An evaluation method and system for the lubrication reliability of industrial equipment

Abstract

The present invention relates to the technical field of mechanical equipment, and specifically relates to a method and system for evaluating the lubrication reliability of industrial equipment, including the following steps: S1, collecting operation data: collecting the operation data of mechanical equipment; S2, evaluating lubrication performance: calculating the flow performance and lubrication performance of lubricating oil by using the Arrhenius model and the Barus model; S3, dynamic monitoring and adjustment of lubricating film thickness: implementing a technology for dynamically monitoring the lubricating film thickness, and real-time monitoring and adjusting the thickness of the lubricating film; S4, evaluating lubrication effect: evaluating the lubrication effect of lubricating oil under the current working conditions; S5, determining the reliability level: determining the reliability level of the lubricating oil; S6, proposing improvement measures: if the reliability of the mechanical equipment lubrication does not meet the preset standard, proposing improvement measures for the lubrication scheme. The present invention ensures that the mechanical equipment always maintains the best lubrication state under changing conditions, and reduces the risks caused by improper lubrication.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical equipment, and particularly to a method and system for evaluating the lubrication reliability of industrial equipment. Background Art

[0002] In modern industry, the stable operation of mechanical equipment has extremely high requirements for 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 laborious, but also often cannot accurately reflect the actual lubrication conditions, especially in situations where the equipment working conditions are complex or change frequently. In addition, the flow performance and lubrication performance of lubricating oil will change under different temperature and pressure conditions, and it is difficult for traditional methods to accurately evaluate the changes of these performance indicators in real time, thus unable to make timely adjustments to optimize the lubrication effect.

[0003] Currently, although there are various sensor technologies for monitoring the operating status of equipment, these technologies still have deficiencies in integrating and analyzing the collected data to achieve intelligent management of the lubrication system. In particular, there is a lack of a systematic assessment method to monitor the thickness of the lubricating film in real time and dynamically adjust the lubrication strategy according to the monitoring results to cope with the changes in working conditions. In addition, there is usually no unified quantitative standard for the reliability assessment of lubricating oil, making it difficult to evaluate and compare the performance of different lubricating oils, resulting in the overall reliability of equipment lubrication being difficult to guarantee. Summary of the Invention

[0004] Based on the above purposes, the present invention provides a method and system for evaluating the lubrication reliability of industrial equipment.

[0005] A method for evaluating the lubrication reliability of industrial equipment includes the following steps:

[0006] S1, collecting operation data: Collecting the operation data of the mechanical equipment, including the operation time, temperature, load, and lubricating oil pressure of the equipment;

[0007] S2, evaluating lubrication performance: Based on the collected data, using the Arrhenius model and the Barus model to calculate the flow performance and lubrication performance of the lubricating oil. The Arrhenius model and the Barus model respectively consider the relationship between the viscosity of the lubricating oil and the changes in temperature and pressure;

[0008] S3, dynamic monitoring and adjustment of the lubricating film thickness: Implementing a technology for dynamically monitoring the thickness of the lubricating film, monitoring and adjusting the thickness of the lubricating film in real time to adapt to the changes in the equipment operating conditions. By using sensor technology to monitor the film thickness in the lubricating contact area and automatically adjusting the supply amount of the lubricating oil to ensure the optimal lubrication effect;

[0009] S4, Evaluate the lubrication effect: Based on the fluidity, lubrication performance, and real-time monitoring results of the lubricating film, evaluate the lubrication effect of the lubricating oil under the current working conditions, including evaluating whether the lubricating film formed by the lubricating oil can prevent metal contact and reduce wear;

[0010] S5, Determine the reliability level: According to the lubrication effect, determine the reliability level of the lubricating oil and compare it with the preset reliability level standard to evaluate the overall reliability of the lubrication of mechanical equipment;

[0011] S6, Propose improvement measures: If the reliability of the lubrication of mechanical equipment does not meet the preset standard, propose improvement measures for the lubrication plan, including replacing the lubricating oil, adjusting the working parameters of the lubrication plan, or improving the lubrication plan design.

