A method and system for online intelligent monitoring of industrial equipment

Abstract

The present invention provides an online intelligent monitoring method and system for industrial equipment, which relates to the field of data processing technology. The method includes: calculating the current viscosity state of the lubricating oil according to the temperature and water content of the lubricating oil; calculating the real-time dynamic factor according to the key parameters, and determining the thickness and pressure distribution of the lubricating oil film under different operating conditions according to the real-time dynamic factor and the current viscosity state of the lubricating oil; obtaining the comprehensive roughness data of the equipment surface, and calculating the film thickness ratio according to the thickness of the lubricating oil film under different operating conditions; judging the current lubrication state of the equipment according to the film thickness ratio, key parameters and pressure distribution. The present invention can realize real-time monitoring of the equipment state, can more accurately calculate the viscosity state of the lubricating oil, and then determine the thickness and pressure distribution of the lubricating oil film under different operating conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an online intelligent monitoring method and system for industrial equipment. Background Art

[0002] In the past, industrial equipment was usually monitored through regular manual inspections and simple instrument readings. Although this method can provide certain feedback on the equipment's operating status, there are also some areas that can be improved.

[0003] For example, a manufacturing company has been using traditional manual inspections and instrument readings to monitor its production equipment for a long time. However, in actual operation, this method gradually exposed some problems. Specifically,

[0004] A key production equipment had abnormal vibration and noise during operation. Due to the limitations of traditional monitoring methods, the operator failed to detect this abnormality in time. It was not until the equipment was severely worn, resulting in a significant drop in production efficiency, that the operator realized the seriousness of the problem. After in-depth investigation, it was found that the lubricating oil state of the equipment was abnormal, and the viscosity and water content were both beyond the normal range. However, because traditional monitoring methods mainly focus on a single viscosity or water content parameter, and ignore other factors that may affect the performance of the lubricating oil, such as temperature, pressure, etc., the fault was not detected in time and accurately. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an online intelligent monitoring method for industrial equipment, which can realize real-time monitoring of the equipment status, more accurately calculate the viscosity state of the lubricating oil, and then determine the thickness and pressure distribution of the lubricating oil film under different operating conditions.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, an online intelligent monitoring method for industrial equipment is provided, the method comprising:

[0008] Real-time collection of key parameters in equipment operation, including equipment operating temperature, pressure, vibration frequency, and lubricating oil temperature, viscosity, and water content;

[0009] Calculate the current viscosity state of the lubricating oil according to the lubricating oil temperature and water content;

[0010] Calculate the real-time dynamic factors according to the key parameters, and determine the thickness and pressure distribution of the lubricating oil film under different operating conditions according to the real-time dynamic factors and the current viscosity state of the lubricating oil, wherein the lubricating oil film is a thin film formed by the lubricating oil between the moving parts of the equipment;

[0011] Obtain comprehensive roughness data of the equipment surface and calculate the film thickness ratio based on the thickness of the lubricating oil film under different operating conditions;

[0012] Determine the current lubrication status of the equipment based on film thickness ratio, key parameters and pressure distribution.

[0013] Furthermore, key parameters of equipment operation are collected in real time, including:

[0014] Use sensor networks to collect key parameters of equipment operation in real time;

[0015] Generate a set of particles, each particle represents a set of key parameters;

[0016] Determining an evaluation function for evaluating the quality of each set of key parameter combinations;

[0017] According to the evaluation function, the particle speed and position are updated, and the final key parameters are obtained through multiple iterations;

[0018] Clean and standardize the final key parameters to obtain standardized data;

[0019] Cluster the standardized data, determine the number of clusters K, and randomly determine K points as the initial cluster centers;

[0020] Calculate the distance between each data point and each cluster center in the cluster number K, and assign the data point to the corresponding cluster center. Determine the center point of each cluster by calculating the average value of all data points in the cluster.

[0021] Repeat the above steps until the preset number of iterations is reached to obtain the cluster analysis results;

[0022] According to the cluster analysis results, the final key parameters are divided into different categories. For each category, its corresponding statistical characteristics are calculated to generate the classified key parameters.

