises a shell (1), a support rod (2) arranged in the shell (1) for supporting a bearing, and a plurality of heating rods (3) evenly arranged in the shell (1); a cover plate (4) is arranged at the upper end of the shell (1); a door-shaped frame (5) is arranged on the cover plate (4); rollers (6) cooperating with the bearing are arranged at the lower ends of the legs of the door-shaped frame (5); a vibration sensor (7) is arranged on one leg of the door-shaped frame (5); and a buffer structure is arranged on the other leg of the door-shaped frame (5).

2. A roller bearing cleaning and preheating device as claimed in claim 1, characterized in that: The buffer structure comprises a sleeve (8) arranged at the lower end of the leg of the door-shaped frame (5) and a lifting rod (9) arranged at the upper end of the roller (6) mounting frame, the upper end of the lifting rod (9) penetrates into the sleeve (8) and slides in the sleeve (8), a compression spring (10) is arranged in the sleeve (8), the upper end of the compression spring (10) contacts the inner wall of the sleeve (8), and the compression spring (10) contacts the upper end of the lifting rod (9).

3. A method for cleaning and preheating a roller bearing, characterized in that: The method comprises the following steps: S101, obtaining a bearing, placing the bearing in a cleaning device, injecting a specific cleaning oil into the bearing, dissolving and removing the lubricating oil and impurities remaining in the bearing; S102, during the cleaning process, obtaining the pressure and temperature data of the cleaning oil, and determining whether the pressure and temperature meet the preset standards, and if so, determining that the cleaning effect meets the standards; S103, after the cleaning is completed, obtaining the concentration data of the residual liquid inside the bearing, comparing the concentration data with a preset threshold, and if the concentration data is higher than the preset threshold, starting a drying procedure to remove the residual liquid by a high-temperature airflow; S104, gradually heating the bearing, obtaining temperature data inside and outside the bearing, calculating the temperature difference inside and outside the bearing, and determining whether the temperature difference is less than a preset value. If so, determining that the temperature field inside and outside the bearing is uniform; S105, acquiring the bearing temperature data at multiple moments, determining whether the bearing temperature is stable and evenly distributed, and if so, determining that the thermal stress has been eliminated, starting the lubrication program, and injecting lubricating oil into the bearing; S106, obtaining lubrication status data of the bearing, comparing the lubrication status data with a preset standard, determining whether the lubrication effect meets the standard, and if not, analyzing the cause, adjusting the amount or type of lubricating oil, and re-executing the lubrication procedure; and if the standard is still not met after adjustment, restarting the cleaning procedure; S107, during the operation of the cleaning and preheating device, obtaining and recording data such as cleaning oil pressure, temperature, residual liquid concentration, bearing temperature and lubrication status, generating a data log, and storing the data log in association with the lubrication effect judgment result; S108, analyzing the data log, optimizing the cleaning oil formula, heating parameters and lubrication procedures, updating the preset thresholds, cleaning oil selection, temperature control parameters and lubricating oil selection, and improving the performance of the cleaning and preheating device.

4. A method for cleaning and preheating a roller bearing as claimed in claim 3, characterized in that: Step S101 mainly includes: Obtaining a roller bearing to be cleaned, and automatically determining the specific model and size parameters of the bearing through a bearing model identification system; According to the bearing model and size parameters, the optimal cleaning oil type and cleaning parameters are matched from a preset cleaning oil database, wherein the cleaning parameters include cleaning oil temperature, pressure and flow rate; Placing the bearing in a high-temperature and high-pressure cleaning device, wherein the cleaning device automatically adjusts according to the optimal cleaning parameters and stabilizes in an optimal cleaning state; Injecting the optimal type of cleaning oil into the bearing, and utilizing the high temperature and high pressure characteristics of the cleaning oil to fully dissolve and remove the lubricating oil and impurities remaining in the bearing; The contamination level of the cleaning oil is monitored in real time by a contamination sensor disposed in the cleaning device, and the cleaning process is automatically stopped when the contamination level reaches a preset threshold; After cleaning is completed, the drying function of the cleaning device is activated to evaporate and discharge the cleaning oil and water remaining inside the bearing by using high-temperature airflow; After drying is completed, the cleaning device automatically switches to the preheating mode and gradually heats the bearing through a precise temperature control system to make the internal and external temperature fields uniform. During the preheating process, the temperature distribution of each part of the bearing is monitored in real time by a temperature sensor, and when the temperature difference is less than a preset value, the preheating is judged to be completed; The preheated bearing is taken out from the cleaning device, and its cleaning and preheating effects are evaluated by the bearing performance detection system, and the evaluation result data is uploaded to the bearing management database.