[0012] Furthermore, the collection of operation data in S1 includes:

[0013] Equipment operation time record: Automatically record the startup, operation, and shutdown times of the equipment by installing a time recorder on the mechanical equipment;

[0014] Temperature monitoring: Use temperature sensors to monitor and record the temperature changes in real time at the moving parts and lubricated parts of the mechanical equipment. By monitoring the temperature of the equipment body and the lubricating oil, analyze the thermal stability of the lubricating oil and the thermal load conditions of the equipment under different working conditions;

[0015] Load monitoring: Measure and record the actual load borne by the mechanical equipment during operation through force sensors;

[0016] Lubricating oil pressure monitoring: Real-time monitor the pressure of the lubricating oil in the lubrication path through pressure sensors.

[0017] Furthermore, the evaluation of lubrication performance in S2 includes:

[0018] Application of the Arrhenius model: The Arrhenius model is used to calculate and predict the viscosity of the lubricating oil under different temperature conditions to help evaluate the fluidity of the lubricating oil. The calculation formula is:

[0019] ;

[0020] Where, represents the viscosity of the lubricating oil at temperature , is the viscosity at the reference temperature, is the activation energy, is the gas constant, is the temperature of the lubricating oil ;

[0021] Application of Barus Model: The Barus model is used to evaluate the viscosity change of lubricating oil under different pressures, thereby judging the lubrication performance of lubricating oil under high-load conditions. The calculation formula is:

[0022] ;

[0023] where, is the viscosity of the lubricating oil under pressure , is the viscosity under standard atmospheric pressure, is the pressure coefficient of the lubricating oil, is the pressure of the lubricating oil ;

[0024] Performance Evaluation: Combining the calculation results of the Arrhenius model and the Barus model, comprehensively evaluate the flow performance and lubrication performance of the lubricating oil. Considering the relationship between the viscosity of the lubricating oil and temperature and pressure changes, judge whether the lubricating oil can maintain the lubrication of equipment components under working conditions, prevent wear and reduce the risk of overheating.

[0025] Furthermore, the dynamic lubricating film thickness monitoring and adjustment in S3 includes:

[0026] Lubricating Film Thickness Monitoring: Install a film thickness sensor in the lubricating contact area of the mechanical equipment. The film thickness sensor monitors the thickness of the lubricating film in real time. The film thickness sensor includes an optical sensor, a capacitive sensor or an ultrasonic sensor;

[0027] Data Analysis and Processing: Analyze the collected lubricating film thickness data, and use the improved policy gradient method to evaluate whether the thickness of the lubricating film is within the ideal range. The improved policy gradient method determines the optimal thickness of the lubricating film based on the operating parameters of the mechanical equipment and the physical properties of the lubricating oil;

[0028] Automatic Adjustment of Lubricating Oil Supply Quantity: Automatically adjust the supply quantity of the lubricating oil according to the data analysis results.

[0029] Furthermore, the improved policy gradient method includes:

[0030] Model Definition: Policy represents the probability of selecting action under the given state , are policy parameters. The 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). The action is the adjusted supply quantity of the lubricating oil. The reward function is used to evaluate taking action under the state The effect, i.e., the ability to maintain the ideal lubricating film thickness;

[0031] Improved policy gradient formula: The improved policy gradient method updates the policy parameters with the calculation formula:

[0032] ;

[0033] where is the performance function, representing the expectation under the policy;

[0034] Reward function design: The reward function is designed to encourage the policy to reduce the gap between the actual lubricating film thickness and the ideal thickness, with the calculation formula:

[0035] ;

[0036] where is the lubricating film thickness measured after taking the action , is the ideal lubricating film thickness;

[0037] Parameter update: In each iteration, the policy parameter is updated by the gradient ascent method, with the calculation formula:

[0038] ;

[0039] where is the learning rate, a pre-set positive number that controls the step size of parameter update.

[0040] Furthermore, the automatic adjustment of the lubricating oil supply volume is achieved by controlling the speed of the lubricating oil pump or adjusting the opening degree of the oil supply valve to adjust the supply volume of the lubricating oil. If it is monitored that the lubricating film thickness is lower than the optimal range, the supply volume of the lubricating oil will be increased. Conversely, if the lubricating film thickness is higher than the optimal range, the supply volume of the lubricating oil will be decreased.