[0023] Furthermore, the calculation formula of the evaluation function is: ;

[0024] in, Represents temperature, which is a key parameter when the equipment is running; Represents pressure, which is a key parameter when the equipment is running; represents flow; T max , P max and F max are the maximum values ​​of temperature, pressure and flow rate respectively; T min , P min and Fmin are the minimum values ​​of temperature, pressure and flow rate respectively; is the weight of the temperature term; is the weight of the pressure term; is the weight of the flow term; is the weight of the interaction term between temperature and pressure; , and is the exponential parameter.

[0025] Furthermore, according to the lubricating oil temperature and water content, the current viscosity state of the lubricating oil is calculated, including:

[0026] According to the lubricating oil temperature and water content, Calculate the current viscosity state of the lubricant ,in, Indicates the basic viscosity of lubricating oil under standard conditions; Indicates the current temperature; represents reference temperature; W represents water content; S represents shear rate; and represents the adjustment factor; and Shows the coefficient related to temperature; and represents the coefficient related to water content; d The coefficient that represents the effect of pressure on viscosity; e and f Represents the coefficient related to shear rate.

[0027] Furthermore, real-time dynamic factors are calculated based on key parameters, including:

[0028] According to the key parameters, Calculate real-time dynamic factors ,in, Indicates the current operating speed of the device; Indicates the maximum design speed of the equipment; Indicates the current load of the device; Indicates the maximum design load of the equipment; Indicates the current ambient temperature; Indicates the standard operating temperature of the equipment; Indicates the current vibration frequency of the device; Indicates the maximum vibration frequency of the equipment under normal operating conditions; , , and is the weight coefficient.

[0029] Furthermore, according to the real-time dynamic factors and the current viscosity state of the lubricating oil, the thickness and pressure distribution of the lubricating oil film under different operating conditions are determined, including:

[0030] According to the real-time dynamic factors and the current viscosity state of the lubricant, Calculate the thickness of the lubricating oil film under different operating conditions, where h is the thickness of the lubricating oil film; V is the operating speed of the device; L is the load borne by the equipment; is a coefficient;

[0031] According to the thickness of lubricating oil film under different operating conditions h ,pass Calculate the pressure distribution of the lubricating oil film, where is the pressure distribution of the lubricating oil film; C is the coefficient.

[0032] Furthermore, the comprehensive roughness data of the equipment surface is obtained, and the film thickness ratio is calculated according to the thickness of the lubricating oil film under different operating conditions, including:

[0033] Set representative measuring points on the surface of the equipment so that the measuring instrument measures the corresponding roughness value at each measuring point;

[0034] The thickness of the lubricating oil film under different operating conditions was divided by the roughness value of the corresponding measuring point to obtain the film thickness ratio.

[0035] In a second aspect, an online intelligent monitoring system for industrial equipment includes:

[0036] The acquisition module is used to collect key parameters of the equipment in real time, including the equipment's operating temperature, pressure, vibration frequency, and the temperature, viscosity, and water content of the lubricating oil; and calculate the current viscosity state of the lubricating oil based on the lubricating oil temperature and water content;

[0037] A dynamic adjustment module is used to calculate the real-time dynamic factors according to the key parameters, and determine the thickness and pressure distribution of the lubricating oil film under different operating conditions according to the real-time dynamic factors and the current viscosity state of the lubricating oil;

[0038] The processing module is used to obtain the comprehensive roughness data of the equipment surface and calculate the film thickness ratio according to the thickness of the lubricating oil film under different operating conditions; the current lubrication state of the equipment is judged according to the film thickness ratio, key parameters and pressure distribution.

[0039] According to a third aspect, a computing device includes:

[0040] one or more processors;

[0041] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0042] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0043] The above solution of the present invention includes at least the following beneficial effects:

[0044] This method can monitor the equipment status in real time by collecting key parameters in the equipment operation, such as operating temperature, pressure, vibration frequency, etc. This method takes into account multiple factors such as the temperature, viscosity and water content of the lubricating oil, and can more accurately calculate the viscosity state of the lubricating oil, and then determine the thickness and pressure distribution of the lubricating oil film under different operating conditions, which makes the assessment of the equipment lubrication status more accurate.

