Industrial equipment lubrication reliability evaluation method

Abstract

The invention relates to the technical field of mechanical equipment, in particular to an industrial equipment lubrication reliability evaluation method which comprises the following steps: S1, collecting operation data: collecting the operation data of mechanical equipment; s2, evaluating the lubricating property: calculating the flowing property and the lubricating property of the lubricating oil by utilizing an Arrhenius model and a Barus model; s3, monitoring and adjusting the thickness of the dynamic lubricating film: implementing a technology for dynamically monitoring the thickness of the lubricating film, and monitoring and adjusting the thickness of the lubricating film in real time; s4, the lubricating effect is evaluated, specifically, the lubricating effect of the lubricating oil under the current working condition is evaluated; s5, determining the reliability grade: determining the reliability grade of the lubricating oil; and S6, improvement measures are put forward, and if the lubricating reliability of the mechanical equipment does not meet the preset standard, the lubricating scheme improvement measures are put forward. According to the invention, the mechanical equipment is always kept in the optimal lubrication state under the condition of continuous change, and the risk caused by improper lubrication is reduced.

Description

[0001] This application is a divisional application of the application filed on November 4, 2024, with application number 2024115570389 and invention name “A method and system for evaluating lubrication reliability of industrial equipment”. Technical Field

[0002] The present invention relates to the technical field of mechanical equipment, and in particular to a method for evaluating the lubrication reliability of industrial equipment. Background Art

[0003] In modern industry, the stable operation of mechanical equipment places extremely high demands on the performance of the lubrication system. Traditional lubrication reliability assessment methods mostly rely on empirical judgment and regular inspections, which are not only time-consuming and labor-intensive, but often cannot accurately reflect the actual lubrication conditions, especially in situations where the equipment's operating conditions are complex or change frequently. In addition, the flow properties and lubrication performance of lubricating oil will change under different temperature and pressure conditions. Traditional methods make it difficult to accurately assess changes in these performance indicators in real time, making it impossible to make timely adjustments to optimize the lubrication effect.

[0004] Currently, although various sensor technologies exist for monitoring the operating status of equipment, these technologies still have shortcomings in integrating and analyzing the collected data to achieve intelligent management of lubrication systems. In particular, there is a lack of a systematic evaluation method to monitor the thickness of the lubricating film in real time and dynamically adjust the lubrication strategy based on the monitoring results to cope with changes in working conditions. In addition, there is usually no unified quantitative standard for the reliability evaluation of lubricating oils, making it difficult to evaluate and compare the performance of different lubricants, resulting in difficulty in ensuring the overall reliability of equipment lubrication. Summary of the Invention

[0005] Based on the above objectives, the present invention provides a method for evaluating the lubrication reliability of industrial equipment.

[0006] A method for evaluating the lubrication reliability of industrial equipment comprises the following steps: S1, collect operating data: collect operating data of mechanical equipment, including equipment operating time, temperature, load and lubricating oil pressure; S2, lubrication performance evaluation: Based on the collected data, the flow properties and lubrication performance of the lubricant are calculated using the Arrhenius model and the Barus model. The Arrhenius model and the Barus model consider the relationship between the viscosity of the lubricant and the change of temperature and pressure respectively; S3, Dynamic Lubricating Film Thickness Monitoring and Adjustment: Implement dynamic lubricating film thickness monitoring technology to monitor and adjust the lubricating film thickness in real time to adapt to changes in equipment operating conditions. By using sensor technology to monitor the film thickness in the lubricating contact area, the lubricating oil supply is automatically adjusted to ensure optimal lubrication. S4, Lubrication Effectiveness Evaluation: Based on the real-time monitoring results of flow properties, lubrication performance, and lubricating film, the lubricating effect of the lubricant under the current working conditions is evaluated, including whether the lubricating film formed by the lubricant prevents metal contact and reduces wear; S5, determine the reliability level: determine the reliability level of the lubricating oil based on the lubrication effect, and compare it with the preset reliability level standard to evaluate the overall reliability of the mechanical equipment lubrication; S6, propose improvement measures: If the reliability of mechanical equipment lubrication does not meet the preset standards, propose improvement measures for the lubrication scheme, including replacing the lubricating oil, adjusting the working parameters of the lubrication scheme, or improving the lubrication scheme design.

[0007] Furthermore, the collected operation data in S1 includes: Equipment operation time record: by installing a time recorder on the mechanical equipment, the equipment's startup, operation and shutdown time are automatically recorded; Temperature monitoring: Use temperature sensors to monitor and record temperature changes in the moving parts and lubrication areas of mechanical equipment in real time. By monitoring the temperature of the equipment body and lubricating oil, the thermal stability of the lubricating oil and the heat load of the equipment under different working conditions can be analyzed. Load monitoring: Use force sensors to measure and record the actual load borne by mechanical equipment during operation; Lubricating oil pressure monitoring: Through the pressure sensor, the pressure of the lubricating oil in the lubrication path is monitored in real time. The monitoring of the lubricating oil pressure is also used to detect whether the lubricating oil channel is unobstructed and whether there is any leakage problem.

