Automatic adjusting strategy and system for intelligent lubricating system

Abstract

The invention relates to an automatic adjustment strategy and system for an intelligent lubrication system, and belongs to the technical field of operation and maintenance of wind turbine generators. S2, acquiring an equipment operation vibration state index S; s3, acquiring an equipment vibration operation grease index G; s4, acquiring an equipment operation temperature index T; and S5, obtaining the actual amount Q of lubricating grease needing to be supplemented. The system is used for filling the bearing with a lubricant; the system comprises a dynamic grease injection execution module. The dynamic grease injection execution module comprises a lubrication execution part and a lubrication power part which are connected through a pipeline; the lubricating power part comprises a quantitative grease injection pump and an oil storage tank which are connected through a control valve liquid path; the lubrication execution part is connected with a grease injection pump through an oil inlet pipeline a; the lubrication execution part is connected with the oil storage tank through a pipeline b; the device is reasonable in design, compact in structure and convenient to use.

Description

Technical Field

[0001] This invention relates to the field of wind turbine operation and maintenance lubrication technology, specifically to an intelligent lubrication system automatic adjustment strategy and system for intelligent lubrication of key bearings in wind turbines. Background Technology

[0002] Wind turbines operate in complex outdoor environments for extended periods, and their key components, such as main shaft bearings and gearbox bearings, are subjected to high loads and variable speeds. The lubrication effect directly determines the bearing life and the stability of the turbine's operation.

[0003] Traditional wind turbine lubrication systems often employ a "timed and quantitative" grease injection mode, which involves injecting a fixed amount of grease according to a preset cycle (such as every 72 hours). This mode has significant drawbacks: on the one hand, when the bearing experiences a surge in lubrication demand due to increased load or rising ambient temperature, the fixed amount of grease cannot meet the demand, easily leading to dry friction and a sudden temperature rise in the bearing, causing jamming or wear failures; on the other hand, when the bearing is under low load and low temperature conditions, excessive grease injection will cause grease accumulation, increasing the bearing's operating resistance. At the same time, excess grease leakage will also cause environmental pollution and resource waste.

[0004] Therefore, it is impossible to achieve the requirement of grease injection as needed under different operating conditions of the bearing, resulting in insufficient lubrication under high load conditions and grease waste under low load conditions. Furthermore, in the existing operation and maintenance model, bearing fault detection mostly relies on regular manual inspections or simple over-limit alarms using temperature sensors, which cannot predict bearing deterioration trends in advance. When the temperature or vibration signal reaches the alarm threshold, the bearing has often already suffered irreversible damage, requiring shutdown and replacement, resulting in high operation and maintenance costs and power generation losses.

[0005] Traditional bearing waste grease recycling relies on the internal pressure of the bearing to squeeze it out, which can easily cause blockages. Even with an independent waste grease auxiliary recycling system, it can only achieve periodic and quantitative recycling, without being linked to the grease injection logic of the lubrication system.

[0006] Traditional equipment status monitoring typically relies on vibration, temperature, and noise. By the time abnormalities are detected, the bearings have often already suffered irreversible damage, requiring shutdown and replacement.

[0007] Traditional bearing grease condition monitoring requires personnel to extract a small amount of grease through the bearing bore for offline monitoring, which is time-consuming and labor-intensive. This is especially true for wind turbines, which typically require an interval of more than six months between inspections. Sampling at a single location is not continuous and has extremely low accuracy.

[0008] Therefore, there is an urgent need for an intelligent lubrication system that can sense bearing conditions in real time, dynamically match lubrication needs, and have fault prediction capabilities to solve the pain points of traditional technologies. Summary of the Invention

[0009] In general, the technical problem to be solved by this invention is to provide an intelligent lubrication system with an automatic adjustment strategy and system. By monitoring and analyzing the grease inside the bearing online, the bearing condition can be warned in advance. At the same time, the grease inside the bearing can be cleaned and replaced through the logic of grease injection and waste grease discharge, thereby improving the bearing's operating environment and preventing further deterioration.

[0010] This invention improves the accuracy of detection by installing multiple waste grease auxiliary recycling systems on the bearing and continuously collecting and mixing waste grease from multiple points for dynamic real-time detection.

[0011] This invention effectively integrates an intelligent lubrication system with a waste grease auxiliary recycling system, allowing them to share a single grease pump as a power source. Simultaneously, it can automatically add grease in appropriate amounts and discharge waste grease based on feedback from the bearing's working status. To solve the above problems, the technical solution adopted by the present invention is as follows: An automatic adjustment strategy for an intelligent lubrication system, comprising the following steps: S1, Calculation method for itemized scores; S2, obtain the equipment operating vibration state index S; S3, obtain the grease index G for equipment vibration operation; S4, obtain the equipment operating temperature index T; S5, obtain the actual amount of grease to be replenished, Q.

[0012] Furthermore, in step S1, S1.1, vibration amplitude value is divided Static strength assessment was conducted. Vibration amplitude is the basis for determining whether equipment is overloaded or unbalanced; The formula is as follows: ; Parameter definition, Current vibration amplitude of the equipment; Warning threshold; Danger threshold; S1.2, Vibration frequency score To conduct fault characteristic assessment; Identify fault characteristic frequencies through spectrum analysis. The formula is as follows: ; Parameter definition, : The number of detected fault feature frequencies; : Severity weight of the i-th type of fault; Vibration power at the characteristic frequency of the i-th type of fault; Root mean square power of vibration across the entire frequency band; S1.3, Vibration Trend Score To conduct dynamic change assessment; Based on linear trend analysis of historical data, the formula is as follows: ; Parameter definition: : Slope of linear trend of vibration amplitude, unit: mm / s / day, based on the most recent set number of days; : Maximum permissible deterioration slope.