[0045] By obtaining the comprehensive roughness data of the equipment surface and calculating the film thickness ratio, this method can provide a more comprehensive understanding of the lubrication condition of the equipment. Through real-time monitoring and accurate evaluation of the lubrication status of the equipment, this method can help companies promptly discover and solve potential lubrication problems, thereby reducing equipment wear and failure rates and extending the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of an online intelligent monitoring method for industrial equipment provided by an embodiment of the present invention.

[0047] Figure 2 It is a schematic diagram of an online intelligent monitoring system for industrial equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0049] like Figure 1 As shown, an embodiment of the present invention provides an online intelligent monitoring method for industrial equipment, the method comprising the following steps:

[0050] Step 11, real-time collection of key parameters in equipment operation, including the equipment's operating temperature, pressure, vibration frequency, and lubricating oil's temperature, viscosity, and water content;

[0051] Step 12, calculating the current viscosity state of the lubricating oil according to the lubricating oil temperature and water content;

[0052] Step 13, calculating the real-time dynamic factor according to the key parameters, and determining the thickness and pressure distribution of the lubricating oil film under different operating conditions according to the real-time dynamic factor and the current viscosity state of the lubricating oil, wherein the lubricating oil film is a thin film formed by the lubricating oil between the moving parts of the equipment;

[0053] Step 14, obtaining comprehensive roughness data of the equipment surface, and calculating the film thickness ratio according to the thickness of the lubricating oil film under different operating conditions;

[0054] Step 15, judging the current lubrication state of the equipment according to the film thickness ratio, key parameters and pressure distribution.

[0055] In the embodiment of the present invention, step 11, real-time acquisition of key operating parameters of the equipment is the basis for accurate monitoring. These parameters include the operating temperature, pressure, vibration frequency, etc. of the equipment, which can fully reflect the operating status of the equipment. Through continuous and real-time data acquisition, the abnormal status of the equipment can be discovered in time to prevent potential failures. Step 12, by calculating the viscosity state of the lubricating oil, it can be evaluated whether its performance meets the requirements of equipment operation, which helps to timely discover problems of the lubricating oil, such as reduced viscosity, excessive water content, etc., so as to avoid damage to the equipment due to poor lubricating oil performance. Step 13, obtaining the thickness and pressure distribution of the lubricating oil film under different operating conditions is helpful to evaluate the lubrication effect and wear of the equipment, which can provide important reference for equipment maintenance, such as adjusting the amount of lubricating oil used, replacement cycle, etc., to ensure that the equipment operates under the best lubrication state. Step 14, the roughness data and film thickness ratio of the equipment surface are important indicators for evaluating the lubrication state and wear of the equipment. By obtaining these data and calculating them, the actual lubrication effect of the equipment can be judged more accurately, which helps to discover potential wear problems and take timely measures for maintenance. Step 15, combining the data and analysis results of the above steps, the current lubrication status of the equipment can be comprehensively and accurately determined, which helps to achieve preventive maintenance, reduce downtime, extend equipment service life and reduce maintenance costs. At the same time, a good lubrication status also helps to improve the operating efficiency and production benefits of the equipment.

[0056] In a preferred embodiment of the present invention, the above step 11 may include:

[0057] Step 111, using a sensor network to collect key parameters of the device in real time; generating a group of particles, each particle representing a group of key parameters;

[0058] Step 112, determining an evaluation function for evaluating the quality of each set of key parameter combinations; updating the speed and position of the particle according to the evaluation function, and obtaining the final key parameters through multiple iterations;

[0059] Step 113, cleaning and standardizing the final key parameters to obtain standardized data;

[0060] Step 114, clustering the standardized data, determining the number of clusters K, and randomly determining K points as initial cluster centers;

[0061] Step 115, calculating the distance between each data point in the number of clusters K and the center of each cluster, and assigning the data point to the corresponding cluster center, and determining the center point of each cluster by calculating the average value of all data points in the cluster; repeating the above steps until the preset number of iterations is reached to obtain the cluster analysis result;

[0062] Step 116, according to the cluster analysis results, the final key parameters are divided into different categories, and for each category, the corresponding statistical features are calculated to generate the classified key parameters; the calculation formula of the evaluation function is: ;

[0063] in, Represents temperature, which is a key parameter when the equipment is running; Represents pressure, which is a key parameter when the equipment is running; represents flow; T max , P max and F max are the maximum values ​​of temperature, pressure and flow rate respectively; T min , P min and F min are the minimum values ​​of temperature, pressure and flow rate respectively; is the weight of the temperature term; is the weight of the pressure term; is the weight of the flow term; is the weight of the interaction term between temperature and pressure; , and is the exponential parameter.