[0041] Furthermore, the evaluation of the lubrication effect in S4 includes:

[0042] Real-time data collection: Continuously collect the parameters during the operation of the mechanical equipment, including load, speed, temperature, and pressure, and continuously monitor the real-time thickness of the lubricating film;

[0043] Data analysis: According to the collected operation parameters and lubricating film thickness data, use the improved Graph Attention Network (GAT) algorithm to predict the quality of the lubrication effect, and continuously update the model according to the newly collected data;

[0044] Comprehensive evaluation of lubrication effect: Based on the prediction results and real-time monitoring data of the lubricating film thickness, comprehensively evaluate the current lubrication state, determine whether the ideal lubrication effect is achieved, pay attention to whether the lubricating film prevents direct contact between metal components, and the wear risk under the current lubrication state.

[0045] Furthermore, the improved Graph Attention Network (GAT) algorithm includes:

[0046] Calculating attention coefficients: For each pair of nodes and , the attention coefficient determines the importance of node when updating the representation of node . The calculation formula is:

[0047] ;

[0048] where is the unnormalized attention coefficient between nodes and , is a non-linear activation function, is the attention mechanism parameter, is the weight matrix, are the input feature vectors of nodes and respectively, is the concatenation operation that concatenates the feature vectors of two nodes together;

[0049] Normalizing attention weights: Normalize the attention coefficients from all nodes to node . The calculation formula is:

[0050] ;

[0051] where is the normalized attention weight, representing the contribution degree of node when updating the feature representation of node , is the set of neighbor nodes of node ;

[0052] Updating node features: Update the feature representation of each node by weighting the feature vectors of neighbor nodes. The calculation formula is:

[0053] ;

[0054] where is the feature representation of node updated according to the attention weight, is the activation function.

[0055] Further, the determination of the reliability level in S5 includes:

[0056] Determine the reliability index: Based on the lubrication effect evaluation results, calculate the reliability index of the lubricating oil , and the reliability index of the lubricating oil is a comprehensive score, reflecting the performance of the lubricating oil in maintaining the lubricating film thickness, preventing wear, and maintaining chemical stability. The calculation formula is:

[0057] ;

[0058] Among them, are the weight coefficients, respectively reflecting the contribution degrees of the lubricating film thickness, temperature stability, pressure stability, and anti-wear performance to the total reliability index, is the lubricating film thickness index, is the stability index of the lubricating oil temperature, is the stability index of the lubricating oil pressure, is the anti-wear performance index, is the normalization function;

[0059] Preset the reliability level standard: Set the preset reliability level standard, and each level standard corresponds to a different range of lubricating oil performance;

[0060] Compare and determine the level: Compare the calculated reliability index of the lubricating oil with the preset reliability level standard to determine the current reliability level of the lubricating oil;

[0061] Evaluate the overall reliability of the mechanical equipment lubrication: According to the reliability level of the lubricating oil, evaluate the reliability of the entire mechanical equipment lubrication. If the reliability levels of the lubricating oils used in all components reach or exceed the preset minimum requirements, the lubrication of the mechanical equipment is overall reliable.

[0062] A reliability evaluation system for mechanical equipment lubrication, used to implement the above-mentioned reliability evaluation method for industrial equipment lubrication, includes the following modules:

[0063] Data collection module: Equipped with sensors and recording devices, used to collect the running time, temperature, load, and lubricating oil pressure of the mechanical equipment;

[0064] Lubrication performance evaluation module: Integrate the calculation capabilities of the Arrhenius model and the Barus model, analyze the change of the viscosity of the lubricating oil with temperature and pressure, and evaluate the flow performance and lubrication performance of the lubricating oil;

[0065] Dynamic monitoring and adjustment module: Used to monitor the lubricating film thickness in real time and dynamically adjust the supply amount of the lubricating oil according to the operating conditions of the equipment;

[0066] Lubrication effect evaluation module: Responsible for comprehensively judging the actual lubrication effect of the lubricating oil based on real-time monitoring data and lubrication performance evaluation results, including whether the lubricating film prevents metal contact and reduces wear;

[0067] Reliability level determination module: Compare the lubrication effect with the preset reliability level standard, determine the reliability level of the lubricating oil, and evaluate the overall reliability of the mechanical equipment lubrication;

[0068] Feedback and improvement module: According to the reliability evaluation results, if the lubrication reliability of the mechanical equipment does not meet the preset standard, the feedback and improvement module will propose specific improvement measures for the lubrication scheme, including replacing the lubricating oil, adjusting the lubrication parameters or improving the lubrication scheme design.

