Efficient oil change system for wind power yaw and variable pitch gear box

Abstract

The invention discloses an efficient oil change system for a wind power yaw and variable pitch gearbox, and relates to the technical field of intelligent operation and maintenance of wind power generation equipment, and the system comprises a data acquisition and preprocessing module which is responsible for acquiring and standardizing gearbox data; the health state evaluation module is used for dynamically adjusting index weights and calculating health scores; the oil change parameter adjusting module is used for determining oil change parameters; the oil change operation execution module executes oil change and corrects parameters; the cloud collaborative management module is used for storing data, supporting remote configuration and jointly realizing efficient oil change management of the gear box; according to the invention, the state of the gearbox is accurately controlled through data acquisition preprocessing and a dynamic health assessment mechanism, the shutdown risk is reduced, and the system stability and economic benefits are improved; meanwhile, oil change parameter self-adaptive adjustment and operation closed-loop control are adopted, an oil change strategy is intelligently matched, parameter error compensation and main and standby redundancy units are combined, it is ensured that oil change is efficient and reliable, the operation and maintenance cost is reduced, and the service life of equipment is prolonged.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation and maintenance technology for wind power generation equipment, specifically to an efficient oil change system for wind yaw and pitch gearboxes. Background Technology

[0002] As a crucial component of clean energy, the stability of wind power equipment operation directly impacts power generation efficiency and economic benefits. The yaw and pitch systems of wind turbine generators are core components. Their gearboxes endure complex loads and environmental changes during long-term operation, and issues such as oil contamination and accelerated wear can easily lead to equipment failure. Currently, gearbox maintenance primarily relies on periodic oil changes and manual inspection. However, due to the harsh environment and fluctuating operating conditions in wind farms, traditional methods struggle to accurately reflect the equipment's health status in real time, often resulting in over-maintenance or under-maintenance. This increases operating costs and may trigger unplanned outages, impacting power generation revenue.

[0003] Traditional gearbox oil change techniques have significant limitations. On the one hand, detection methods are limited, relying mainly on periodic oil quality sampling and analysis or manual vibration data checks, lacking real-time comprehensive evaluation of multiple parameters and making it difficult to detect gradual performance degradation. On the other hand, fixed oil change parameters fail to consider the impact of changes in operating conditions such as oil temperature, ambient humidity, and engine speed on oil performance, leading to inaccurate timing of oil changes. This can result in premature oil replacement leading to waste, or delayed replacement accelerating equipment wear. Furthermore, traditional oil change operations rely on manual labor, which is cumbersome and inefficient, especially in remote wind farms where maintenance resources are limited, further exacerbating maintenance difficulties and costs. These shortcomings hinder the efficient operation of wind power equipment, necessitating technological innovation to upgrade maintenance methods. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a high-efficiency oil change system for wind-powered yaw and pitch gearboxes. This system achieves accurate data acquisition and standardized processing through a data acquisition and preprocessing module, and dynamically adjusts indicator weights and calculates health scores in conjunction with a health status assessment module to accurately control the gearbox status. The system can intelligently match oil change strategies, adaptively adjust oil change parameters, and ensure efficient and reliable oil change through closed-loop operation control. This system reduces operation and maintenance costs, extends equipment life, and improves the stability and economic benefits of wind power generation systems.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a high-efficiency oil change system for wind-driven yaw and pitch gearboxes, the system comprising: Data acquisition and preprocessing module: Collects data on oil impurity content, vibration frequency, oil temperature, oil pressure, oil tank level and cumulative running time of the gearbox through sensors. After removing outliers using the 3σ criterion, the data is normalized to a uniform range through data normalization and the preprocessed data is output. Health status assessment module: Receives preprocessed data, adopts dynamic weight coefficient adjustment method, dynamically adjusts the weight of three core assessment indicators, namely oil quality, vibration and running time, based on oil temperature, ambient humidity and gearbox input speed, and then calculates the health status score by combining the real-time change rate of each indicator through the health status score calculation method, classifies the health level according to the score and outputs it. Oil change parameter adjustment module: Receives health level and health status score, combines gearbox volume and oil viscosity conditions to determine oil extraction speed, cleaning pressure and oil injection flow rate parameters, and outputs oil change parameters; Oil change operation execution module: Receives oil change parameters, sequentially performs old oil extraction, gearbox cleaning and new oil injection, collects and feeds back real-time data on residual oil and actual oil injection volume, corrects oil change parameters through parameter error compensation method, and switches to backup redundant unit when the main execution unit is abnormal. Cloud-based collaborative management module: It adopts an architecture that combines edge nodes and cloud databases, receives and classifies the collected data, evaluation results, adjustment parameters and operation data generated by the aforementioned modules, supports remote configuration of health assessment thresholds and oil change parameters, and generates gearbox operation data reports according to predefined hierarchical permissions.