[0064] In the embodiment of the present invention, step 111 deploys a network composed of multiple sensors, which can collect various key parameters in the operation of the equipment in real time, such as temperature, pressure, vibration frequency, etc., to generate a group of particles, each particle represents a group of key parameter combinations obtained from the sensor network, and the sensor network can be used to obtain the key parameters of the equipment operation in real time and accurately, providing a rich data source for subsequent data analysis and processing. The introduction of particles makes the parameter combination more flexible and helps to capture the dynamic changes in the operation of the equipment. Step 112 defines an evaluation function for evaluating the quality of each group of key parameter combinations. Through an iterative process, the speed and position of the particles are continuously updated to optimize the parameter combination, and finally a group of optimal key parameters are obtained; the introduction of the evaluation function enables quantitative evaluation of the quality of the parameter combination, and through iterative optimization, more accurate and representative key parameters can be obtained, where the particle speed update formula is:

[0065] ,in, represents the velocity of particle i at time t; represents the position of particle i at time t; represents the individual optimal position of particle i; represents the global optimal position; is the inertia weight, which is used to balance the capabilities of global search and local search; and are the acceleration coefficients, which affect the impact of individual optimum and global optimum on particle velocity respectively; and is a random number between [0, 1], used to increase the randomness of the search.

[0066] The position update formula is:

[0067] , represents particle i at time location, represents particle i at time speed.

[0068] Step 113, check whether there are missing values ​​in the data set. If there are, according to the nature of the data and business needs, you can choose to delete the records with missing values, or use interpolation and other methods to fill in the missing values; use statistical methods (such as Z-score, etc.) to detect outliers in the data. Outliers may be caused by data collection errors or equipment abnormalities. You can choose to correct or delete these outliers according to the actual situation; check and delete duplicate records in the data set to ensure the uniqueness of the data; standardize the data through minimum-maximum standardization. Standardizing the data is to convert the data value into a certain range (usually between 0 and 1 or -1 to 1) to eliminate the impact of different dimensions and orders of magnitude between different key parameters.

[0069] Step 114, according to actual needs and the size of the data volume, set a reasonable range of cluster numbers, for example, from 2 to a certain upper limit value N; for each K value within the range, use a certain clustering algorithm (such as K-Means) to cluster the data; for the clustering result of each K value, calculate its silhouette coefficient, which is an indicator to measure the clustering effect, and its value range is between -1 and 1. The larger the value, the better the clustering effect; the calculation method of the silhouette coefficient is: for each point in the data set, calculate its average distance to other points in the same cluster (cohesion) and the average distance to the nearest point in a different cluster (separation), then subtract the cohesion from the separation and divide it by the larger value of the two to obtain the silhouette coefficient of the point. The average value of the silhouette coefficients of all data points is the silhouette coefficient of the cluster; according to the size of the silhouette coefficient, select the K value with the largest silhouette coefficient as the optimal number of clusters.

[0070] Step 115, for each data point in the data set, calculate the Euclidean distance between it and each cluster center; according to the calculated Euclidean distance, assign the data point to the cluster corresponding to the cluster center closest to it, that is, for each data point, find the cluster center with the smallest Euclidean distance, and assign the data point to the cluster; within each cluster, calculate the average coordinate value of all data points to obtain a new cluster center, and update the centers of all clusters to the calculated new cluster center; check whether the preset number of iterations has been reached, if the termination condition is met, stop the iteration; otherwise, return to continue the iteration; when the iteration is terminated, the cluster analysis result is obtained according to the final distribution of the cluster center and the data points, and the data points in each cluster have similar characteristics, while the data points between different clusters have large differences.