[0069] Advantages of the present invention:

[0070] In the present invention, by accurately collecting and analyzing the operation data of the equipment, including key parameters such as operation 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 lubrication effect more accurate and comprehensive. By real-time monitoring and using the Arrhenius model and Barus model to predict the flow performance and lubrication performance of the lubricating oil, not only the scientific nature of the evaluation is improved, but also the monitoring and adjustment of the lubrication state are made more intelligent and adaptive, ensuring that the mechanical equipment always maintains the best lubrication state under changing working conditions and reducing the wear risk caused by improper lubrication.

[0071] In the present invention, by dynamically monitoring the thickness of the lubricating film and timely adjusting the supply amount of the lubricating oil, it effectively adapts to the changes in the equipment operation conditions. 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 use efficiency of the lubricating oil, avoiding waste of resources. During 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, thus ensuring the reliability and long-term stable operation of the mechanical equipment.

[0072] In the present invention, by determining the reliability level of the lubricating oil and comparing it with the preset standard, a quantitative standard is provided for the overall reliability evaluation of the mechanical equipment lubrication. This enables the equipment maintenance personnel to clearly understand the performance status of the lubricating oil, make adjustments or replace the lubricating oil in a timely manner, thereby preventing potential failures in advance. In addition, if the reliability of the lubricating oil does not meet the preset standard, improvement measures are also provided, further enhancing the maintenance strategy of the mechanical equipment and providing an effective solution for extending the equipment service life and improving the equipment performance. Description of the Drawings

[0073] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0074] Figure 1 Schematic diagram of the evaluation method process for the embodiment of the present invention;

[0075] Figure 2 Schematic diagram of the system function module for the embodiment of the present invention. Detailed implementation manners

[0076] To make the objectives, technical solutions and advantages of the present invention more clearly understood, the following further details the present invention in conjunction with specific embodiments.

[0077] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0078] As Figure 1 shown, an industrial equipment lubrication reliability evaluation method includes the following steps:

[0079] S1. Collect operation data: Collect the operation data of mechanical equipment, including the operation time, temperature, load and lubricating oil pressure of the equipment;

[0080] S2. Evaluate lubrication performance: Based on the collected data, use the Arrhenius model and the Barus model to calculate the flow performance and lubrication performance of the lubricating oil. The Arrhenius model and the Barus model respectively consider the relationships between the viscosity of the lubricating oil and temperature and pressure changes;

[0081] S3, Dynamic Lubricating Film Thickness Monitoring and Adjustment: Implement the technology of dynamically monitoring the thickness of the lubricating film, real-time monitor and adjust the thickness of the lubricating film to adapt to the changes in the operating conditions of the equipment. By using sensor technology to monitor the film thickness in the lubricating contact area and automatically adjust the supply amount of the lubricating oil to ensure the optimal lubrication effect;

[0082] S4, Evaluate Lubrication Effect: Based on the flow performance, lubrication performance, and real-time monitoring results of the lubricating film, evaluate the lubrication effect of the lubricating oil under the current working conditions, including evaluating whether the lubricating film formed by the lubricating oil can prevent metal contact and reduce wear;

[0083] S5, Determine Reliability Level: According to the lubrication effect, determine the reliability level of the lubricating oil and compare it with the preset reliability level standard to evaluate the overall reliability of the lubrication of the mechanical equipment;

[0084] S6, Propose Improvement Measures: If the reliability of the lubrication of the mechanical equipment does not meet the preset standard, propose improvement measures for the lubrication scheme, including replacing the lubricating oil, adjusting the working parameters of the lubrication scheme, or improving the design of the lubrication scheme;

[0085] The above method can accurately evaluate and optimize the lubrication state of mechanical equipment, thereby improving lubrication reliability, reducing equipment failures, and extending the service life of the equipment.