[0006] Furthermore, the 3σ criterion used in the data acquisition and preprocessing module is implemented as follows: for each type of raw data collected by each sensor, the mean and standard deviation of the data are first calculated, and data that exceed the mean ± 3 times the standard deviation are judged as outliers; the outlier handling method is to replace it with the average of the first 3-5 consecutive valid samples of the data, and the replaced data is then used for subsequent normalization processing.

[0007] Furthermore, the data normalization calculation method in the data acquisition and preprocessing module is as follows: subtract the safety threshold of the corresponding index from the measured value of oil impurity content, vibration frequency or cumulative running time, and divide the result by the difference between the corresponding index danger threshold and safety threshold to obtain the normalized standard value, which ranges from 0 to 1.

[0008] Furthermore, the formula for calculating the dynamic weight coefficient adjustment in the health status assessment module is as follows: ,in, Weighting coefficients for oil quality, vibration, or operating time; The basic weights for the corresponding indicators; This is the oil temperature influence coefficient; This refers to the gearbox oil temperature. The environmental humidity influence coefficient; For ambient humidity; The influence coefficient of rotational speed; Input the rotational speed for the gearbox.

[0009] Furthermore, the formula for calculating the health status score of the health status assessment module is as follows: ,in, Score the health status; This is the weighting coefficient for the content of oil impurities; This is the weighting coefficient for the vibration frequency; This is a weighting factor for the cumulative runtime; This represents the normalized value of the oil impurity content; This is the normalized value of the vibration frequency; This is a normalized value of the cumulative runtime; This is the penalty coefficient for the rate of change; This represents the average of the real-time change rates of the three core evaluation indicators.

[0010] Furthermore, the health status assessment module classifies health levels into three categories: normal, maintenance-required, and emergency oil change. Each level corresponds to a specific oil change priority. The normal level corresponds to a health status score of not less than 0.8, and oil change can be delayed for 60-84 hours. The maintenance-required level corresponds to a health status score of 0.5-0.8, and oil change must be completed within 8-28 hours. The emergency oil change level corresponds to a health status score below 0.5, and oil change must be performed immediately.

[0011] Furthermore, the formula for calculating the pump power compensation corresponding to the parameter error compensation of the oil change operation execution module is as follows: ,in, This is the power compensation amount for the oil pump; This is the basic power compensation amount; This is the error correction factor; This represents the actual amount of oil remaining after extraction. The target is the amount of residual oil extracted.

[0012] Furthermore, the method for determining the oil injection flow rate of the oil change parameter adjustment module is as follows: using the basic oil injection flow rate as a benchmark, making an initial adjustment based on the health status score, and then making a secondary correction based on the oil viscosity conditions. When the oil viscosity is high, the oil injection flow rate is appropriately increased; when the oil viscosity is low, the oil injection flow rate is appropriately decreased. Finally, the oil injection flow rate is within a reasonable range that is suitable for the gearbox operating conditions.