[0008] Furthermore, the evaluation of lubrication performance in S2 includes: Arrhenius model application: The Arrhenius model is used to calculate and predict the viscosity of lubricating oil under different temperature conditions, helping to evaluate the flow properties of lubricating oil. The calculation formula is: ; in, Represents temperature The viscosity of the lubricating oil under is the viscosity at the reference temperature, is the activation energy, is the gas constant, is the temperature of the lubricating oil ; Application of Barus model: The Barus model evaluates the viscosity change of lubricating oil under different pressures, thereby determining the lubricating performance of lubricating oil under high load conditions. The calculation formula is: ; in, is the viscosity of the lubricating oil under pressure is the viscosity at standard atmospheric pressure is the pressure coefficient of the lubricating oil is the pressure of the lubricating oil Performance evaluation: based on the calculation results of the Arrhenius model and the Barus model, the flow performance and lubrication performance of the lubricating oil are comprehensively evaluated, considering the relationship between the viscosity of the lubricating oil and the temperature and pressure, to determine whether the lubricating oil can maintain the lubrication of the equipment components, prevent wear and reduce the risk of overheating under working conditions.

[0009] Further, the dynamic lubrication film thickness monitoring and adjustment in S3 includes: Lubrication film thickness monitoring: installing a film thickness sensor in the lubrication contact area of the mechanical equipment, the film thickness sensor monitors the thickness of the lubrication film in real time, the film thickness sensor includes an optical sensor, a capacitive sensor or an ultrasonic sensor; Data analysis and processing: analyzing the collected lubrication film thickness data, using an improved policy gradient method to evaluate whether the thickness of the lubrication film is within the ideal range, the improved policy gradient method determines the optimal thickness of the lubrication film based on the operating parameters of the mechanical equipment and the physical characteristics of the lubricating oil; Automatic adjustment of lubricating oil supply: automatically adjusting the supply of lubricating oil according to the results of data analysis.

[0010] Further, the improved policy gradient method includes: Model definition: policy represents the probability of selecting action in a given state , is the policy parameter, state includes the operating parameters of the mechanical equipment (such as load, speed) and the physical characteristics of the lubricating oil (such as temperature, pressure), action is the adjusted lubricating oil supply, reward function is used to evaluate the effect of taking action in state , that is, the ability to maintain the ideal lubrication film thickness; Improved policy gradient formula: the improved policy gradient method updates the policy parameter , the calculation formula is: ; where, is the performance function, represents the expectation under the policy; ​​​Reward function design: The reward function is to encourage the strategy to reduce the gap between the actual lubricating film thickness and the ideal thickness. The calculation formula is: ; in, Taking action The lubricating film thickness is then measured. is the ideal lubricating film thickness; Parameter update: In each iteration, the policy parameters The update is performed by the gradient ascent method, and the calculation formula is: ; in, Is the learning rate, a pre-set positive number that controls the step size of parameter updates.

[0011] Furthermore, the automatic adjustment of the lubricating oil supply amount is achieved by controlling the speed of the lubricating oil pump or adjusting the opening of the oil supply valve. If it is monitored that the lubricating film thickness is lower than the optimal range, the lubricating oil supply amount will be increased. Conversely, if the lubricating film thickness is higher than the optimal range, the lubricating oil supply amount will be reduced.

[0012] Furthermore, the evaluating of the lubrication effect in S4 includes: Real-time data acquisition: Real-time collection of operating parameters of mechanical equipment, including load, speed, temperature and pressure, and continuous monitoring of the real-time thickness of the lubricating film; Data analysis: Based on the collected operating parameters and lubricant film thickness data, an improved Graph Attention Network (GAT) algorithm is used to predict the quality of lubrication. The model is continuously updated based on newly collected data. Comprehensive evaluation of lubrication effect: Based on the prediction results and real-time monitoring data of lubricating film thickness, the current lubrication status is comprehensively evaluated to determine whether the ideal lubrication effect is achieved. The focus is on whether the lubricating film prevents direct contact between metal parts and the wear risk under the current lubrication status.