[0086] The collection of operation data in S1 includes:

[0087] Equipment Operation Time Record: Automatically record the start-up, operation, and shutdown times of the equipment by installing a time recorder on the mechanical equipment. The time recorder provides data on the cumulative operation time of the equipment, providing basic information for evaluating lubrication requirements and the wear state of the equipment;

[0088] Temperature Monitoring: Use temperature sensors to real-time monitor and record temperature changes in the moving parts and lubricated parts of the mechanical equipment. By monitoring the temperature of the equipment body and the lubricating oil, analyze the thermal stability of the lubricating oil and the thermal load conditions of the equipment under different working conditions. In this way, the best performance of the equipment and its lubrication system in various operating environments can be ensured, and potential problems caused by abnormal temperatures can be detected in a timely manner;

[0089] Load Monitoring: Measure and record the actual load borne by the mechanical equipment during operation through force sensors. Load data is crucial for understanding the working state of the equipment and evaluating the load-bearing capacity of the lubricating oil;

[0090] Lubricating Oil Pressure Monitoring: Real-time monitor the pressure of the lubricating oil in the lubrication path through pressure sensors. Monitoring the lubricating oil pressure can not only ensure the normal operation of the lubrication counterweight but also serve as an important basis for detecting whether the lubricating oil channel is unobstructed and whether there are leaks, etc.;

[0091] By integrating these monitoring tools and methods, this evaluation method can comprehensively and accurately collect key data of mechanical equipment during actual operation, providing solid data support for lubrication reliability evaluation, thus making the evaluation results more accurate and reliable.

[0092] The evaluation of lubrication performance in S2 includes:

[0093] Application of Arrhenius model: The Arrhenius model is used to calculate and predict the viscosity of lubricating oil under different temperature conditions, helping to evaluate the flow performance of lubricating oil. The calculation formula is:

[0094] ;

[0095] Where, represents the viscosity of the lubricating oil at temperature , is the viscosity at the reference temperature, is the activation energy, is the gas constant, is the temperature of the lubricating oil ;

[0096] Application of Barus model: The Barus model evaluates the viscosity change of lubricating oil under different pressures, thereby judging the lubrication performance of lubricating oil under high load conditions. The calculation formula is:

[0097] ;

[0098] Where, is the pressure under which the viscosity of the lubricating oil, is the viscosity under standard atmospheric pressure, is the pressure coefficient of the lubricating oil, is the pressure of the lubricating oil ;

[0099] Performance evaluation: Combining the calculation results of the Arrhenius model and the Barus model, comprehensively evaluate the flow performance and lubrication performance of the lubricating oil, considering the relationship between the viscosity of the lubricating oil and the changes in temperature and pressure, and judge whether the lubricating oil can maintain the lubrication of equipment components, prevent wear and reduce the risk of overheating under specific working conditions;

[0100] Through the above methods, using specific physical models and actual collected data, it is possible to accurately evaluate and optimize the lubrication configuration, improving the operation efficiency and reliability of mechanical equipment.

[0101] The dynamic lubricating film thickness monitoring and adjustment in S3 includes:

[0102] Lubricating film thickness monitoring: Install a film thickness sensor in the lubricating contact area of the mechanical equipment. The film thickness sensor monitors the thickness of the lubricating 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 lubricating film under different working conditions;

[0103] Data analysis and processing: Analyze the collected lubricating film thickness data, and use the improved policy gradient method to evaluate whether the thickness of the lubricating film is within the ideal range. The improved policy gradient method determines the optimal thickness of the lubricating film based on the operating parameters of the mechanical equipment and the physical properties of the lubricating oil;

[0104] Automatic adjustment of lubricating oil supply: Automatically adjust the supply of lubricating oil according to the data analysis results;

[0105] Through the above method, it is possible to ensure that the mechanical equipment obtains the best lubrication effect under various operating conditions, reduce the wear risk, improve the operating efficiency of the equipment and extend its service life.

[0106] The improved policy gradient method includes:

[0107] Model definition: The policy represents the probability of selecting an action under a given state . is the policy parameter. The 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). The action is the adjusted lubricating oil supply. The reward function is used to evaluate the effect of taking an action under the state , that is, the ability to maintain the ideal lubricating film thickness. The reward is defined based on the gap between the lubricating film thickness and the ideal thickness;

[0108] Improved policy gradient formula: The improved policy gradient method updates the policy parameter , and the calculation formula is:

[0109] ;

[0110] where is the performance function, representing the expected value of the long-term reward, represents the expectation under the policy;

[0111] Reward function design: The reward function is designed to encourage the policy to reduce the gap between the actual lubricating film thickness and the ideal thickness. The calculation formula is:

[0112] ;

[0113] Among them, is the lubricating film thickness measured after taking action and is the ideal lubricating film thickness;

[0114] Parameter update: In each iteration, the policy parameter is updated by the gradient ascent method, and the calculation formula is:

[0115] ;

[0116] Among them, is the learning rate, a preset positive number that controls the step size of parameter update;

[0117] The improved policy gradient method can be applied to adjust the lubricating oil supply in real time in response to changes in the operating state of mechanical equipment and changes in the physical properties of lubricating oil, so as to dynamically maintain the lubricating film within the ideal thickness range. By continuously learning and updating policy parameters, this method can adapt to complex working conditions, optimize the lubrication maintenance of equipment, 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.