[0013] Furthermore, in step S2, the vibration state index S of the equipment operation ranges from 1 to 100, with a higher value indicating a better operating state. The calculation formula is as follows: ; in, Vibration amplitude weight; The amplitude of vibration is scored, with a value range of 0 to 100. Vibration frequency weight, with a value range of 0 to 100; Vibration frequency score, with a value range of 0 to 100; Oscillation trend weight; The vibration trend score ranges from 0 to 100.

[0014] Furthermore, in step S3, the equipment vibration operation grease index G ranges from 1 to 100, with a higher value indicating a better operating condition. The calculation formula is as follows: ; in, ; ; Parameter definition: Metal content score in oils and fats, 0-100; Water content score in oils and fats, 0–100; N: The percentage of metals in the oil, in ppm; M: Water content in oils and fats, unit: ppm.

[0015] Furthermore, in step S4, the equipment operating temperature index T ranges from 1 to 100, with higher values ​​indicating better operating conditions. The calculation formula is as follows: ; Parameter definition: The formula for calculating the difference between the actual bearing temperature and the reference temperature.

[0016] Bearing reference temperature, here is .

[0017] Furthermore, in step S5, the steps for obtaining the actual amount of grease to be replenished, Q, are as follows: ; ; in, Q: The actual amount of grease to be added, with a range of (0.3×) ~5× ); Theoretical grease replenishment amount for bearings under ideal operating conditions; S: Equipment operating vibration state index, based on step S2; G: Equipment operating grease deterioration index, based on step S3; T: Temperature index of equipment operating status, based on step S4; K: Correction factor for grease injection volume.

[0018] An intelligent lubrication system with automatic adjustment, used to add lubricant to bearings; The system includes a dynamic grease injection execution module; The dynamic grease injection module includes a lubrication actuator and a lubrication power unit connected by pipelines; The lubrication and power unit includes a metered grease pump and an oil reservoir connected via a control valve fluid circuit; The lubrication actuator is connected to the grease pump via oil inlet line a; The lubrication actuator is connected to the oil reservoir via pipe b; Several grease filling ports and grease suction ports are distributed on the bearing; The lubrication actuator includes a grease distribution valve for lubricating the bearing and a grease suction device with a corresponding suction port; The grease extractor is used to remove bearing lubricating oil; The grease filling port is connected to the grease distribution valve via pipe C; The grease pump is connected to the inlet of the grease distribution valve and / or line e via inlet line a; The oil storage tank is connected to the inlet of the grease distribution valve via pipe b or pipe e.

[0019] Furthermore, the control valve is either a directional valve or a shut-off valve; When the control valve is a directional valve, a check valve is installed at the outlet of the directional valve; The reversing valve is installed on oil inlet line a and line b; When a shut-off valve is used as the control valve, the shut-off valve is installed on pipeline b. A valve plunger monitoring sensor is installed on the grease distribution valve; A pressure sensor is installed in pipeline e; The dynamic grease injection module is connected to a dynamic grease removal module via piping. The dynamic fat removal module includes a waste oil collection device; The liposuction device is connected to a waste oil collection device via tubing d; e; the liquid control chamber connection tube of the liposuction device; A multi-dimensional dynamic sensing module is used to collect bearing operating parameters in real time, including a grease condition detection sensor installed on pipeline d, a vibration sensor and / or a temperature sensor installed at the bearing. The multi-dimensional dynamic sensing module is electrically connected to the intelligent control module; The intelligent control module includes an analysis module; The intelligent control module includes a backend; the backend includes a processor. The processor is electrically connected to a controller for controlling the rotation of the motor; The processor is electrically connected to a data memory; The processor is electrically connected to the early warning and interaction module.

[0020] Furthermore, the oil storage tank is equipped with a liquid level sensor; The back-end is electrically connected to the liquid level sensor, the distribution valve plunger monitoring sensor, the control valve, the grease pump, the pressure sensor, the grease status detection sensor, the vibration sensor, and / or the temperature sensor, respectively. The back-end systems are electrically connected to fixed terminals and / or mobile terminals respectively.

[0021] Furthermore, the liposuction device includes a liposuction housing; A hydraulic cavity and an inner cavity are provided in the liposuction housing; A piston body with a spring return mechanism is movable within the inner cavity; A pipe a is connected to the hydraulic cavity, which pushes the piston to move, thereby realizing the linkage between liposuction and fat removal. A knob plug is connected to the outlet of the inner cavity; A sealing plug is connected to the knob plug by a spring. Under the action of the spring force, the sealing plug blocks the connection between the knob plug outlet and the inner cavity. A bypass hole is provided on the side wall of the liposuction housing, which is normally blocked and closed by a sealing plug; When the piston moves to the left, the sealing plug moves to the left against the spring force, thereby connecting the inner cavity with the bypass hole; Bypass hole connects to pipe d; The temperature sensor is installed at the contact point between the inner and outer rings of the bearing. The vibration sensor is a piezoelectric accelerometer, which is installed in the vertical and horizontal directions of the bearing housing respectively; The grease condition detection sensor integrates sensors for detecting metal impurities and moisture; a concentration block is installed on the pipe d; the grease condition detection sensor is inserted into the inlet of the liposuction device or discharged into the concentration block; The multi-dimensional dynamic sensing module communicates with the intelligent control module via an RS485 bus; The intelligent control module uses the GD32E50x series MCU, which has data receiving, logic operation and command sending functions; after receiving the real-time parameters transmitted by the multi-dimensional dynamic sensing module, it compares them with the preset model; calculates the amount of grease required for the current bearing and sends control commands to the dynamic grease injection execution module; at the same time, it uploads the real-time parameters and calculation results to the data storage device, thereby enabling the big data storage and analysis module. The preset model is established based on the working condition and grease injection volume benchmark; Dynamic grease injection execution module: After receiving instructions from the intelligent control module, the grease injection pump starts working to deliver high-pressure grease through pipelines to the grease distribution valve. The intelligent control module achieves precise control of the grease injection volume by monitoring the feedback from the plunger sensor inside the grease distribution valve.