[0013] Furthermore, the improved graph attention network (GAT) algorithm includes: Calculate the attention coefficient: For each pair of nodes and , the attention coefficient is determined in the update node The representation time node The importance of is calculated as follows: ; in, is a node and The unnormalized attention coefficient between is a nonlinear activation function, is the attention mechanism parameter, is the weight matrix, Node and The input feature vector of It is a connection operation that connects the feature vectors of two nodes together; Normalized attention weights: for all nodes To Node The attention coefficient is normalized and the calculation formula is: ; in, is the normalized attention weight, indicating that When the feature representation of The contribution of is a node The set of neighbor nodes of Node feature update: Update the feature representation of each node by weighting the feature vectors of neighboring nodes. The calculation formula is: ; in, is a node According to the updated feature representation of attention weights, is the activation function.

[0014] Furthermore, the determining of the reliability level in S5 includes: Determine reliability index: Calculate the reliability index of lubricating oil based on lubrication effect evaluation results The reliability index of lubricating oil is a comprehensive score that reflects the performance of lubricating oil in maintaining lubricating film thickness, preventing wear and maintaining chemical stability. The calculation formula is: ; in, are weight coefficients, which respectively reflect the contribution of lubricating film thickness, temperature stability, pressure stability and anti-wear performance to the overall reliability index. is an indicator of lubricating film thickness, Lubricating oil temperature stability index, Lubricating oil pressure stability index, Anti-wear performance indicators, is the normalization function; Preset reliability level standards: Set preset reliability level standards, each level standard corresponds to a different range of lubricant performance; Comparison and determination of grade: Compare the calculated lubricant reliability index with the preset reliability grade standard to determine the reliability grade of the current lubricant; Assess the overall reliability of mechanical equipment lubrication: Evaluate the reliability of the lubrication of the entire mechanical equipment based on the reliability level of the lubricant. If the reliability level of the lubricant used in all components meets or exceeds the preset minimum requirements, the overall lubrication of the mechanical equipment is reliable.

[0015] A mechanical equipment lubrication reliability assessment system is used to implement the above-mentioned industrial equipment lubrication reliability assessment method, including the following modules: Data collection module: equipped with sensors and recording devices to collect information on the operating time, temperature, load, and lubricating oil pressure of mechanical equipment; Lubrication performance evaluation module: Integrates the calculation capabilities of the Arrhenius model and the Barus model to analyze the changes in lubricant viscosity with temperature and pressure, and evaluate the flow properties and lubrication performance of the lubricant; Dynamic monitoring and adjustment module: used to monitor the lubricating film thickness in real time and dynamically adjust the lubricating oil supply according to the equipment operating conditions; Lubrication effect evaluation module: responsible for comprehensively judging the actual lubrication effect of the lubricant based on real-time monitoring data and lubrication performance evaluation results, including whether the lubricating film prevents metal contact and reduces wear; Reliability level determination module: compares the lubrication effect with the preset reliability level standard, determines the reliability level of the lubricant, and evaluates the overall reliability of the mechanical equipment lubrication; Feedback Improvement Module: Based on the reliability assessment results, if the lubrication reliability of the mechanical equipment does not meet the preset standards, the feedback improvement module will propose specific lubrication solution improvement measures, including replacing the lubricating oil, adjusting the lubrication parameters, or improving the lubrication solution design.

[0016] Beneficial effects of the present invention: The present invention accurately collects and analyzes the operating data of the equipment, including key parameters such as operating time, temperature, load and lubricating oil pressure. The comprehensive data collection provides a solid foundation for the performance evaluation of the lubricating oil, making the evaluation of the lubricating effect more accurate and comprehensive. By real-time monitoring and using the Arrhenius model and the Barus model to predict the flow properties and lubricating performance of the lubricating oil, not only the scientific nature of the evaluation is improved, but also the monitoring and adjustment of the lubrication status are more intelligent and adaptive, ensuring that the mechanical equipment always maintains the optimal lubrication status under constantly changing working conditions, and reducing the risk of wear caused by improper lubrication.

[0017] The present invention effectively adapts to changes in equipment operating conditions by dynamically monitoring the thickness of the lubricating film and timely adjusting the lubricating oil supply. This dynamic adjustment mechanism not only ensures that the thickness of the lubricating film is always in an ideal state, preventing direct contact and wear of metal parts, but also optimizes the efficiency of lubricating oil use and avoids waste of resources. In the process of evaluating the lubrication effect, special attention is paid to whether the lubricating film can prevent direct contact of metal parts and the wear risk under the current lubrication state, thereby ensuring the reliability and long-term stable operation of mechanical equipment.