[0071] Step 116, according to the cluster analysis results obtained in step 115, data points of different categories can be determined. Each clustered data point represents a specific category, and these categories will be used as the basis for classifying key parameters. According to the results of cluster analysis, the data points of each key parameter are assigned to the corresponding category. In this way, the key parameters that were originally mixed together are divided into several clear categories. For the key parameters in each category, calculate their statistical characteristics. These characteristics may include but are not limited to: mean, which is used to reflect the average level of this type of parameter, which helps to understand the overall situation of this type of parameter; variance, which is used to measure the degree of dispersion of this type of parameter data, that is, the fluctuation of the data. The larger the variance, the more dispersed the data; the smaller the variance, the more concentrated the data; median, when the data has a skewed distribution, the median can better reflect the central position of the data; mode, the value that appears most frequently in the data, can reflect the central trend of the data; maximum and minimum values, understand the value range of this type of parameter data; skewness and kurtosis, respectively measure the degree of skewness and sharpness of the data distribution, which helps to understand the shape of the data distribution more deeply. The calculated statistical characteristics are used as a description of the key parameters after classification. In this way, each category will have a set of statistical features to characterize the characteristics and laws of its internal key parameters. According to the classified key parameters and their statistical features, the operating status of the equipment can be analyzed and evaluated more finely. For example, the high or low mean of key parameters in certain categories may indicate that the equipment is in an abnormal state; a significant change in variance may mean an increase in the instability of equipment operation, etc. Through these analyses, the accuracy and effectiveness of equipment status monitoring can be improved, and potential problems can be discovered and handled in a timely manner.

[0072] In a preferred embodiment of the present invention, the above step 12 may include:

[0073] According to the lubricating oil temperature and water content, Calculate the current viscosity state of the lubricant ,in, Indicates the basic viscosity of lubricating oil under standard conditions; Indicates the current temperature; represents reference temperature; W represents water content; S represents shear rate; and represents the adjustment factor; and Shows the coefficient related to temperature; and represents the coefficient related to water content; d The coefficient that represents the effect of pressure on viscosity; e and f Represents the coefficient related to shear rate.

[0074] In the embodiment of the present invention, the formula comprehensively considers the influence of temperature, water content, shear rate and pressure on the viscosity of the lubricating oil, so that the viscosity state of the lubricating oil can be accurately calculated under different working conditions. By inputting the current temperature, water content, shear rate and other parameters, the viscosity of the lubricating oil under the current working conditions can be predicted, so as to determine whether it meets the needs of the equipment. Understanding the viscosity state of the lubricating oil helps to replace or dispose of unqualified lubricating oil in time, reduce equipment wear and failure, and extend the service life of the equipment. By adjusting the viscosity of the lubricating oil, the operating state of the equipment can be optimized and the operating efficiency can be improved. Appropriate lubricating oil viscosity can reduce the friction and wear of the equipment, reduce energy consumption, and reduce the generation of waste, which is beneficial to environmental protection. By monitoring and adjusting the viscosity of the lubricating oil, the service life of the lubricating oil can be extended, the replacement frequency can be reduced, and thus the maintenance cost can be reduced. Ensuring that the lubricating oil operates within the optimal viscosity range can reduce equipment failures caused by poor lubricating oil performance and improve the safety of equipment operation.

[0075] In a preferred embodiment of the present invention, the above step 13 may include:

[0076] According to the key parameters, Calculate real-time dynamic factors ,in, Indicates the current operating speed of the device; Indicates the maximum design speed of the equipment; Indicates the current load of the device; Indicates the maximum design load of the equipment; Indicates the current ambient temperature; Indicates the standard operating temperature of the equipment; Indicates the current vibration frequency of the device; Indicates the maximum vibration frequency of the equipment under normal operating conditions; , , and is the weight coefficient.

[0077] In an embodiment of the present invention, the formula comprehensively considers key parameters such as the operating speed, load, ambient temperature, and vibration frequency of the equipment, and provides a comprehensive evaluation index that can fully reflect the real-time operating status of the equipment. By inputting the current operating data of the equipment, the dynamic factor can be calculated in real time to timely reflect the changes in the operating status of the equipment. The weight coefficient in the formula can be adjusted according to actual needs to reflect the degree of influence of different parameters on the operating status of the equipment, thereby improving the flexibility of the evaluation. By monitoring the changes in the dynamic factors, abnormal conditions of equipment operation can be discovered in a timely manner. According to the feedback of the dynamic factors, the operating parameters of the equipment can be adjusted, the operating status of the equipment can be optimized, and the operating efficiency can be improved; by reducing the excessive load and adverse environmental impact of the equipment, the service life of the equipment can be extended. Real-time monitoring of dynamic factors helps to timely discover potential safety hazards and improve the safety of equipment operation. By reducing equipment failures and downtime, maintenance costs and production losses can be reduced, thereby improving overall cost-effectiveness.