5. A method for cleaning and preheating a roller bearing as claimed in claim 3, characterized in that: Step S102 mainly includes: Obtaining the model and size information of the roller bearing, determining the flow and pressure parameters of the cleaning oil according to the model and size information, and inputting the flow and pressure parameters into the control system of the cleaning device; Starting the cleaning device, heating the cleaning oil to a preset temperature, and obtaining real-time temperature and pressure data of the cleaning oil; If the real-time temperature and pressure data reach the set values, the cleaning oil circulation valve is opened to allow the cleaning oil to enter the bearing under high temperature and high pressure to dissolve and remove pollutants; Acquire the temperature, pressure and flow data of the cleaning oil during the cleaning process through sensors, and transmit the data to the control system; The control system determines whether the temperature and pressure of the cleaning oil continue to reach the preset standard based on the acquired temperature, pressure and flow data, and if so, continues the cleaning process; if not, adjusts the temperature and pressure parameters of the cleaning oil; After the cleaning process lasts for a preset time, the cleaning oil circulation valve is closed to stop the supply of cleaning oil, and the oil drain valve is opened to discharge the cleaning oil and pollutants inside the bearing; After the oil is drained, the oil drain valve is closed, and the bearing preheating system is started to heat the bearing through the heating element so that the temperature field inside and outside the bearing is uniform; During the preheating process, the temperature data of each part of the bearing is obtained through the temperature sensor, and the temperature data is transmitted to the control system; The control system determines whether the bearing temperature field reaches a uniform standard based on the acquired temperature data. If so, it determines that preheating is completed and stops heating. If not, the preheating process continues until the preset standard is reached.

6. A method for cleaning and preheating a roller bearing as claimed in claim 3, characterized in that: Step S103 mainly includes: Obtaining a residual liquid sample after bearing cleaning, measuring the concentration of pollutants in the sample by using a spectral analysis method, and obtaining concentration data; Comparing the concentration data with a preset pollutant concentration threshold to determine whether the residual liquid meets a preset cleaning standard; If the concentration data is higher than the preset threshold, starting the bearing drying procedure; If the concentration data is lower than or equal to the preset threshold, proceed to the next step; In the drying process, the bearing material properties and the residual liquid properties are obtained, and the temperature of the high-temperature airflow for blowing the inside of the bearing is automatically adjusted according to the bearing material properties and the residual liquid properties; Use infrared temperature measurement technology to obtain the temperature data of the inner and outer surfaces of the bearing, and determine whether the bearing temperature field is uniform based on the temperature data; Dynamically adjust the air flow temperature and flow rate parameters according to the temperature data to optimize the drying process; Acquiring humidity data inside the bearing through a humidity sensor, and determining whether the residual liquid is completely removed according to the humidity data; When the humidity inside the bearing drops to a preset value, the drying process is stopped and the bearing cooling process is started; In the cooling procedure, the bearing size and material properties are obtained, and the cooling time and cooling gas flow parameters are automatically calculated according to the bearing size and material properties. The bearing is temperature-controlled and cooled by the cooling gas until the bearing temperature drops steadily to room temperature, thereby completing the drying and cooling process of the bearing after cleaning. The time required for bearing cooling is calculated as follows: t represents the cooling time, Q represents the amount of heat to be released, h represents the heat transfer coefficient, A represents the bearing surface area, and ΔT represents the temperature difference between the bearing and the cooling gas; The gas flow rate calculation formula to achieve the required cooling effect is as follows: represents the cooling gas mass flow rate, Q represents the heat to be removed, and ΔH represents the specific enthalpy change of the cooling gas.