[0018] The present invention provides a quantitative standard for the overall reliability assessment of mechanical equipment lubrication by determining the reliability level of the lubricant and comparing it with preset standards. This allows equipment maintenance personnel to clearly understand the performance status of the lubricant and make timely adjustments or replace the lubricant, thereby preventing potential failures in advance. In addition, if the reliability of the lubricant does not meet the preset standards, improvement measures are also provided, further enhancing the maintenance strategy of the mechanical equipment and providing an effective solution for extending the service life of the equipment and improving equipment performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 Schematic diagram of the evaluation method flow in an embodiment of the present invention; Figure 2 Schematic diagram of system function modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0022] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0023] like Figure 1 As shown, a method for evaluating the lubrication reliability of industrial equipment includes the following steps: S1, collect operating data: collect operating data of mechanical equipment, including equipment operating time, temperature, load and lubricating oil pressure; S2, lubrication performance evaluation: Based on the collected data, the flow properties and lubrication performance of the lubricant are calculated using the Arrhenius model and the Barus model. The Arrhenius model and the Barus model consider the relationship between the viscosity of the lubricant and the change of temperature and pressure respectively; S3, Dynamic Lubricating Film Thickness Monitoring and Adjustment: Implement dynamic lubricating film thickness monitoring technology to monitor and adjust the lubricating film thickness in real time to adapt to changes in equipment operating conditions. By using sensor technology to monitor the film thickness in the lubricating contact area, the lubricating oil supply is automatically adjusted to ensure optimal lubrication. S4, Lubrication Effectiveness Evaluation: Based on the real-time monitoring results of flow properties, lubrication performance, and lubricating film, the lubricating effect of the lubricant under the current working conditions is evaluated, including whether the lubricating film formed by the lubricant prevents metal contact and reduces wear; S5, determine the reliability level: determine the reliability level of the lubricating oil based on the lubrication effect, and compare it with the preset reliability level standard to evaluate the overall reliability of the mechanical equipment lubrication; S6, propose improvement measures: If the reliability of mechanical equipment lubrication does not meet the preset standards, propose improvement measures for the lubrication scheme, including replacing the lubricating oil, adjusting the working parameters of the lubrication scheme, or improving the lubrication scheme design; The above method can accurately evaluate and optimize the lubrication status of mechanical equipment, thereby improving lubrication reliability, reducing equipment failures and extending equipment service life.

[0024] The collected operation data in S1 includes: Equipment operating time recording: By installing a time recorder on mechanical equipment, the equipment's startup, operation, and shutdown time are automatically recorded. The time recorder provides data on the equipment's cumulative operating time, providing basic information for evaluating lubrication needs and equipment wear status; Temperature monitoring: Using temperature sensors, we monitor and record temperature changes in the moving parts and lubrication areas of mechanical equipment in real time. By monitoring the temperature of the equipment and lubricating oil, we can analyze the thermal stability of the lubricating oil and the heat load of the equipment under different operating conditions. This ensures the optimal performance of the equipment and its lubrication system in various operating environments and promptly identifies potential problems caused by abnormal temperatures. Load monitoring: Force sensors are used to measure and record the actual load borne by mechanical equipment during operation. Load data is crucial for understanding the working status of the equipment and evaluating the load-bearing capacity of the lubricant. Lubricating oil pressure monitoring: The pressure sensor is used to monitor the pressure of the lubricating oil in the lubrication path in real time. The monitoring of the lubricating oil pressure can not only ensure the normal operation of the lubricating counterweight, but also serve as an important basis for detecting whether the lubricating oil channel is unobstructed and whether there is leakage. By integrating these monitoring tools and methods, the evaluation method can comprehensively and accurately collect key data of mechanical equipment in actual operation, providing solid data support for lubrication reliability evaluation, thereby making the evaluation results more accurate and reliable.

[0025] The lubrication performance evaluation in S2 includes: Arrhenius model application: The Arrhenius model is used to calculate and predict the viscosity of lubricating oil under different temperature conditions, helping to evaluate the flow properties of lubricating oil. The calculation formula is: ; in, Represents temperature The viscosity of the lubricating oil under is the viscosity at the reference temperature, is the activation energy, is the gas constant, is the temperature of the lubricating oil ; Application of Barus model: The Barus model evaluates the viscosity change of lubricating oil under different pressures, thereby determining the lubricating performance of lubricating oil under high load conditions. The calculation formula is: ; in, It's pressure The viscosity of the lubricating oil under is the viscosity at standard atmospheric pressure, is the pressure coefficient of the lubricating oil, is the pressure of the lubricating oil ; Performance evaluation: based on the calculation results of the Arrhenius model and the Barus model, the flow performance and lubrication performance of the lubricating oil are comprehensively evaluated, considering the relationship between the viscosity of the lubricating oil and the temperature and pressure, to determine whether the lubricating oil can maintain the lubrication of the equipment components, prevent wear and reduce the risk of overheating under specific working conditions; Through the above method, using specific physical models and actual collected data, the lubrication configuration can be accurately evaluated and optimized, improving the operating efficiency and reliability of mechanical equipment.