[0078] In a preferred embodiment of the present invention, the above step 13 may further include:

[0079] According to the real-time dynamic factors and the current viscosity state of the lubricant, Calculate the thickness of the lubricating oil film under different operating conditions, where h is the thickness of the lubricating oil film; V is the operating speed of the device; L is the load borne by the equipment; is a coefficient;

[0080] According to the thickness of lubricating oil film under different operating conditions h ,pass Calculate the pressure distribution of the lubricating oil film, where is the pressure distribution of the lubricating oil film; C is the coefficient.

[0081] In the embodiment of the present invention, by calculating the thickness of the lubricating oil film, the lubricating performance of the lubricating oil under different operating conditions can be evaluated. A thicker oil film generally means a better lubrication effect, which can reduce the friction and wear of the equipment. The thickness of the lubricating oil film can help prevent the wear of the equipment under extreme or adverse conditions. If the thickness of the oil film is insufficient, timely measures can be taken, such as replacing the lubricating oil or adjusting the operating conditions, to avoid damage to the equipment. According to the thickness and pressure distribution of the lubricating oil film, the lubrication strategy can be optimized, such as selecting a suitable type of lubricating oil, adjusting the supply of lubricating oil, or improving the design of the lubrication system. By ensuring that the lubricating oil film can provide sufficient lubrication effect under various operating conditions, the reliability and stability of the equipment can be improved, and failures and downtime can be reduced. Good lubrication effect can significantly extend the service life of the equipment and reduce the cost of repairing and replacing equipment. Optimizing the lubrication strategy can also reduce the energy consumption of the equipment and the generation of waste, which is conducive to achieving energy conservation, emission reduction and environmental protection. By monitoring the state of the lubricating oil film, potential lubrication problems can be discovered in time and corresponding measures can be taken to enhance the safety of equipment operation. By reducing the wear, failure and downtime of the equipment and optimizing the lubrication strategy, long-term cost savings can be achieved.

[0082] In a preferred embodiment of the present invention, the above step 14 may further include:

[0083] Set representative measuring points on the surface of the equipment so that the measuring instrument measures the corresponding roughness value at each measuring point;

[0084] The thickness of the lubricating oil film under different operating conditions was divided by the roughness value of the corresponding measuring point to obtain the film thickness ratio.

[0085] In an embodiment of the present invention, representative measurement points are selected on the surface of the device. These points should be able to reflect the overall roughness of the surface of the device and be easy to measure; a measuring instrument is used to measure the corresponding roughness value at each measuring point. The roughness value can be obtained by a contact or non-contact measuring instrument; the measured roughness value is recorded, and the comprehensive roughness data of the device surface is calculated. The comprehensive roughness data can be obtained by statistically analyzing the roughness values ​​of each measuring point, such as calculating the average value, standard deviation, etc.; the film thickness ratio is calculated according to the thickness of the lubricating oil film under different operating conditions. The film thickness ratio refers to the ratio of the thickness of the lubricating oil film to the roughness value of the device surface. The film thickness ratio can be obtained by dividing the thickness of the lubricating oil film by the roughness value of the corresponding measuring point.

[0086] In an embodiment of the present invention, the film thickness ratio is an important indicator for evaluating lubrication performance. A higher film thickness ratio means that the lubricating oil film can better cover the surface of the equipment, provide better lubrication effect, and reduce friction and wear. By monitoring the changes in the film thickness ratio, the wear of the equipment can be predicted. When the film thickness ratio is reduced to a certain extent, it may mean that the lubricating oil film cannot effectively cover the surface of the equipment, and it is necessary to replace the lubricating oil in time or take other measures to reduce wear. Determining the film thickness ratio can help optimize the operating conditions of the equipment. For example, parameters such as the operating speed, load or temperature of the equipment can be adjusted to maintain an appropriate film thickness ratio and improve the operating efficiency and reliability of the equipment. By maintaining an appropriate film thickness ratio, the wear and fatigue of the equipment can be reduced and the service life of the equipment can be extended. This helps to reduce the cost of repairing and replacing equipment and improve the economic benefits of the equipment. Real-time monitoring of the film thickness ratio helps to detect potential lubrication problems in a timely manner and take corresponding measures, thereby improving the safety of equipment operation. This helps to reduce equipment failures and downtime and reduce production risks.