[0022] After receiving instructions from the intelligent control module, the grease pump controls the operation of the grease pump, controls the valve to switch, and delivers high-pressure grease to the hydraulic chamber of the liposuction device as power to drive the liposuction device to work. Through negative pressure, the waste grease inside the bearing is extracted and transported to the waste grease collection device. The collected grease is detected by the grease status detection sensor.

[0023] The analytics module includes local edge computing nodes and cloud servers with data connectivity; Local edge computing nodes use industrial-grade solid-state drives for caching and offline analysis; Cloud server with built-in bearing condition prediction model; This predictive model is trained and generated using historical operation and maintenance data. It employs the Long Short-Term Memory (LSTM) algorithm and takes temperature change trends, vibration spectrum characteristics, and grease viscosity change rates as input parameters. The output results are the bearing health score and fault type prediction for a future set time period. Historical operation and maintenance data includes samples of normal operating conditions, insufficient lubrication, deteriorated grease, and / or bearing wear. Vibration spectrum characteristics include peak value and / or root mean square value; Health score definition: 0-100 points, the lower the score, the higher the risk of failure; Fault type predictions include insufficient lubrication, grease aging, and rolling element wear; The early warning interaction module includes a local audible and visual alarm and a remote operation and maintenance platform; The local audible and visual alarm is installed in the wind turbine control cabinet. The alarm threshold can be set. When the health score is 40 < health score ≤ 60, a yellow warning is triggered. When the health score is ≤ 40, a red warning is triggered. The remote operation and maintenance platform supports access from both web and mobile devices. It is used to display bearing operating parameters, grease injection records and health curves in real time. When a red warning is issued, it automatically sends a text message to the operation and maintenance personnel, which includes the fault prediction type and / or suggested handling solutions. During the analysis module processing, an intelligent lubrication system automatically adjusts the lubrication amount strategy.

[0024] This invention achieves precise lubrication: by sensing the bearing condition in real time through multi-dimensional sensors, the amount of grease injected is dynamically adjusted to avoid "excessive waste" and "insufficient damage". Tests have shown that it can reduce grease consumption by 30% and extend the bearing service life by 20%.

[0025] This invention provides early fault warning: based on big data and LSTM algorithm, it predicts bearing failure risk 72 hours in advance, transforming "post-event maintenance" into "pre-event warning", reducing unplanned downtime of wind turbines. In actual application, it can reduce operation and maintenance costs by 15%.

[0026] This invention is intelligent and scalable: the system integrates local edge computing and cloud analysis, supports cluster management of multiple units, and has the ability to iteratively optimize models. It can be adapted to bearings of different types of wind turbines and has a wide range of applications.

[0027] This invention enables full circulation of grease, avoids increasing the load, and achieves precise and high-quality regulation.

[0028] This invention is reasonably designed, low in cost, sturdy and durable, safe and reliable, simple to operate, time-saving and labor-saving, cost-saving, compact in structure and easy to use. Attached Figure Description

[0029] Figure 1 This is an overall schematic diagram of the present invention.

[0030] Figure 2 This is a schematic diagram of the grease injection working structure of Embodiment 1 of the present invention.

[0031] Figure 3 This is a schematic diagram of the liposuction working structure of Embodiment 1 of the present invention.

[0032] Figure 4 This is a schematic diagram of the internal structure of the liposuction device of the present invention.

[0033] Figure 5 This is a schematic diagram of the operation of the single-line lubrication system of the present invention.

[0034] The components include: 1. Grease pump; 2. Control valve; 3. Check valve; 4. Grease distribution valve; 5. Grease suction device; 6. Pressure sensor; 7. Grease status detection sensor; 8. Vibration sensor; 9. Temperature sensor; 10. Waste grease collection device; 11. Back-end system; 12. Fixed terminal; 13. Mobile terminal; 14. Bearing; 1.1. Liquid level sensor; 4.1. Distribution valve plunger monitoring sensor; 14.1. Grease inlet; 14.2. Grease suction inlet; 15. Grease suction housing; 16. Piston body; 17. Inlet; 18. Inner cavity; 19. Sealing plug; 20. Knob plug; 21. Outlet; 22. Bypass hole; 23. Hydraulic chamber. Detailed Implementation

[0035] Example 1, such as Figure 1-5 An intelligent lubrication system automatic adjustment strategy for wind turbine bearing lubrication, the strategy steps are as follows: S1, Calculation method for itemized scores; S2, obtain the equipment operating vibration state index S; S3, obtain the grease index G for equipment vibration operation; S4, obtain the equipment operating temperature index T; S5, obtain the actual amount of grease to be replenished, Q.

[0036] In step S1, S1.1, vibration amplitude value is divided Static strength assessment was conducted. Vibration amplitude is the basis for determining whether equipment is overloaded or unbalanced; The formula is as follows: ; Parameter definition, A: Current vibration amplitude of the equipment; Warning threshold; Danger threshold; S1.2, Vibration frequency score To conduct fault characteristic assessment; Identify fault characteristic frequencies through spectrum analysis. The formula is as follows: ; Parameter definition, : The number of detected fault feature frequencies; : Severity weight of the i-th type of fault; Vibration power at the characteristic frequency of the i-th type of fault; Root mean square power of vibration across the entire frequency band; S1.3, Vibration Trend Score To conduct dynamic change assessment; Based on linear trend analysis of historical data, the formula is as follows: ; Parameter definition: : Slope of linear trend of vibration amplitude, unit: mm / s / day, based on the most recent set number of days; : Maximum permissible deterioration slope.