[0013] Compared with existing technologies, this wind-powered yaw and pitch gearbox high-efficiency oil change system has the following advantages: I. This invention achieves precise control over the gearbox's operating status by constructing a collaborative mechanism for data acquisition and preprocessing with dynamic health assessment. It effectively eliminates abnormal data using the 3σ criterion, unifies data scales using normalization technology, and dynamically adjusts the assessment weights of core indicators such as oil quality, vibration, and operating time through real-time influence coefficients of oil temperature, humidity, and speed. This overcomes the limitations of traditional static assessment, enabling earlier detection of equipment performance changes and precise prioritization of oil change, thereby significantly reducing the risk of unexpected downtime and improving the stability and economic benefits of wind power generation systems. Second, this invention revolutionizes the traditional gearbox oil changing mode through adaptive adjustment of oil change parameters and closed-loop control of operations. The system can intelligently match the oil change strategy according to the equipment's health status and actual operating conditions, and perform secondary parameter optimization based on the oil viscosity characteristics to form a more practical operating plan. The parameter error compensation mechanism dynamically adjusts the execution parameters through real-time feedback, and, together with the intelligent switching of the main and backup redundant units, ensures the high efficiency and reliability of the oil change operation, significantly improves oil change efficiency, reduces maintenance costs, and extends the service life of the equipment. Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0015] Figure 1 A flowchart of a high-efficiency oil change system for a wind-driven yaw and pitch gearbox. Figure 2 This is a framework diagram of a wind-driven yaw and variable pitch gearbox high-efficiency oil change system. Detailed Implementation

[0016] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0017] Example 1: Implementation of Oil Change Operation for Yaw Gearbox in Onshore Wind Turbine This embodiment is applied to the yaw gearbox of a 1.5MW onshore wind farm. The gearbox has a rated input speed of 1800r / min, a volume of 80L, and a daily operating environment humidity between 55% and 65%. The cumulative operating time has reached 4500h.

[0018] In this embodiment, the overall operation process of the wind yaw gearbox oil change system is as follows: Figure 1 As shown, it sequentially covers five stages: data acquisition and preprocessing, health status assessment, oil change parameter adjustment, oil change operation execution, and cloud-based collaborative management. The data acquisition and preprocessing module uses multiple sensors deployed on the gearbox to collect real-time data on oil impurity content, vibration frequency, oil temperature, oil pressure, tank level, and cumulative running time. During the acquisition process, outliers in the oil impurity content data that exceed the normal range are removed using the 3σ criterion, and the outlier is replaced with the average of the previous four consecutive valid samples of that indicator. Subsequently, according to the data normalization calculation method, the measured values ​​of oil impurity content, vibration frequency, and cumulative running time are subtracted from their respective safety thresholds, and the results are divided by the difference between the corresponding indicator's danger threshold and safety threshold to obtain a normalized standard value ranging from 0 to 1, which is then output as preprocessed data.

[0019] After receiving the preprocessed data, the health status assessment module initiates the dynamic weight coefficient adjustment method. The calculation formula for the dynamic weight coefficient adjustment is as follows: ,in, Weighting coefficients for oil quality, vibration, or operating time; The basic weights for the corresponding indicators; This is the oil temperature influence coefficient; This refers to the gearbox oil temperature. The environmental humidity influence coefficient; For ambient humidity; The influence coefficient of rotational speed; Input speed to the gearbox. Based on the actual operating conditions of 42℃ gearbox oil temperature, 62% ambient humidity, and an input speed of 1780 r / min, the basic weights of the three core evaluation indicators—oil quality, vibration, and operating time—are dynamically adjusted. Then, using a health status score calculation method, combining the normalized standard values ​​of the three core evaluation indicators and the mean of the real-time change rate of each indicator, a health status score of 0.65 is calculated. The formula for calculating the health status score is: ,in, Score the health status; This is the weighting coefficient for the content of oil impurities; This is the weighting coefficient for the vibration frequency; This is a weighting factor for the cumulative runtime; This represents the normalized value of the oil impurity content; This is the normalized value of the vibration frequency; This is a normalized value of the cumulative runtime; This is the penalty coefficient for the rate of change; This is the average of the real-time change rates of the three core assessment indicators. According to the health level classification standard, this score is in the range of 0.5-0.8, indicating a maintenance requirement. The oil change priority is set to medium, and the oil change must be completed within 15 hours.

[0020] After receiving the required maintenance level and a health status score of 0.65, the oil change parameter adjustment module, using a gearbox volume of 80L as a base reference and considering the current oil viscosity test results, first makes preliminary adjustments to the pumping speed, cleaning pressure, and injection flow rate. Considering that the oil viscosity is at a medium level, no significant adjustment to the injection flow rate is required. The final oil change parameters are determined to be: pumping speed 8L / min, cleaning pressure 0.3MPa, and injection flow rate 10L / min, and the above parameters are output.