[0026] Dynamic lubrication film thickness monitoring and adjustment in S3 includes: Lubrication film thickness monitoring: install a film thickness sensor in the lubrication contact area of the mechanical equipment, the film thickness sensor monitors the thickness of the lubrication film in real time, the film thickness sensor includes an optical sensor, a capacitive sensor or an ultrasonic sensor, the film thickness sensor has high sensitivity and accuracy, and can accurately measure the thickness of the lubrication film under different working conditions; Data analysis and processing: analyze the collected lubrication film thickness data, and use the improved policy gradient method to evaluate whether the thickness of the lubrication film is within the ideal range, the improved policy gradient method determines the optimal thickness of the lubrication film based on the operating parameters of the mechanical equipment and the physical properties of the lubricating oil; Automatic adjustment of lubricating oil supply: automatically adjust the supply of lubricating oil according to the data analysis results; Through the above method, the mechanical equipment can obtain the best lubrication effect under various operating conditions, reduce the risk of wear and tear, improve the operating efficiency of the equipment and prolong its service life.

[0027] The improved policy gradient method includes: Model definition: policy represents the probability of selecting action in a given state , is the policy parameter, state includes the operating parameters of the mechanical equipment (such as load, speed) and the physical properties of the lubricating oil (such as temperature, pressure), action is the adjusted lubricating oil supply, reward function is used to evaluate the effect of taking action in state , that is, the ability to maintain the ideal lubrication film thickness, the reward is defined based on the difference between the lubrication film thickness and the ideal thickness; Improved policy gradient formula: the improved policy gradient method adjusts the policy parameter To update, the calculation formula is: ; in, is the performance function, which represents the expected value of the long-term reward, Express expectations under the strategy; Reward function design: The reward function is to encourage the strategy to reduce the gap between the actual lubricating film thickness and the ideal thickness. The calculation formula is: ; in, Taking action The lubricating film thickness is then measured. is the ideal lubricating film thickness; Parameter update: In each iteration, the policy parameters The update is performed by the gradient ascent method, and the calculation formula is: ; in, is the learning rate, a pre-set positive number that controls the step size of parameter updates; The improved policy gradient method can be applied to adjust the lubricant supply in real time in response to changes in the operating state of mechanical equipment and changes in the physical properties of the lubricant, thereby dynamically maintaining the lubricating film within the ideal thickness range. By continuously learning and updating the strategy parameters, this method can adapt to complex working conditions, optimize the lubrication maintenance of the equipment, and ensure long-term equipment efficiency and reliability. This method optimizes the lubrication process through real-time monitoring and adaptive adjustment, reduces wear or damage caused by improper lubrication, thereby extending the service life of the equipment and improving its performance.

[0028] Automatic adjustment of lubricating oil supply quantity is achieved by controlling the speed of the lubricating oil pump or adjusting the opening of the oil supply valve. If the lubricating film thickness is monitored to be lower than the optimal range, the lubricating oil supply quantity will be increased. Conversely, if the lubricating film thickness is higher than the optimal range, the lubricating oil supply quantity will be reduced to ensure that the dynamic adjustment of lubricating oil supply matches the actual needs of the equipment.

[0029] The evaluation of lubrication effect in S4 includes: Real-time data acquisition: Real-time collection of operating parameters of mechanical equipment, including load, speed, temperature and pressure, and continuous monitoring of the real-time thickness of the lubricating film; Data analysis: Based on the collected operating parameters and lubricant film thickness data, an improved Graph Attention Network (GAT) algorithm is used to predict the quality of lubrication. The model is continuously updated based on newly collected data to improve the accuracy and adaptability of the prediction; Comprehensive lubrication effect evaluation: Based on the prediction results and real-time monitoring data of lubricating film thickness, the current lubrication status is comprehensively evaluated to determine whether the ideal lubrication effect is achieved, focusing on whether the lubricating film prevents direct contact between metal parts and the wear risk under the current lubrication status; In the present invention, the method for evaluating lubrication effect makes the monitoring and adjustment of lubrication status more intelligent and adaptive. Through real-time data collection and data analysis, the method can accurately evaluate the lubrication effect and promptly detect problems of insufficient lubrication or excessive lubrication. This not only ensures the efficient operation of mechanical equipment, but also extends the service life of the equipment and reduces maintenance costs.