[0037] In step S2, the vibration state index S of the equipment operates within a range of 1 to 100. A higher value indicates a better operating state. The calculation formula is as follows: ; in, Vibration amplitude weight; The amplitude of vibration is scored, with a value range of 0 to 100. Vibration frequency weight, with a value range of 0 to 100; Vibration frequency score, with a value range of 0 to 100; Oscillation trend weight; The vibration trend score ranges from 0 to 100.

[0038] In step S3, the grease index G for equipment vibration operation ranges from 1 to 100. A higher value indicates a better operating condition. The calculation formula is as follows: ; in, ; ; Parameter definition: Metal content score in oils and fats, 0-100; Water content score in oils and fats, 0–100; N: The percentage of metals in the oil, in ppm; M: Water content in oils and fats, unit: ppm.

[0039] In step S4, the equipment operating temperature index T ranges from 1 to 100, with higher values ​​indicating better operating conditions. The calculation formula is as follows: ; Parameter definition: The formula for calculating the difference between the actual bearing temperature and the reference temperature.

[0040] Bearing reference temperature, here is .

[0041] In step S5, the steps for obtaining the actual amount of grease to be replenished, Q, are as follows: ; ; in, Q: The actual amount of grease to be added, with a range of (0.3×) ~5× ); Theoretical grease replenishment amount for bearings under ideal operating conditions; S: Equipment operating vibration state index, based on step S2; G: Equipment operating grease deterioration index, based on step S3; T: Temperature index of equipment operating status, based on step S4; K: Correction factor for grease injection volume.

[0042] Example 2, as Figure 1-5 An intelligent lubrication system automatic adjustment system is used to add lubricant to bearing 14; The system includes a dynamic grease injection execution module; Used to execute the above strategies; The dynamic grease injection module includes a lubrication actuator and a lubrication power unit connected by pipelines; The lubrication power unit includes a metered grease pump 1 and an oil reservoir connected via a control valve 2 hydraulic circuit; The lubrication actuator is connected to the grease pump 1 via the oil inlet line a; The lubrication actuator is connected to the oil reservoir via pipe b; Several grease inlets 14.1 and grease suction inlets 14.2 are distributed on the bearing 14; The lubrication actuator includes a grease distribution valve 4 for lubricating the bearing 14 and a grease suction device 5 corresponding to the grease suction port 14.2; The grease extractor 5 is used to remove the lubricating oil from the bearing 14; Grease filling port 14.1 is connected to grease distribution valve 4 via pipe c; Grease pump 1 is connected to the inlet of grease distribution valve 4 and / or line e via oil inlet line a; The oil storage tank is connected to the inlet of the grease distribution valve 4 or to pipeline e via pipeline b.

[0043] Among them, control valve 2 is a directional valve or a shut-off valve; When the control valve 2 is a directional valve, a check valve 3 is provided at the outlet of the directional valve; The reversing valve is installed on oil inlet line a and line b; When control valve 2 is a shut-off valve, the shut-off valve is installed on pipeline b; A distribution valve plunger monitoring sensor 4.1 is installed on the grease distribution valve 4; A pressure sensor 6 is installed in pipeline e; The dynamic grease injection module is connected to a dynamic grease removal module via piping. The dynamic fat removal execution module includes a waste oil collection device 10; The liposuction device 5 is connected to the waste oil collection device 10 via the tube d; The bypass hole 22 of the liposuction device is connected to the tubing e; A multi-dimensional dynamic sensing module is used to collect bearing operating parameters in real time, including a grease condition detection sensor 7 installed on pipeline d, a vibration sensor 8 installed at bearing 14, and / or a temperature sensor 9. The multi-dimensional dynamic sensing module is electrically connected to the intelligent control module; The intelligent control module includes an analysis module; The intelligent control module includes a backend 11; the backend 11 includes a processor; The processor is electrically connected to a controller for controlling the rotation of the motor; The processor is electrically connected to a data memory; The processor is electrically connected to the early warning and interaction module.

[0044] The oil storage tank is equipped with a liquid level sensor 1.1; The back-end 11 is electrically connected to the liquid level sensor 1.1, the distribution valve plunger monitoring sensor 4.1, the control valve 2, the grease pump 1, the pressure sensor 6, the grease status detection sensor 7, the vibration sensor 8, and / or the temperature sensor 9, respectively. The back-end 11 is electrically connected to a fixed terminal 12 and / or a mobile terminal 13.