[0118] The automatic adjustment of the lubricating oil supply is achieved by controlling the speed of the lubricating oil pump or adjusting the opening of the oil supply valve to adjust the supply volume of the lubricating oil. If it is detected that the lubricating film thickness is lower than the optimal range, the supply volume of the lubricating oil will be increased. Conversely, if the lubricating film thickness is higher than the optimal range, the supply volume of the lubricating oil will be reduced to ensure that the dynamic adjustment of the lubricating oil supply matches the actual needs of the equipment.

[0119] The evaluation of the lubrication effect in S4 includes:

[0120] Real-time data acquisition: Real-time collect the parameters of the mechanical equipment during operation, including load, speed, temperature and pressure, and continuously monitor the real-time thickness of the lubricating film;

[0121] Data analysis: According to the collected operating parameters and lubricating film thickness data, use the improved graph attention network (GAT) algorithm to predict the quality of the lubrication effect, and continuously update the model according to the newly collected data to improve the accuracy and adaptability of the prediction;

[0122] Comprehensive evaluation of lubrication effect: Based on the prediction results and real-time monitoring data of the lubricating film thickness, comprehensively evaluate the current lubrication state, judge whether the ideal lubrication effect is achieved, pay attention to whether the lubricating film prevents direct contact between metal parts, and the wear risk under the current lubrication state;

[0123] In the present invention, the method for evaluating the lubrication effect makes the monitoring and adjustment of the lubrication state more intelligent and adaptive. Through real-time data acquisition and data analysis, this method can accurately evaluate the lubrication effect, timely detect problems of insufficient lubrication or over-lubrication, which not only ensures the efficient operation of mechanical equipment, but also extends the service life of the equipment and reduces the maintenance cost.

[0124] The improved Graph Attention Network (GAT) algorithm includes:

[0125] Calculating the attention coefficient: For each pair of nodes and , the attention coefficient determines the importance of node when updating the representation of node . The calculation formula is:

[0126] ;

[0127] where, is the unnormalized attention coefficient between nodes and , is a non-linear activation function that allows small gradients to pass through when the input is negative, avoiding the "dead neuron" problem, is the attention mechanism parameter, is the weight matrix for the linear transformation of node features, are the input feature vectors of nodes and respectively, is the concatenation operation that concatenates the feature vectors of two nodes together;

[0128] Normalizing the attention weights: Normalize the attention coefficients from all nodes to node . The calculation formula is:

[0129] ;

[0130] where, is the normalized attention weight, indicating the contribution degree of node when updating the feature representation of node , is the set of neighbor nodes of node ;

[0131] Updating the node features: Update the feature representation of each node by weighting the feature vectors of neighbor nodes. The calculation formula is:

[0132] ;

[0133] where, is a node The feature representation updated according to the attention weights is an activation function, which is used to introduce non-linearity and increase the expressive power of the model;

[0134] Through the attention mechanism, GAT can automatically learn and emphasize the mutual relationships and influences between different lubrication points in mechanical equipment, and can identify the most critical parts for maintaining the ideal lubricant film thickness, thus 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, temperature change) will change continuously. The GAT model can dynamically adjust the learned attention weights according to real-time data, so as to achieve a rapid response to these changes. By using the comprehensive analysis of GAT for the lubrication system, the change trend of the lubricant film thickness and the quality of the lubrication effect can be predicted more accurately. Accurate prediction of the lubrication effect and timely adjustment of the lubrication strategy can significantly reduce the risks and costs brought by over-lubrication or under-lubrication.