[0030] The improved Graph Attention Network (GAT) algorithm includes: Calculate the attention coefficient: For each pair of nodes and , the attention coefficient is determined in the update node The representation time node The importance of is calculated as follows: ; in, is a node and The unnormalized attention coefficient between It is a nonlinear activation function that allows small gradients to pass when the input is negative, avoiding the "dead neuron" problem. is the attention mechanism parameter, is the weight matrix, used for linear transformation of node features, Node and The input feature vector of It is a connection operation that connects the feature vectors of two nodes together; Normalized attention weights: for all nodes To Node The attention coefficient is normalized and the calculation formula is: ; in, is the normalized attention weight, indicating that When the feature representation of The contribution of is a node The set of neighbor nodes of Node feature update: Update the feature representation of each node by weighting the feature vectors of neighboring nodes. The calculation formula is: ; in, is a node According to the updated feature representation of attention weights, It is an activation function, which is used to introduce nonlinearity and increase the expressive power of the model; Through the attention mechanism, GAT can automatically learn and emphasize the relationships and influences between different lubrication points in mechanical equipment, and can identify the most critical parts for maintaining the ideal lubrication film thickness, thereby providing an accurate basis for the formulation and adjustment of lubrication strategies. During the operation of mechanical equipment, working conditions (such as temperature, load) and environmental factors (such as humidity and temperature changes) will continue to change. The GAT model can dynamically adjust its learned attention weights according to real-time data, thereby achieving a rapid response to these changes. Using GAT's comprehensive analysis of the lubrication system, it can more accurately predict the changing trend of the lubrication film thickness and the quality of the lubrication effect. Accurate lubrication effect prediction and timely lubrication strategy adjustment can significantly reduce the risks and costs brought about by over-lubrication or under-lubrication.

[0031] Determining reliability levels in S5 includes: Determine reliability index: Calculate the reliability index of lubricating oil based on lubrication effect evaluation results The reliability index of lubricating oil is a comprehensive score that reflects the performance of lubricating oil in maintaining lubricating film thickness, preventing wear and maintaining chemical stability. The calculation formula is: ; in, are weight coefficients, which respectively reflect the contribution of lubricating film thickness, temperature stability, pressure stability and anti-wear performance to the overall reliability index. is an indicator of lubricating film thickness, Lubricating oil temperature stability index, Lubricating oil pressure stability index, Anti-wear performance indicators, It is a normalization function that ensures that each indicator contributes to the total score at the same level; The ratio of the measured lubricating film thickness to the ideal lubricating film thickness is used to calculate the thickness. The calculation formula is: ,in, is the flow rate of lubricating oil (usually expressed in liters per minute), is the area covered by the lubricating film (usually expressed in square meters), is the time the lubricating oil flows (usually expressed in minutes); It is calculated by measuring the lubricating oil temperature fluctuation range. The calculation formula is: ,in, It's in time The temperature measurement value, is the average temperature, is the number of temperature measurements; Evaluate by lubricating oil pressure fluctuation range, the calculation formula is: ,in, It's in time The pressure measurement value, is the average pressure, is the number of pressure measurements; It is determined by comparing the changes in the degree of wear of mechanical parts before and after lubrication. The calculation formula is: ,in, is the depth of the component after wear, is the depth of the component before wear; Preset reliability grade standards: Set preset reliability grade standards, each grade standard corresponds to a different range of lubricant performance. For example, the grades can be set from "A" to "E", where "A" represents the highest reliability and "E" represents the lowest; Comparison and determination of grade: The calculated lubricant reliability index is compared with the preset reliability grade standard to determine the reliability grade of the current lubricant. If the reliability index of the lubricant meets or exceeds the requirements of a preset grade, the lubricant is classified into that grade. Assess the overall reliability of mechanical equipment lubrication: Based on the reliability level of the lubricant, the reliability of the lubrication of the entire mechanical equipment is assessed. If the reliability level of the lubricant used in all components meets or exceeds the preset minimum requirements, the overall lubrication of the mechanical equipment is reliable; The above method can systematically evaluate the reliability level of lubricants and, accordingly, assess the overall reliability of the mechanical equipment lubrication system. This method helps identify potential risk points in the lubrication system and guides maintenance personnel in making targeted lubricant selections and adjustments, thereby optimizing the equipment's operating performance and extending its service life.