[0021] After receiving the oil change parameters, the oil change execution module initiates the oil change process. First, it performs the old oil extraction operation, using an oil pump to extract the old oil from the gearbox, collecting real-time data on the residual oil volume. After old oil extraction, the gearbox's internal oil passages and component surfaces are circulated and cleaned according to the set cleaning pressure to remove residual oil and impurities. After cleaning, new oil is injected, with the actual injected oil volume data collected simultaneously. The collected residual oil volume and actual injected oil volume data are fed back to the control unit. The oil change parameters are corrected using the oil pump power compensation method within the parameter error compensation framework. The oil pump power is adjusted to reduce the residual oil volume. The oil pump power compensation calculation formula is as follows: ,in, This is the power compensation amount for the oil pump; This is the basic power compensation amount; This is the error correction factor; This represents the actual amount of oil remaining after extraction. The target residual oil level was determined. Throughout the oil change operation, the main execution unit operated stably without any abnormalities, eliminating the need to switch to the backup redundant unit.

[0022] The cloud-based collaborative management module receives preprocessed data from the data acquisition and preprocessing module, assessment results from the health status assessment module, adjustment parameters from the oil change parameter adjustment module, and operational data from the oil change operation execution module via edge nodes, and stores all data in a cloud database. Maintenance personnel can log in to the cloud management platform via remote terminals and view various operational data of the yaw gearbox according to preset permissions, enabling remote configuration of health assessment thresholds and oil change parameters without on-site visits. After the oil change operation is completed, the system automatically generates an operational data report for the gearbox according to a preset format, clearly recording the changes in various indicators before and after the oil change and operational details, providing data support for subsequent maintenance.

[0023] In summary, this embodiment addresses the operational characteristics and requirements of the yaw gearbox in a 1.5MW onshore wind farm. The data acquisition and preprocessing module uses the 3σ criterion to eliminate abnormal data and normalizes it to obtain standardized data, providing a reliable foundation for subsequent assessments. The health status assessment module combines the actual oil temperature, ambient humidity, and input speed of the gearbox, using a dynamic weighting coefficient adjustment method to optimize the weights of core indicators. A health status score is then calculated to determine the maintenance level, clarifying the oil change deadline and priority. The oil change parameter adjustment module determines reasonable oil change parameters based on the gearbox volume and oil viscosity conditions. The oil change execution module ensures oil change quality through parameter error compensation. The cloud-based collaborative management module enables data storage, remote configuration, and report generation. The entire process is adapted to the onshore wind power environment, effectively completing the maintenance-level oil change operation and providing data support for the subsequent operation and maintenance of the gearbox.

[0024] Example 2: Implementation of Oil Change Operation for Offshore Wind Turbine Pitch Gearbox This embodiment is applied to a pitch gearbox in a 3MW offshore wind farm. The gearbox has a rated input speed of 1600r / min and a volume of 120L. The humidity of the offshore operating environment is maintained between 75% and 85% for a long time. Affected by marine salt spray, the viscosity of the gearbox oil is prone to change, and the speed has recently fluctuated. The cumulative operating time is 3800h.

[0025] In this embodiment, the overall operation process of the wind yaw gearbox oil change system is as follows: Figure 2 As shown, it sequentially covers five stages: data acquisition and preprocessing, health status assessment, oil change parameter adjustment, oil change operation execution, and cloud-based collaborative management. The data acquisition and preprocessing module continuously collects data on gearbox oil impurity content, vibration frequency, oil temperature, oil pressure, oil tank level, and cumulative running time using an anti-salt spray sensor. During the data processing stage, sudden anomalies in the vibration frequency data are identified and removed using the 3σ criterion, and replaced with the average of the previous three consecutive valid samples of that indicator. Subsequently, the oil impurity content, vibration frequency, and cumulative running time data are standardized according to a data normalization calculation method. The measured values ​​of each indicator are converted into normalized standard values ​​between 0 and 1 through calculation, ensuring a unified data format, and the preprocessed data is output.