[0135] Determining the reliability level in S5 includes:

[0136] Determining the reliability index: Based on the lubrication effect evaluation results, calculate the reliability index of the lubricating oil , and the reliability index of the lubricating oil is a comprehensive score, which reflects the performance of the lubricating oil in maintaining the lubricant film thickness, preventing wear and maintaining chemical stability. The calculation formula is:

[0137] ;

[0138] Among them, are weight coefficients, which respectively reflect the contribution degrees of the lubricant film thickness, temperature stability, pressure stability and anti-wear performance to the total reliability index, is the lubricant film thickness index, is the stability index of the lubricating oil temperature, is the stability index of the lubricating oil pressure, is the anti-wear performance index, is a normalization function to ensure that each index contributes to the total score on the same order of magnitude;

[0139] It is calculated by the ratio of the measured thickness of the lubricant film to the ideal lubricant film thickness. The calculation formula is:

[0140] , among which, is the flow rate of the lubricating oil (usually expressed in liters per minute), is the area covered by the lubricant film (usually expressed in square meters), is the time for the lubricating oil to flow (usually expressed in minutes);

[0141] Calculated by measuring the fluctuation range of the lubricating oil temperature, and the calculation formula is:

[0142] , where is the temperature measurement value at time , is the average temperature, is the number of temperature measurements;

[0143] Evaluated by the fluctuation range of the lubricating oil pressure, and the calculation formula is:

[0144] , where is the pressure measurement value at time , is the average pressure, is the number of pressure measurements;

[0145] Determined by comparing the change in the wear degree of mechanical components before and after lubrication, and the calculation formula is:

[0146] , where is the depth of the component after wear, is the depth of the component before wear;

[0147] Preset reliability level standard: Set the preset reliability level standard, and each level standard corresponds to a different range of lubricating oil performance. For example, the levels can be set from "A" to "E", where "A" represents the highest reliability and "E" represents the lowest;

[0148] Compare and determine the level: Compare the calculated reliability index of the lubricating oil with the preset reliability level standard to determine the current reliability level of the lubricating oil. If the reliability index of the lubricating oil meets or exceeds the requirements of a certain preset level, then the lubricating oil is classified into that level;

[0149] Evaluate the overall reliability of the lubrication of mechanical equipment: According to the reliability level of the lubricating oil, evaluate the reliability of the lubrication of the entire mechanical equipment. If the reliability levels of the lubricating oils used for all components reach or exceed the preset minimum requirements, then the lubrication of the mechanical equipment is overall reliable;

[0150] Through the above method, the reliability level of the lubricating oil can be systematically evaluated, and based on this, the overall reliability of the lubrication system of the mechanical equipment can be evaluated. This method helps to identify potential risk points in the lubrication system, guide maintenance personnel to make targeted lubricating oil selection and adjustment, so as to optimize the operation performance of the equipment and extend the service life of the equipment.

[0151] As shown Figure 2 in the figure, a lubrication reliability evaluation system for mechanical equipment is used to implement the above-mentioned lubrication reliability evaluation method for industrial equipment, and includes the following modules:

[0152] Data collection module: Equipped with sensors and recording devices, it is used to collect the running time, temperature, load, and lubricating oil pressure of mechanical equipment;

[0153] Lubrication performance evaluation module: Integrating the calculation capabilities of the Arrhenius model and the Barus model, it analyzes the change of the viscosity of lubricating oil with temperature and pressure, and evaluates the flow performance and lubrication performance of lubricating oil;

[0154] Dynamic monitoring and adjustment module: It is used to monitor the lubricating film thickness in real time and dynamically adjust the supply amount of lubricating oil according to the operating conditions of the equipment;

[0155] Lubrication effect evaluation module: Responsible for comprehensively judging the actual lubrication effect of lubricating oil based on real-time monitoring data and lubrication performance evaluation results, including whether the lubricating film can prevent metal contact and reduce wear;

[0156] Reliability level determination module: Compares the lubrication effect with the preset reliability level standard, determines the reliability level of lubricating oil, and evaluates the overall reliability of mechanical equipment lubrication;

[0157] Feedback and improvement module: According to the reliability evaluation results, if the lubrication reliability of mechanical equipment does not meet the preset standard, the feedback and improvement module will propose specific improvement measures for the lubrication plan, including replacing lubricating oil, adjusting lubrication parameters, or improving the lubrication plan design.

[0158] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present invention is limited to these examples; under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the present invention as described above, which are not provided in detail for the sake of brevity.