[0087] In a preferred embodiment of the present invention, the above step 15 may further include:

[0088] Obtain the film thickness ratio data, key parameters (such as equipment operating speed, load, ambient temperature, etc.) and pressure distribution data of the lubricating oil film calculated in the previous steps; set the reasonable range or threshold of the film thickness ratio, key parameters and pressure distribution according to the historical operation data of the equipment; compare the collected data with the set judgment criteria to analyze whether the film thickness ratio is within a reasonable range, whether the key parameters are abnormal, and whether the pressure distribution is uniform and does not exceed the safety limit. If all data are within a reasonable range, it can be judged that the current lubrication state of the equipment is good. If one or more data exceed the reasonable range, it is necessary to further analyze the reasons and judge the possible problems in the lubrication state, such as insufficient lubricating oil, oil quality deterioration, equipment wear, etc.; output the judgment results in the form of reports or data visualization for easy viewing and understanding by management personnel and technical personnel, and record the judgment results and data for each time for historical data analysis and future prediction.

[0089] In an embodiment of the present invention, by monitoring the lubrication status of the equipment regularly or in real time, potential lubrication problems can be discovered in time to avoid equipment failure or damage due to poor lubrication. According to the judgment result of the lubrication status, preventive maintenance can be performed in advance, such as replacing lubricating oil, cleaning the lubrication system, etc., so as to extend the service life of the equipment and improve the operating efficiency. By understanding the lubrication status of the equipment, the operating parameters or working environment of the equipment can be adjusted to optimize the operating status of the equipment and reduce energy consumption. A good lubrication status helps to reduce equipment failures and downtime, thereby improving the stability and safety of the production process. By timely discovering and handling lubrication problems, production interruptions and increased maintenance costs caused by equipment failures can be avoided, thereby saving costs for the enterprise.

[0090] like Figure 2 As shown, an embodiment of the present invention further provides an industrial equipment online intelligent monitoring system 20, comprising:

[0091] The acquisition module 21 is used to collect key parameters of the equipment in real time, including the working temperature, pressure, vibration frequency of the equipment, and the temperature, viscosity and water content of the lubricating oil; and calculate the current viscosity state of the lubricating oil according to the temperature and water content of the lubricating oil;

[0092] The dynamic adjustment module 22 is used to calculate the real-time dynamic factor according to the key parameters, and determine the thickness and pressure distribution of the lubricating oil film under different operating conditions according to the real-time dynamic factor and the current viscosity state of the lubricating oil;

[0093] The processing module 23 is used to obtain the comprehensive roughness data of the equipment surface and calculate the film thickness ratio according to the thickness of the lubricating oil film under different operating conditions; and judge the current lubrication state of the equipment according to the film thickness ratio, key parameters and pressure distribution.