[0026] After receiving the preprocessed data, the health status assessment module activates a dynamic weighting coefficient adjustment method. Considering the current operating conditions—gearbox oil temperature of 38℃, ambient humidity of 82%, and input speed of 1550 r / min—and taking into account the impact of the high humidity environment at sea on gearbox operation, the basic weights of the three core assessment indicators—oil quality, vibration, and operating time—are dynamically adjusted, with a focus on increasing the weight of oil quality. Subsequently, a health status score is calculated using a method that combines the normalized standard values ​​of the three core assessment indicators with the average of the real-time change rates of each indicator, resulting in a health status score of 0.42. According to the health level classification rules, a score below 0.5 indicates an emergency oil change level, with the highest priority, requiring immediate oil change operations.

[0027] After receiving the emergency oil change level and a health status score of 0.42, the oil change parameter adjustment module, referencing the 120L gearbox volume parameter and combining it with the current oil viscosity test results, first makes preliminary adjustments to the pump speed, cleaning pressure, and injection flow rate based on the health status score. Considering the high oil viscosity, to ensure injection efficiency and effectiveness, the injection flow rate is secondarily corrected, appropriately increasing the injection flow rate. The final determined parameters are: pump speed 10L / min, cleaning pressure 0.4MPa, and injection flow rate 12L / min. The adjusted oil change parameters are then output.

[0028] Upon receiving the oil change parameters, the oil change execution module immediately initiates the emergency oil change process. The first step involves extracting the old oil. The oil pump operates at a set speed, monitoring and collecting data on the residual oil volume in real time. After extraction, the gearbox is cleaned under high pressure to remove stubborn oil and impurities. Following cleaning, new oil injection is initiated, simultaneously collecting data on the actual injection volume. The actual residual oil volume and injection volume are fed back to the control unit, where parameter error compensation is used to correct the oil change parameters. The oil pump power compensation is adjusted to optimize the extraction effect. If the main execution unit's oil pressure sensor malfunctions during the process, the system quickly detects the anomaly and automatically switches to the backup redundant unit, ensuring uninterrupted oil change operations until the entire process is completed.

[0029] The cloud-based collaborative management module adopts an architecture combining edge nodes and a cloud database. It receives and categorizes all collected data, assessment results, adjustment parameters, and operational data related to the current oil change operation in real time. Maintenance personnel remotely configure health assessment thresholds through the cloud management platform to ensure that subsequent assessment standards for this type of gearbox are compatible with the high humidity environment at sea. After the oil change operation is completed, the system generates an operational data report for the pitch gearbox according to preset permission levels. This report details changes in oil quality, vibration, and other indicators before and after the oil change, operational parameters, and equipment operating status, providing a basis for subsequent operation and maintenance and oil change strategy optimization for offshore wind farm gearboxes.

[0030] In summary, this embodiment addresses the characteristics of a 3MW offshore wind farm's pitch gearbox, including high humidity, fluctuating oil viscosity, and susceptibility to salt spray. The data acquisition and preprocessing module collects data using salt spray sensors and ensures data validity through 3σ criteria and data normalization. The health status assessment module dynamically adjusts indicator weights based on offshore operating conditions, calculates a health status score, and determines the emergency oil change level. The oil change parameter adjustment module adjusts the injection flow rate based on gearbox volume and high-viscosity oil. The oil change operation execution module uses a backup redundant unit to handle main unit anomalies, ensuring continuous operation. The cloud-based collaborative management module enables data management and remote configuration. This process adapts to the complex environment of offshore wind power, efficiently completes emergency oil change operations, and provides a basis for offshore wind turbine gearbox operation and maintenance and strategy optimization.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A high-efficiency oil change system for a wind-powered yaw and pitch gearbox, characterized in that, The system includes: Data acquisition and preprocessing module: Collects data on oil impurity content, vibration frequency, oil temperature, oil pressure, oil tank level and cumulative running time of the gearbox through sensors. After removing outliers using the 3σ criterion, the data is normalized to a uniform range through data normalization and the preprocessed data is output. Health status assessment module: Receives preprocessed data, adopts dynamic weight coefficient adjustment method, dynamically adjusts the weight of three core assessment indicators, namely oil quality, vibration and running time, based on oil temperature, ambient humidity and gearbox input speed, and then calculates the health status score by combining the real-time change rate of each indicator through the health status score calculation method, classifies the health level according to the score and outputs it. Oil change parameter adjustment module: Receives health level and health status score, combines gearbox volume and oil viscosity conditions to determine oil extraction speed, cleaning pressure and oil injection flow rate parameters, and outputs oil change parameters; Oil change operation execution module: Receives oil change parameters, sequentially performs old oil extraction, gearbox cleaning and new oil injection, collects and feeds back real-time data on residual oil and actual oil injection volume, corrects oil change parameters through parameter error compensation method, and switches to backup redundant unit when the main execution unit is abnormal. Cloud-based collaborative management module: It adopts an architecture that combines edge nodes and cloud databases, receives and classifies the collected data, evaluation results, adjustment parameters and operation data generated by the aforementioned modules, supports remote configuration of health assessment thresholds and oil change parameters, and generates gearbox operation data reports according to predefined hierarchical permissions.