[0159] 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 principle of the present invention shall be included within the protection scope 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 operation data: collect the operation data of mechanical equipment, including the operation time, temperature, load and lubricating oil pressure of the equipment; S2, evaluate lubrication performance: based on the collected data, the flow properties and lubrication performance of the lubricant are calculated using the Arrhenius model and the Barus model, which respectively consider the relationship between the viscosity of the lubricant and changes in temperature and pressure; S3, Dynamic lubrication film thickness monitoring and adjustment: Implement dynamic monitoring of lubrication film thickness technology, monitor and adjust the thickness of the lubrication film in real time to adapt to changes in equipment operating conditions, monitor the film thickness of the lubrication contact area by using sensor technology, and automatically adjust the lubricant supply to ensure the best lubrication effect; S4, evaluate the lubrication effect: based on the real-time monitoring results of flow properties, lubrication performance and lubrication film, evaluate the lubrication effect of the lubricant under the current working conditions, including evaluating whether the lubrication film formed by the lubricant prevents metal contact and reduces wear. The 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 according to 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 design of the lubrication scheme.

2. The method for evaluating the lubrication reliability of industrial equipment according to claim 1, characterized in that: The collected operation data in S1 includes: Equipment operation time record: by installing a time recorder on the mechanical equipment, the equipment 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 parts 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 thermal load of the equipment under different working conditions can be analyzed. Load monitoring: Use force sensors to measure and record the actual load that the mechanical equipment is subjected to during operation; Lubricating oil pressure monitoring: Use pressure sensors to monitor the pressure of the lubricating oil in the lubrication path in real time.

3. The method for evaluating the lubrication reliability of industrial equipment according to claim 2, characterized in that: The lubrication performance evaluated 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, Represent 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, so as to judge 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: Combined with the calculation results of the Arrhenius model and the Barus model, the flow properties and lubrication performance of the lubricant are comprehensively evaluated. The relationship between the viscosity of the lubricant and the change of temperature and pressure is considered to determine whether the lubricant can maintain the lubrication of equipment parts under working conditions, prevent wear and reduce the risk of overheating.

4. The method for evaluating the lubrication reliability of industrial equipment according to claim 3, 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 lubrication contact area of ​​the mechanical equipment. The film thickness sensor monitors 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 lubricating film thickness data and use the improved policy gradient method to evaluate whether the thickness of the lubricating film is within the ideal range. The improved policy gradient method determines the optimal thickness of the lubricating 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 lubricating oil supply according to the data analysis results.

5. The method for evaluating the lubrication reliability of industrial equipment according to claim 4, characterized in that: The improved policy gradient method includes: Model Definition: Strategy Indicates that in a given state Next 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, and 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 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.

6. The method for evaluating the lubrication reliability of industrial equipment according to claim 5, characterized in that: The automatic regulation of the 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 it is monitored that the lubricating film thickness is 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.

7. The method for evaluating the lubrication reliability of industrial equipment according to claim 6, characterized in that: The evaluation of lubrication effect in S4 includes: Real-time data collection: real-time collection of parameters of mechanical equipment during operation, 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 effects, and 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 lubrication film thickness, comprehensively evaluate the current lubrication status to determine whether the ideal lubrication effect has been achieved, pay attention to whether the lubrication film prevents direct contact between metal parts, and the wear risk under the current lubrication status.

8. The method for evaluating the lubrication reliability of industrial equipment according to claim 7, 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: ； in, Is a node and The unnormalized attention coefficient between is a nonlinear activation function, is the attention mechanism parameter, is the weight matrix, The nodes are and The input feature vector is is a connection operation, which 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 updating a node 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.

9. The method for evaluating the lubrication reliability of industrial equipment according to claim 8, 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: According to the reliability level of the lubricant, assess the reliability of the lubrication of the entire mechanical equipment. If the reliability level of the lubricant used in all components meets or exceeds the preset minimum requirements, the lubrication of the mechanical equipment is reliable as a whole.

10. A mechanical equipment lubrication reliability evaluation system, used to implement an industrial equipment lubrication reliability evaluation method as described in any one of claims 1 to 9, characterized in that: Includes the following modules: Data collection module: equipped with sensors and recording devices to collect 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, analyzes the changes in lubricant viscosity with temperature and pressure, and evaluates 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 lubricating oil, adjusting lubrication parameters or improving lubrication solution design.

Images