[0045] The liposuction device 5 includes a liposuction housing 15; A hydraulic cavity 23 and an inner cavity 18 are provided in the liposuction housing 15; A spring-returning piston body 16 is movably disposed in the inner cavity 18; A pipe a is connected to the hydraulic cavity 23, which pushes the piston body 16 to move, thereby realizing the linkage action of liposuction and lipo expulsion. A knob plug 20 is connected to the outlet 21 of the inner cavity 18; A sealing plug 19 is connected to the knob plug 20 via a spring. Under the action of the spring force, the sealing plug 19 blocks the connection between the outlet of the knob plug 20 and the inner cavity 18. A bypass hole 22 is provided on the side wall of the liposuction housing 15, which is normally blocked and closed by the sealing plug 19; When the piston body 16 moves to the left, the sealing plug 19 moves to the left against the spring force, thereby connecting the inner cavity 18 with the bypass hole 22. Bypass hole 22 connects to pipe d; Temperature sensor 9 is installed at the contact point between the inner and outer rings of the bearing. Vibration sensor 8 is a piezoelectric accelerometer, which is installed in the vertical and horizontal directions of the bearing housing respectively; The grease condition sensor 7 integrates sensors for detecting metal impurities and moisture; a concentration block is set on the pipeline d; the grease condition sensor 7 is inserted into the inlet of the liposuction device 5 or discharged into the concentration block; The multi-dimensional dynamic sensing module communicates with the intelligent control module via an RS485 bus; The intelligent control module uses the GD32E50x series MCU, which has data receiving, logic operation and command sending functions; after receiving the real-time parameters transmitted by the multi-dimensional dynamic sensing module, it compares them with the preset model; calculates the amount of grease required for the current bearing 14 and sends control commands to the dynamic grease injection execution module; at the same time, it uploads the real-time parameters and calculation results to the data storage device, thereby enabling the big data storage and analysis module. The preset model is established based on the working condition and grease injection volume benchmark; Based on experimental data, a model was established for the working conditions according to the lubricant viscosity coefficient, different temperatures, and corresponding grease injection amounts. In the model, factors such as vibration, pressure, and rotational speed can be taken into consideration. Dynamic grease injection execution module: After receiving the instruction from the intelligent control module, the grease injection pump starts working and delivers high-pressure grease through the pipeline to the grease distribution valve 4. The intelligent control module achieves precise control of the grease injection volume by monitoring the working feedback of the plunger sensor inside the grease distribution valve.

[0046] After receiving instructions from the intelligent control module, the grease pump 1 controls the operation of the grease pump 1 and controls the valve 2 to switch, delivering high-pressure grease to the hydraulic chamber 23 of the liposuction device 5 as power to drive the liposuction device 5 to work. The waste grease inside the bearing 14 is extracted through negative pressure and delivered to the waste grease collection device 10. The collected grease is detected by the grease status detection sensor 7.

[0047] The analytics module includes local edge computing nodes and cloud servers with data connectivity; Local edge computing nodes use industrial-grade solid-state drives for caching and offline analysis; Cloud server with built-in bearing condition prediction model; This predictive model is generated by training historical operation and maintenance data and uses the Long Short-Term Memory (LSTM) algorithm. The input parameters are the temperature change trend, vibration spectrum characteristics, and grease viscosity change rate in the most recent time period. The output results are the health score of bearing 14 and the failure type prediction in the future set time period. Historical operation and maintenance data includes samples of normal operating conditions, insufficient lubrication, deteriorated grease, and / or bearing wear. Vibration spectrum characteristics include peak value and / or root mean square value; Health score definition: 0-100 points, the lower the score, the higher the risk of failure; Fault type predictions include insufficient lubrication, grease aging, and rolling element wear; The early warning interaction module includes a local audible and visual alarm and a remote operation and maintenance platform; Local audible and visual alarm, installed in the wind turbine control cabinet; The alarm threshold can be set: when 40 < health score ≤ 60, a yellow warning is triggered; when the health score ≤ 40, a red warning is triggered. The remote operation and maintenance platform supports access from both web and mobile devices. It is used to display bearing operating parameters, grease injection records and health curves in real time. When a red warning is issued, it automatically sends a text message to the operation and maintenance personnel, which includes the fault prediction type and / or suggested handling solutions. During the analysis module processing, an intelligent lubrication system automatically adjusts the lubrication amount strategy.

[0048] Among them, such as Figure 1-3 Dual-wire working principle Figure 2 As part of the grease injection process, pump 1 is started, and pipeline a passes through control valve 2, check valve 3, and grease distribution valve 4 to reach each grease inlet 14.1 for grease injection. At the same time, hydraulic control chamber 23 is reset, and the lubricant returns to the oil storage tank through pipelines e and b. The grease suction port 14.2 enters the inner cavity 18 through inlet 17.

[0049] Of course, conventional components such as one-way valves can be added to the liposuction port 14.2 and the inlet 17.

[0050] When control valve 2 reverses, pipeline a enters hydraulic control chamber 23 through control valve 2 and pipeline e, thereby causing piston body 16 to overcome spring force and move to the left, squeezing inner cavity 18. Sealing plug 19 overcomes spring force, causing bypass hole 22 to connect with inner cavity 18, allowing lubricant in inner cavity 18 to be discharged into pipeline d, passing through grease status detection sensor 7, and entering waste grease collection device 10.

[0051] against Figure 5 In this process, control valve 2 is shut off, and pipeline a passes through pipeline e and each grease inlet 14.1. Piston body 16 overcomes spring force and moves to the left, squeezing inner cavity 18. Sealing plug 19 overcomes spring force, so that bypass hole 22 communicates with inner cavity 18, so that lubricant in inner cavity 18 is discharged into pipeline d, passes through grease status detection sensor 7, and enters waste grease collection device 10.

[0052] After the control valve 2 is turned on, the hydraulic control chamber 23 is reset, in which the lubricant enters the inner cavity 18 from the grease suction port 14.2 through the inlet 17.

[0053] Example 3, as a practical application, such as Figure 1-5 The intelligent lubrication system based on equipment health status automatically adjusts its strategy and system, which mainly consists of a grease pump 1, a control valve 2, a grease distribution valve 4, a grease suction device 5, a pressure sensor 6, a grease status detection sensor 7, a vibration sensor 8, a temperature sensor 9, a waste grease collection device 10, a backend system 11, a fixed terminal 12, a mobile terminal 13, and a bearing 14. The system can be divided into a dynamic grease injection execution module, a dynamic grease removal execution module, a multi-dimensional dynamic sensing module, an intelligent control module, a data storage and analysis module, and an early warning interaction module. These modules work collaboratively, as detailed below. Control valve 2 is a two-position four-solenoid valve.