2. The high-efficiency oil change system for a wind-powered yaw and pitch gearbox according to claim 1, characterized in that, The 3σ criterion used in the data acquisition and preprocessing module is implemented as follows: For each type of raw data collected by each sensor, the mean and standard deviation of the data are first calculated, and data that exceed the mean ± 3 times the standard deviation are judged as outliers. The outlier handling method is to replace it with the average of the first 3-5 consecutive valid samples of the data, and the replaced data participates in the subsequent normalization process.

3. The high-efficiency oil change system for a wind-powered yaw and pitch gearbox according to claim 1, characterized in that, The data normalization calculation method in the data acquisition and preprocessing module is as follows: subtract the safety threshold of the corresponding index from the measured value of oil impurity content, vibration frequency or cumulative running time, and divide the result by the difference between the corresponding index danger threshold and safety threshold to obtain the normalized standard value. The standard value ranges from 0 to 1.

4. The high-efficiency oil change system for a wind-powered yaw and pitch gearbox according to claim 1, characterized in that, The formula for adjusting the dynamic weighting coefficient in the health status assessment module is as follows: ,in, Weighting coefficients for oil quality, vibration, or operating time; The basic weights for the corresponding indicators; This is the oil temperature influence coefficient; This refers to the gearbox oil temperature. The environmental humidity influence coefficient; For ambient humidity; The rotational speed influence coefficient; Input the rotational speed for the gearbox.

5. The high-efficiency oil change system for a wind-powered yaw and pitch gearbox according to claim 1, characterized in that, The formula for calculating the health status score in the health status assessment module is as follows: ,in, Score the health status; This is the weighting coefficient for the content of oil impurities; This is the weighting coefficient for the vibration frequency; This is a weighting factor for the cumulative runtime; This represents the normalized value of the oil impurity content; This is the normalized value of the vibration frequency; This is a normalized value of the cumulative runtime; This is the penalty coefficient for the rate of change; This represents the average of the real-time change rates of the three core evaluation indicators.

6. The high-efficiency oil change system for a wind-powered yaw and pitch gearbox according to claim 1, characterized in that, The health status assessment module classifies health levels into three categories: normal, maintenance-required, and emergency oil change. Each level corresponds to a specific oil change priority. The normal level corresponds to a health status score of not less than 0.8, and oil change can be delayed for 60-84 hours. The maintenance-required level corresponds to a health status score of 0.5-0.8, and oil change must be completed within 8-28 hours. The emergency oil change level corresponds to a health status score below 0.5, and oil change must be performed immediately.

7. The high-efficiency oil change system for a wind-powered yaw and pitch gearbox according to claim 1, characterized in that, The formula for calculating the pump power compensation corresponding to the parameter error compensation of the oil change operation execution module is as follows: ,in, This is the power compensation amount for the oil pump; This is the basic power compensation amount; This is the error correction factor; This represents the actual amount of oil remaining after extraction. The target is the amount of residual oil extracted.

8. The high-efficiency oil change system for a wind-powered yaw and pitch gearbox according to claim 1, characterized in that, The method for determining the oil injection flow rate of the oil change parameter adjustment module is as follows: using the basic oil injection flow rate as a benchmark, making an initial adjustment based on the health status score, and then making a second correction based on the oil viscosity conditions. When the oil viscosity is high, the oil injection flow rate is appropriately increased; when the oil viscosity is low, the oil injection flow rate is appropriately decreased. Finally, the oil injection flow rate is within a reasonable range that is suitable for the gearbox operating conditions.

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