[0032] like Figure 2 As shown, a mechanical equipment lubrication reliability evaluation system is used to implement the above-mentioned industrial equipment lubrication reliability evaluation method, including the following modules: Data collection module: equipped with sensors and recording devices to collect information on the operating time, temperature, load, and lubricating oil pressure of mechanical equipment; Lubrication performance evaluation module: Integrates the calculation capabilities of the Arrhenius model and the Barus model to analyze the changes in lubricant viscosity with temperature and pressure, and evaluate the flow properties and lubrication performance of the lubricant; Dynamic monitoring and adjustment module: used to monitor the lubricating film thickness in real time and dynamically adjust the lubricating oil supply according to the equipment operating conditions; Lubrication effect evaluation module: responsible for comprehensively judging the actual lubrication effect of the lubricant based on real-time monitoring data and lubrication performance evaluation results, including whether the lubricating film can prevent metal contact and reduce wear; Reliability level determination module: compares the lubrication effect with the preset reliability level standard, determines the reliability level of the lubricant, and evaluates the overall reliability of the mechanical equipment lubrication; Feedback Improvement Module: Based on the reliability assessment results, if the lubrication reliability of the mechanical equipment does not meet the preset standards, the feedback improvement module will propose specific lubrication solution improvement measures, including replacing the lubricating oil, adjusting the lubrication parameters, or improving the lubrication solution design.

[0033] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Within the scope of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0034] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the lubrication reliability of industrial equipment, characterized in that: The following steps are involved: S1, collect operating data: collect operating data of mechanical equipment, including equipment operating time, temperature, load and lubricating oil pressure; S2, lubrication performance evaluation: Based on the collected data, the flow properties and lubrication performance of the lubricant are calculated using the Arrhenius model and the Barus model. The Arrhenius model and the Barus model consider the relationship between the viscosity of the lubricant and the change of temperature and pressure respectively; S3, Dynamic Lubricating Film Thickness Monitoring and Adjustment: Implement dynamic lubricating film thickness monitoring technology to monitor and adjust the lubricating film thickness in real time to adapt to changes in equipment operating conditions. By using sensor technology to monitor the film thickness in the lubricating contact area, the lubricating oil supply is automatically adjusted to ensure optimal lubrication. S4, Lubrication Effectiveness Evaluation: Based on the real-time monitoring results of flow properties, lubrication performance, and lubricating film, the lubricating effect of the lubricant under the current working conditions is evaluated, including whether the lubricating film formed by the lubricant prevents metal contact and reduces wear. An improved graph attention network algorithm is used to predict the quality of the lubrication effect; S5, determine the reliability level: determine the reliability level of the lubricant based on the lubrication effect, and compare it with the preset reliability level standard to evaluate the overall reliability of the mechanical equipment lubrication. The reliability level includes calculating the reliability index of the lubricant based on the lubrication effect evaluation result. The reliability index of lubricating oil is a comprehensive score that reflects the performance of lubricating oil in maintaining lubricating film thickness, preventing wear and maintaining chemical stability. The calculation formula is: ； in, are weight coefficients, which respectively reflect the contribution of lubricating film thickness, temperature stability, pressure stability and anti-wear performance to the overall reliability index. is an indicator of lubricating film thickness, Lubricating oil temperature stability index, Lubricating oil pressure stability index, Anti-wear performance indicators, is the normalization function; S6, propose improvement measures: If the reliability of mechanical equipment lubrication does not meet the preset standards, propose improvement measures for the lubrication scheme, including replacing the lubricating oil, adjusting the working parameters of the lubrication scheme, or improving the lubrication scheme design; The collected operation data in S1 includes: Equipment operation time record: by installing a time recorder on the mechanical equipment, the equipment's startup, operation and shutdown time are automatically recorded; Temperature monitoring: Use temperature sensors to monitor and record temperature changes in the moving parts and lubrication areas of mechanical equipment in real time. By monitoring the temperature of the equipment body and lubricating oil, the thermal stability of the lubricating oil and the heat load of the equipment under different working conditions can be analyzed. Load monitoring: Use force sensors to measure and record the actual load borne by mechanical equipment during operation; Lubricating oil pressure monitoring: Use pressure sensors to monitor the pressure of the lubricating oil in the lubrication path in real time.

2. The method for evaluating the lubrication reliability of industrial equipment according to claim 1, characterized in that: The lubrication performance evaluation in S2 includes: Arrhenius model application: The Arrhenius model is used to calculate and predict the viscosity of lubricating oil under different temperature conditions, helping to evaluate the flow properties of lubricating oil. The calculation formula is: ； in, Represents temperature The viscosity of the lubricating oil under is the viscosity at the reference temperature, is the activation energy, is the gas constant, is the temperature of the lubricating oil; Application of Barus model: The Barus model evaluates the viscosity change of lubricating oil under different pressures, thereby determining the lubricating performance of lubricating oil under high load conditions. The calculation formula is: ； in, It's pressure The viscosity of the lubricating oil under is the viscosity at standard atmospheric pressure, is the pressure coefficient of the lubricating oil, is the lubricating oil pressure; Performance evaluation: Combining the calculation results of the Arrhenius model and the Barus model, comprehensively evaluate the flow properties and lubrication performance of the lubricant. Considering the relationship between the change of lubricant viscosity with temperature and pressure, it is determined whether the lubricant maintains lubrication of equipment components under operating conditions, prevents wear and reduces the risk of overheating.