7. A method for cleaning and preheating a roller bearing as claimed in claim 3, characterized in that: Step S104 mainly includes: According to the model and size parameters of the bearing, determine the temperature measurement point positions of the inner and outer rings of the bearing, install temperature sensors at the temperature measurement points, and obtain real-time temperature data of the inner and outer rings of the bearing; The temperature data of the inner and outer rings of the bearing are transmitted to a data processing unit, and the temperature data are filtered using a data processing algorithm to obtain filtered temperature data; Calculate the temperature difference between the inner and outer rings of the bearing according to the filtered temperature data, and establish a relationship model between the temperature difference and the uniformity of the temperature field inside and outside the bearing by mathematical modeling; Determine whether the temperature difference is less than a preset temperature difference threshold. If so, determine that the temperature field inside and outside the bearing has reached a uniform state, and mark the state of the temperature field inside and outside the bearing as uniform; If the temperature difference is greater than a preset threshold, the heating device is controlled to continue heating the bearing, while the temperature changes of the inner and outer rings of the bearing are monitored in real time, and the heating power and heating time are dynamically adjusted according to the temperature change trend; During the bearing heating process, thermal simulation software is used to simulate the temperature distribution inside the bearing, and the temperature cloud map of each point inside the bearing is obtained to provide real-time visual monitoring of the heating process; The temperature field uniformity evaluation index is used to quantitatively evaluate the uniformity of the temperature field inside and outside the bearing, and the numerical result of the temperature field uniformity is obtained, which provides a quantitative basis for judging whether the bearing has reached the ideal preheating state; The temperature field uniformity calculation formula is as follows: U represents the uniformity of the temperature field, Ti represents the temperature of the i-th measurement point, Tavg represents the average temperature of all measurement points, and n represents the total number of measurement points. This index reflects the degree of discreteness of the temperature distribution. The closer the value is to 1, the more uniform the temperature field is. According to the thermophysical characteristic parameters of the bearing material, the thermal stress distribution of the bearing at different preheating temperatures is calculated by the finite element analysis method to determine the optimal bearing preheating temperature to avoid thermal deformation and thermal damage caused by excessive preheating temperature; The calculation formula for the thermal stress generated by the material under temperature change is as follows: s th =αEΔT σth represents thermal stress, α represents linear expansion coefficient, E represents elastic modulus, and ΔT represents temperature change.

8. A method for cleaning and preheating a roller bearing as claimed in claim 3, characterized in that: Step S105 mainly includes: Acquiring real-time temperature data of the roller bearing, wherein the real-time temperature data is collected by a plurality of temperature sensors distributed at different positions of the bearing; Preprocessing and filtering the real-time temperature data to remove noise and interference therein, and obtaining a stable and reliable bearing temperature measurement value; According to the preset temperature threshold and temperature uniformity index, judging whether the bearing temperature measurement value reaches a stable and uniformly distributed state, and if so, determining that the bearing thermal stress has been eliminated; If the bearing temperature measurement value does not reach a stable and evenly distributed state, the heating power and time of the bearing are adjusted through the precise temperature control system until the bearing temperature meets the requirements; When it is determined that the bearing temperature is stable and uniform, the lubricating oil injection device is controlled to inject an appropriate amount of lubricating oil into the bearing, and the bearing temperature and lubricating oil pressure are monitored in real time; Through a closed-loop control algorithm, the lubricant injection rate and amount are dynamically adjusted according to the monitoring values ​​of the bearing temperature and lubricant pressure, the bearing temperature and vibration data are obtained, and through data analysis and pattern recognition, it is determined whether the bearing lubrication state is normal. If an abnormality is found, an early warning signal is generated; R(t)=R0exp(-β|T(t)-T0|-γ|P(t)-P0|) R(t) represents the lubricant injection rate adjustment factor, R_0 represents the initial adjustment factor, T_0 and P_0 represent the ideal operating points of temperature and pressure, respectively, and β and γ represent the weight coefficients of temperature and pressure deviations, respectively; this formula describes an adaptive adjustment strategy based on temperature and pressure deviations; According to the working status and usage time of the bearing, the machine learning algorithm is used to adaptively adjust the lubrication cycle and lubricant dosage, continuously optimize the lubrication strategy, and extend the service life of the bearing; The bearing temperature, vibration and lubrication related data are uploaded to the cloud platform, and the health status of the roll bearings can be monitored in real time and predictive maintenance can be achieved through big data analysis and remote diagnosis technology.