[0054] Multi-dimensional sensing module: used for real-time acquisition of bearing operating parameters, including a temperature sensor (using a PT100 platinum resistance sensor, installed at the contact point between the inner and outer rings of the bearing, measuring range -50℃~200℃, accuracy ±0.5℃), a vibration sensor (using a piezoelectric accelerometer, installed vertically and horizontally in the bearing housing, measuring frequency range 0.1Hz~10kHz, sensitivity 100mV / g), and a grease condition detection sensor (integrating metal impurity detection and moisture detection sensors, inserted into the inlet of the grease extractor or inside the grease extractor discharge block); each sensor communicates with the intelligent control module via an RS485 bus, with a data transmission rate ≥9600bps, ensuring real-time parameter accuracy.

[0055] Intelligent control module: As the core of the system, it has the functions of data reception, logical operation and command sending. After receiving the real-time parameters transmitted by the sensor module, it compares them with the preset "working condition-grease injection amount" benchmark model, calculates the current required grease injection amount, and sends control commands to the dynamic grease injection execution module. At the same time, it uploads the real-time parameters and calculation results to the big data storage and analysis module.

[0056] The dynamic grease injection execution module includes a quantitative grease pump 1, a grease distribution valve 4 (progressive, single-line, or double-line types are all available, with an injection accuracy of ±0.02mL / time and an injection pressure range of 0~35MPa), a distribution valve plunger monitoring sensor, and grease tubing (using low-temperature resistant high-pressure hoses, with an operating temperature of -40℃~80℃). After receiving instructions from the intelligent control module, the grease pump starts working and delivers high-pressure grease through the tubing to the grease distribution valve 4. The intelligent control module achieves precise control of the grease injection volume by monitoring the feedback from the plunger sensor inside the grease distribution valve.

[0057] Dynamic grease removal execution module: includes a quantitative waste grease collection device 10 and grease pipeline; after receiving the command from the intelligent control module, the grease pump controls the operation of the grease pump, the two-position four-way solenoid valve 2 switches, and the high-pressure grease is delivered to the liposuction device as power to drive the liposuction device to work. The waste grease inside the bearing is extracted by negative pressure and delivered to the waste grease collection device 10. All collected grease is detected by the grease status detection sensor 7.

[0058] The big data storage and analysis module includes local edge computing nodes (using industrial-grade solid-state drives with a storage capacity of ≥1TB, supporting data caching and offline analysis) and cloud servers. The cloud server has a built-in bearing condition prediction model. This model is generated by training on historical operation and maintenance data (including samples of normal operation, insufficient lubrication, grease deterioration, bearing wear, etc.). It adopts the LSTM (Long Short-Term Memory) algorithm. The input parameters are the temperature change trend, vibration spectrum characteristics (such as peak value and root mean square value) and grease viscosity change rate of the past 24 hours. The output results are the bearing health score (0~100 points, the lower the score, the higher the failure risk) and failure type prediction (such as insufficient lubrication, grease aging, rolling element wear) for the next 72 hours.

[0059] Early warning interaction module: includes a local audible and visual alarm (installed in the wind turbine control cabinet, alarm threshold can be set, triggers a yellow warning when the health score is ≤60 points, and triggers a red warning when it is ≤40 points) and a remote operation and maintenance platform (supports web and mobile access, displays bearing operating parameters, grease injection records and health curves in real time, and automatically pushes SMS to operation and maintenance personnel when a red warning is triggered, including fault prediction type and suggested handling solution).

[0060] The intelligent lubrication system automatically adjusts the lubrication amount using the following strategy:

[0061] Parameter definition: Q: The actual amount of grease to be added, with a range of (0.3×) ~5× ); Theoretical grease replenishment amount for bearings under ideal operating conditions; S: Equipment operating vibration state index, with a value range of 1 to 100; G: Grease deterioration index during equipment operation, with a value range of 1 to 100; T: Temperature index of equipment operating status, with a value range of 1 to 100; K: Correction factor for injection volume; The vibration state index S of the equipment operation ranges from 1 to 100. A higher value indicates a better operating state. The calculation formula is as follows: S, Equipment Vibration Operating Status Index, with a weight range of 1 to 100; its core function is to quantitatively assess the vibration operating status of equipment. Vibration amplitude weight, weight range 0.5; core function, prioritizing the fundamental impact of vibration intensity; The vibration amplitude score (static intensity assessment) has a weight range of 1 to 100; its core function is to assess whether the current vibration exceeds the safety threshold. Vibration frequency weight, weight range 0.3; core function: distinguishing fault types; Vibration frequency score (fault characteristic assessment), weight range 1-100; core function: identifying the presence of fault characteristic frequencies; The oscillation trend weight, with a weight range of 0.2; its core function is to capture potential deterioration risks. Vibration trend score (dynamic change assessment), weight range 1-100; core function, assessing whether the vibration continues to deteriorate. Example 4

[0062] I. Calculation method for individual item scores ( , , ) 1. Analysis of vibration amplitude (Static Strength Assessment) Vibration amplitude is the basis for determining whether equipment is "overloaded / unbalanced"; the formula is as follows: Parameter definition: Current vibration amplitude of the equipment (unit: μm / s); Warning threshold (upper limit of acceptable range); Danger threshold (lower limit of unacceptable range); 2. Vibration frequency score (Fault characteristic assessment); The fault characteristic frequencies are identified through spectrum analysis, using the following formula:

[0063] Parameter definition: : The number of detected fault feature frequencies (e.g., bearing + gear faults, (k=2)); Severity weight of type i fault (bearing fault (K=30), gear fault (K=25), loose foundation (K=20)); Vibration power at the characteristic frequency of the i-th type of fault; Root mean square of vibration power across the entire frequency band.