[0094] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0095] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0096] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0097] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An online intelligent monitoring method for industrial equipment, characterized in that: The method comprises: Real-time collection of key parameters in equipment operation, including equipment operating temperature, pressure, vibration frequency, and lubricating oil temperature, viscosity, and water content; Calculate the current viscosity state of the lubricating oil according to the lubricating oil temperature and water content; Calculate the real-time dynamic factors according to the key parameters, and determine the thickness and pressure distribution of the lubricating oil film under different operating conditions according to the real-time dynamic factors and the current viscosity state of the lubricating oil, wherein the lubricating oil film is a thin film formed by the lubricating oil between the moving parts of the equipment; Obtain comprehensive roughness data of the equipment surface and calculate the film thickness ratio based on the thickness of the lubricating oil film under different operating conditions; Determine the current lubrication status of the equipment based on the film thickness ratio, key parameters and pressure distribution; collect key parameters of the equipment in real time, including: Use sensor networks to collect key parameters of equipment operation in real time; Generate a set of particles, each particle represents a set of key parameters; Determining an evaluation function for evaluating the quality of each set of key parameter combinations; According to the evaluation function, the particle speed and position are updated, and the final key parameters are obtained through multiple iterations; Clean and standardize the final key parameters to obtain standardized data; Cluster the standardized data, determine the number of clusters K, and randomly determine K points as the initial cluster centers; Calculate the distance between each data point and each cluster center in the cluster number K, and assign the data point to the corresponding cluster center. Determine the center point of each cluster by calculating the average value of all data points in the cluster. Repeat the above steps until the preset number of iterations is reached to obtain the cluster analysis results; According to the cluster analysis results, the final key parameters are divided into different categories. For each category, the corresponding statistical characteristics are calculated to generate the classified key parameters. The calculation formula of the evaluation function is: ; in, Represents temperature, which is a key parameter when the equipment is running; Represents pressure, which is a key parameter when the equipment is running; represents flow; T max , P max and F max are the maximum values ​​of temperature, pressure and flow rate respectively; T min , P min and F min are the minimum values ​​of temperature, pressure and flow rate respectively; is the weight of the temperature term; is the weight of the pressure term; is the weight of the flow term; is the weight of the interaction term between temperature and pressure; , and Is an index parameter; according to the lubricating oil temperature and water content, calculate the current lubricating oil viscosity state, including: According to the lubricating oil temperature and water content, Calculate the current viscosity state of the lubricant ,in, Indicates the basic viscosity of lubricating oil under standard conditions; Indicates the current temperature; represents reference temperature; W represents water content; S represents shear rate; and represents the adjustment factor; and Shows the coefficient related to temperature; and represents the coefficient related to water content; d The coefficient that represents the effect of pressure on viscosity; e and f Represents coefficients related to shear rate; calculates real-time dynamic factors based on key parameters, including: According to the key parameters, Calculate real-time dynamic factors ,in, Indicates the current operating speed of the device; Indicates the maximum design speed of the equipment; Indicates the current load of the device; Indicates the maximum design load of the equipment; Indicates the current ambient temperature; Indicates the standard operating temperature of the equipment; Indicates the current vibration frequency of the device; Indicates the maximum vibration frequency of the equipment under normal operating conditions; , , and is the weight coefficient; according to the real-time dynamic factors and the current viscosity state of the lubricating oil, the thickness and pressure distribution of the lubricating oil film under different operating conditions are determined, including: According to the real-time dynamic factors and the current viscosity state of the lubricant, Calculate the thickness of the lubricating oil film under different operating conditions, where h is the thickness of the lubricating oil film; V is the operating speed of the device; L is the load borne by the equipment; is a coefficient; According to the thickness of lubricating oil film under different operating conditions h ,pass Calculate the pressure distribution of the lubricating oil film, where is the pressure distribution of the lubricating oil film; C is a coefficient; obtain the comprehensive roughness data of the equipment surface, and calculate the film thickness ratio according to the thickness of the lubricating oil film under different operating conditions, including: Set representative measuring points on the surface of the equipment so that the measuring instrument measures the corresponding roughness value at each measuring point; The thickness of the lubricating oil film under different operating conditions was divided by the roughness value of the corresponding measuring point to obtain the film thickness ratio.

2. An online intelligent monitoring system for industrial equipment, characterized in that: Executing the method according to claim 1, comprising: The acquisition module is used to collect key parameters of the equipment in real time, including the equipment's operating temperature, pressure, vibration frequency, and the temperature, viscosity, and water content of the lubricating oil; and calculate the current viscosity state of the lubricating oil based on the lubricating oil temperature and water content; A dynamic adjustment module is used to calculate the real-time dynamic factor according to the key parameters, and determine the thickness and pressure distribution of the lubricating oil film under different operating conditions according to the real-time dynamic factor and the current viscosity state of the lubricating oil, wherein the lubricating oil film is a thin film formed by the lubricating oil between the moving parts of the equipment; The processing module is used to obtain the comprehensive roughness data of the equipment surface and calculate the film thickness ratio according to the thickness of the lubricating oil film under different operating conditions; the current lubrication state of the equipment is judged according to the film thickness ratio, key parameters and pressure distribution.

3. A computing device, characterized in that: include: one or more processors; A storage device, used for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in claim 1.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to claim 1 when executed by a processor.

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