3. The method for evaluating the lubrication reliability of industrial equipment according to claim 2, characterized in that: The dynamic lubricating film thickness monitoring and adjustment in S3 includes: Lubricating film thickness monitoring: Install a film thickness sensor in the lubricating contact area of ​​the mechanical equipment to monitor the thickness of the lubricating film in real time. The film thickness sensor includes an optical sensor, a capacitive sensor, or an ultrasonic sensor. Data analysis and processing: Analyze the collected lubricant film thickness data and use an improved policy gradient method to assess whether the lubricant film thickness is within the ideal range. The improved policy gradient method determines the optimal lubricant film thickness based on the operating parameters of the mechanical equipment and the physical properties of the lubricant. Automatic adjustment of lubricating oil supply: Automatically adjust the lubricating oil supply according to data analysis results.

4. The method for evaluating the lubrication reliability of industrial equipment according to claim 3, characterized in that: The improved policy gradient method includes: Model Definition: Strategy Indicates that in a given state Select Action The probability of is the policy parameter, state Including the operating parameters of mechanical equipment and the physical properties of lubricating oil, action is the adjusted lubricant supply, the reward function To evaluate the status Take action The effect of lubrication, that is, the ability to maintain an ideal lubricating film thickness; Improved policy gradient formula: Improved policy gradient method for policy parameters To update, the calculation formula is: ； in, is the performance function, Express expectations under the strategy; Reward function design: The reward function is to encourage the strategy to reduce the gap between the actual lubricating film thickness and the ideal thickness. The calculation formula is: ； in, Taking action The lubricating film thickness is then measured. is the ideal lubricating film thickness; Parameter update: In each iteration, the policy parameters The update is performed by the gradient ascent method, and the calculation formula is: ； in, Is the learning rate, a pre-set positive number that controls the step size of parameter updates.

5. The method for evaluating the lubrication reliability of industrial equipment according to claim 4, characterized in that: The automatic adjustment of the lubricating oil supply amount is achieved by controlling the speed of the lubricating oil pump or adjusting the opening of the oil supply valve. If the lubricating film thickness is monitored to be lower than the optimal range, the lubricating oil supply amount will be increased. Conversely, if the lubricating film thickness is higher than the optimal range, the lubricating oil supply amount will be reduced.

6. The method for evaluating the lubrication reliability of industrial equipment according to claim 5, characterized in that: The evaluation of lubrication effect in S4 includes: Real-time data acquisition: Real-time collection of operating parameters of mechanical equipment, including load, speed, temperature and pressure, and continuous monitoring of the real-time thickness of the lubricating film; Data analysis: Based on the collected operating parameters and lubricant film thickness data, an improved graph attention network algorithm is used to predict the quality of lubrication. The model is continuously updated based on newly collected data. Comprehensive evaluation of lubrication effect: Based on the prediction results and real-time monitoring data of lubricating film thickness, the current lubrication status is comprehensively evaluated to determine whether the ideal lubrication effect is achieved. The focus is on whether the lubricating film prevents direct contact between metal parts and the wear risk under the current lubrication status.

7. The method for evaluating the lubrication reliability of industrial equipment according to claim 6, characterized in that: The improved graph attention network algorithm includes: Calculate the attention coefficient: For each pair of nodes and , the attention coefficient is determined in the update node The representation time node The importance of is calculated as follows: ； in, is a node and The unnormalized attention coefficient between is a nonlinear activation function, is the attention mechanism parameter, is the weight matrix, Node and The input feature vector of It is a connection operation that connects the feature vectors of two nodes together; Normalized attention weights: for all nodes To Node The attention coefficient is normalized and the calculation formula is: ； in, is the normalized attention weight, indicating that When the feature representation of The contribution of is a node The set of neighbor nodes of Node feature update: Update the feature representation of each node by weighting the feature vectors of neighboring nodes. The calculation formula is: ； in, is a node According to the updated feature representation of attention weights, is the activation function.

8. The method for evaluating the lubrication reliability of industrial equipment according to claim 7, characterized in that: The S5 also includes: Preset reliability level standards: Set preset reliability level standards, each level standard corresponds to a different range of lubricant performance; Comparison and determination of grade: Compare the calculated lubricant reliability index with the preset reliability grade standard to determine the reliability grade of the current lubricant; Assess the overall reliability of mechanical equipment lubrication: Evaluate the reliability of the lubrication of the entire mechanical equipment based on the reliability level of the lubricant. If the reliability level of the lubricant used in all components meets or exceeds the preset minimum requirements, the overall lubrication of the mechanical equipment is reliable.

Images