9. A method for cleaning and preheating a roller bearing as claimed in claim 3, characterized in that: Step S107 mainly includes: Determine the type and amount of the cleaning oil, as well as the temperature and pressure range during the cleaning process according to the model and size parameters of the roller bearing; The pressure and temperature data of the cleaning oil are collected in real time by sensors, and the data are transmitted to a data collection module for processing and storage; Using an optical concentration sensor to measure the concentration of residual liquid in the cleaning oil to obtain residual liquid concentration data; Obtain the bearing surface temperature data by using an infrared thermometer to determine whether the bearing temperature is uniform; According to the bearing temperature data and a preset temperature threshold, controlling the heating device to accurately control the temperature of the bearing to eliminate thermal stress; A vibration sensor is used to monitor the vibration signal of the bearing, and the lubrication state of the bearing is determined by spectrum analysis; Summarize the cleaning oil pressure, temperature, residual liquid concentration, bearing temperature and lubrication status data to generate a data log, and store the data log in a database; Analyzing the data log by a data analysis algorithm to obtain a quality assessment result of the bearing cleaning and preheating process; Based on the quality assessment results, determine whether the bearing has achieved the expected cleaning and preheating effect. If not, adjust the cleaning oil parameters and temperature control strategy, and re-perform the cleaning and preheating process until a satisfactory effect is achieved.

10. A method for cleaning and preheating a roller bearing as claimed in claim 3, characterized in that: Step S108 mainly includes: Acquiring operation data of a roller bearing cleaning and preheating device, wherein the operation data includes cleaning oil temperature, pressure, flow rate and cleaning time parameters, as well as bearing temperature distribution and vibration frequency status data; Preprocessing the operating data, removing outliers and noise data, and converting data of different dimensions into a unified scale through data normalization and standardization; According to the pre-processed data, a mathematical model between the cleaning oil formula and heating parameters and the bearing cleaning quality is established, and a machine learning algorithm is used to train and optimize the parameters of the mathematical model; Apply the optimized mathematical model to the cleaning and preheating process of the roll bearing, monitor various parameters in real time, and dynamically adjust the cleaning oil formula and heating parameters according to the prediction results of the mathematical model to achieve the best cleaning effect; Obtain the internal and external temperature field distribution data after the bearing is cleaned, and calculate the thermal stress distribution inside the bearing through finite element analysis; If there is thermal stress concentration or excessive temperature gradient, adjust the heating curve and holding time according to the finite element analysis results to make the temperature field inside and outside the bearing uniform and eliminate thermal stress; Detect the residual cleaning fluid on the bearing surface, determine the residual amount by infrared spectroscopy or gas chromatography, and judge whether it is lower than the preset threshold; According to the bearing material, size and working conditions, the most suitable lubricant type and viscosity are selected from the lubricant database, and the lubricant is accurately injected into the bearing through the automatic lubrication system; Establish a knowledge base for the roller bearing cleaning and preheating device, record the optimal cleaning oil formula, heating parameters and lubrication schemes for various types of bearings, continuously update and optimize the knowledge base content based on actual usage results, and improve the intelligence level of the device.

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