[0064] 3. Vibration trend score (Dynamic Change Assessment) Based on linear trend analysis of historical data, the formula is as follows:

[0065] Parameter definition: : Slope of linear trend of vibration amplitude (unit: mm / s / day, based on data of the past 30 days); Maximum allowable deterioration slope (recommended): =0.2mm / s / day); II. The grease index G for equipment vibration operation ranges from 1 to 100. A higher value indicates a better operating condition. The calculation formula is as follows: ; ; ; Parameter definition: Metal content score in oils and fats, 0-100; Water content score in oils and fats, 0–100; N: The percentage of metals in the oil, in ppm; M: Water content in oil, unit: ppm; III. The equipment operating temperature index T ranges from 1 to 100. A higher value indicates a better operating condition. The calculation formula is as follows: ; Parameter definition: The formula for calculating the difference between the actual bearing temperature and the reference temperature.

[0066] Bearing reference temperature, here is =40℃.

[0067] The present invention has been described in detail for the purpose of making the disclosure clearer, and the prior art will not be listed in detail.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. It is obvious to those skilled in the art that multiple technical solutions of the present invention can be combined. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. All technical contents not described in detail in the present invention are well-known technologies.

Claims

1. An automatic adjustment strategy for an intelligent lubrication system, characterized in that: For wind turbine bearing lubrication, the strategy and steps are as follows: S1, Calculation method for itemized scores; S2, obtain the equipment operating vibration state index S; S3, obtain the grease index G for equipment vibration operation; S4, obtain the equipment operating temperature index T; S5, obtain the actual amount of grease to be replenished, Q.

2. The automatic adjustment strategy of the intelligent lubrication system according to claim 1, characterized in that: In step S1, S1.1, vibration amplitude value is divided Static strength assessment was conducted. Vibration amplitude is the basis for determining whether equipment is overloaded or unbalanced; The formula is as follows: ； Parameter definition, Current vibration amplitude of the equipment; Warning threshold; Danger threshold; S1.2, Vibration frequency score To conduct fault characteristic assessment; Identify fault characteristic frequencies through spectrum analysis. The formula is as follows: ； Parameter definition, : The number of detected fault feature frequencies; : Severity weight of the i-th type of fault; Vibration power at the characteristic frequency of the i-th type of fault; Root mean square power of vibration across the entire frequency band; S1.3, Vibration Trend Score To conduct dynamic change assessment; Based on linear trend analysis of historical data, the formula is as follows: ； Parameter definition: : Slope of linear trend of vibration amplitude, unit: mm / s / day, based on the most recent set number of days; : Maximum permissible deterioration slope.

3. The automatic adjustment strategy of the intelligent lubrication system according to claim 1, characterized in that: In step S2, the vibration state index S of the equipment operation ranges from 1 to 100. A higher value indicates a better operating state. The calculation formula is as follows: ； in, Vibration amplitude weight; The amplitude of vibration is scored, with a value range of 0 to 100. Vibration frequency weight, with a value range of 0 to 100; Vibration frequency score, with a value range of 0 to 100; , oscillation trend weight; The vibration trend score ranges from 0 to 100.

4. The automatic adjustment strategy of the intelligent lubrication system according to claim 1, characterized in that: In step S3, the grease index G for equipment vibration operation ranges from 1 to 100. A higher value indicates a better operating condition. The calculation formula is as follows: ； in, ； ； Parameter definition: Metal content score in oils and fats, 0-100; Water content score in oils and fats, 0–100; : The percentage of metals in oils and fats, in ppm; Water content in oils and fats, in ppm.

5. The automatic adjustment strategy of the intelligent lubrication system according to claim 1, characterized in that: In step S4, the equipment operating temperature index T ranges from 1 to 100. A higher value indicates a better operating condition. The calculation formula is as follows: ； Parameter definition: The formula for calculating the difference between the actual bearing temperature and the reference temperature. ; Bearing reference temperature, here is .

6. The automatic adjustment strategy of the intelligent lubrication system according to any one of claims 1-5, characterized in that: In step S5, the steps to obtain the actual amount of grease to be replenished Q are as follows: ； ； in, Q: The actual amount of grease to be added, with a range of (0.3×) ~5× ); Theoretical grease replenishment amount for bearings under ideal operating conditions; S: Equipment operating vibration state index, based on step S2; G: Grease deterioration index during equipment operation, based on step S3; T: Temperature index of equipment operating status, based on step S4; K: Correction factor for grease injection volume.

7. An intelligent lubrication system automatic adjustment system, characterized in that: The system is used to lubricate the bearing (14); The system includes a dynamic grease injection execution module; Used to execute the strategy described in claim 1; The dynamic grease injection module includes a lubrication actuator and a lubrication power unit connected by pipelines; The lubrication power unit includes a metered grease pump (1) and an oil reservoir connected via a control valve (2) fluid circuit; The lubrication actuator is connected to the grease pump (1) via the oil inlet pipe a; The lubrication actuator is connected to the oil reservoir via pipe b; Several grease inlets (14.1) and grease suction inlets (14.2) are distributed on the bearing (14). The lubrication actuator includes a grease distribution valve (4) for lubricating the bearing (14) and a grease suction device (5) corresponding to the grease suction port (14.2). The grease extractor (5) is used to extract the lubricating oil from the bearing (14); The grease filling port (14.1) is connected to the grease distribution valve (4) via pipe c; The grease pump (1) is connected to the inlet of the grease distribution valve (4) and / or the pipeline e via the oil inlet pipeline a; The oil storage tank is connected to the inlet of the grease distribution valve (4) or the pipeline e via pipeline b.

8. The intelligent lubrication system automatic adjustment system according to claim 7, characterized in that: The control valve (2) is a directional valve or a shut-off valve; When the control valve (2) is a directional valve, a check valve (3) is provided at the outlet of the directional valve. The reversing valve is installed on oil inlet line a and line b; When the control valve (2) is a shut-off valve, the shut-off valve is installed on pipeline b; A distribution valve plunger monitoring sensor (4.1) is installed on the grease distribution valve (4). A pressure sensor (6) is installed in pipeline e; The dynamic grease injection module is connected to a dynamic grease removal module via piping. The dynamic fat removal execution module includes a waste oil collection device (10). The liposuction device (5) is connected to the waste oil collection device (10) through the pipe d; The hydraulic control chamber (23) of the liposuction device (5) is connected to the tubing e; A multi-dimensional dynamic sensing module is used to collect bearing operating parameters in real time, including a grease condition detection sensor (7) set on the pipeline d, a vibration sensor (8) set on the bearing (14) and / or a temperature sensor (9). The multi-dimensional dynamic sensing module is electrically connected to the intelligent control module; The intelligent control module includes an analysis module; The intelligent control module includes a backend (11); the backend (11) includes a processor; The processor is electrically connected to a controller for controlling the rotation of the motor; The processor is electrically connected to a data memory; The processor is electrically connected to the early warning and interaction module.

9. The intelligent lubrication system automatic adjustment system according to claim 8, characterized in that: The oil storage tank is equipped with a liquid level sensor (1.1). The back-end (11) is electrically connected to the liquid level sensor (1.1), the distribution valve plunger monitoring sensor (4.1), the control valve (2), the grease pump (1), the pressure sensor (6), the grease status detection sensor (7), the vibration sensor (8), and / or the temperature sensor (9), respectively. The back-end (11) is electrically connected to a fixed terminal (12) and / or a mobile terminal (13).

10. The intelligent lubrication system automatic adjustment system according to claim 9, characterized in that: The liposuction device (5) includes a liposuction housing (15); A hydraulic cavity (23) and an inner cavity (18) are provided in the liposuction housing (15); A spring-returning piston body (16) is movably disposed in the inner cavity (18); A pipe a is connected to the hydraulic cavity (23), thereby pushing the piston body (16) to move, thereby realizing the linkage action of liposuction and lipo expulsion; A knob plug (20) is connected to the outlet (21) of the inner cavity (18). A sealing plug (19) is connected to the knob plug (20) by a spring. Under the action of the spring force, the sealing plug (19) blocks the connection between the outlet of the knob plug (20) and the inner cavity (18). A bypass hole (22) is provided on the side wall of the liposuction housing (15), which is normally blocked and closed by the sealing plug (19). When the piston body (16) moves to the left, the sealing plug (19) moves to the left against the spring force, thereby connecting the inner cavity (18) with the bypass hole (22). Bypass hole (22) connects to pipe d; Temperature sensor (9) is installed at the contact point between the inner and outer rings of the bearing; The vibration sensor (8) is a piezoelectric accelerometer, which is installed in the vertical and horizontal directions of the bearing housing respectively; The grease state detection sensor (7) integrates the detection of metal impurities and moisture; a concentration block is set on the pipeline d; the grease state detection sensor (7) is inserted into the inlet of the liposuction device (5) or discharged into the concentration block; The multi-dimensional dynamic sensing module communicates with the intelligent control module via an RS485 bus; The intelligent control module uses the GD32E50x series MCU, which has data receiving, logic operation and instruction sending functions; after receiving the real-time parameters transmitted by the multi-dimensional dynamic sensing module, it compares them with the preset model; calculates the amount of grease required for the current bearing (14) and sends control instructions to the dynamic grease injection execution module; at the same time, it uploads the real-time parameters and calculation results to the data storage, so as to perform big data storage and analysis module. The preset model is established based on the working condition and grease injection volume benchmark; Dynamic grease injection execution module: After receiving the instruction from the intelligent control module, the grease injection pump starts working and delivers high-pressure grease to the grease distribution valve (4) through the pipeline. The intelligent control module achieves precise control of the grease injection volume by monitoring the working feedback of the plunger sensor inside the grease distribution valve. After receiving the command from the intelligent control module, the grease pump (1) controls the operation of the grease pump (1), and the control valve (2) switches to deliver high-pressure grease to the hydraulic chamber (23) of the liposuction device (5) as power to drive the liposuction device (5) to work. The waste grease inside the bearing (14) is extracted through negative pressure and delivered to the waste grease collection device (10). The collected grease is detected by the grease status detection sensor (7). The analytics module includes local edge computing nodes and cloud servers with data connectivity; Local edge computing nodes use industrial-grade solid-state drives for caching and offline analysis; Cloud server with built-in bearing condition prediction model; The prediction model is generated by training historical operation and maintenance data. It adopts the Long Short-Term Memory Network (LSTM) algorithm. The input parameters are the temperature change trend, vibration spectrum characteristics, and grease viscosity change rate in the most recent time period. The output results are the health score and fault type prediction of the bearing (14) in the future set time period. Historical operation and maintenance data includes samples of normal operating conditions, insufficient lubrication, deteriorated grease, and / or bearing wear. Vibration spectrum characteristics include peak value and / or root mean square value; Health score definition: 0-100 points, the lower the score, the higher the risk of failure; Fault type predictions include insufficient lubrication, grease aging, and rolling element wear; The early warning interaction module includes a local audible and visual alarm and a remote operation and maintenance platform; Local audible and visual alarm, installed in the wind turbine control cabinet; The alarm threshold can be set: when 40 < health score ≤ 60, a yellow warning is triggered; when the health score ≤ 40, a red warning is triggered. The remote operation and maintenance platform supports access from both web and mobile devices. It is used to display bearing operating parameters, grease injection records and health curves in real time. When a red warning is issued, it automatically sends a text message to the operation and maintenance personnel, which includes the fault prediction type and / or suggested handling solutions. During the analysis module processing, an intelligent lubrication system automatically adjusts the lubrication amount strategy.

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