Wind turbine generator bearing lubricating system based on grease state online diagnosis

Abstract

The invention belongs to the technical field of maintenance and monitoring of wind power generation equipment, and particularly relates to a wind turbine generator bearing lubricating system based on grease state online diagnosis. The grease online monitoring module is used for collecting operation data of a bearing; the data analysis and diagnosis module is used for receiving the operation data, performing diagnosis and generating a lubrication instruction; the central control and communication module is used for receiving data from the monitoring module and operating an algorithm of the data analysis and diagnosis module; and the lubrication execution module is used for injecting grease into the bearing and discharging waste grease. Predictive maintenance is achieved, operation and maintenance strategies are optimized, equipment reliability is improved, the failure rate is reduced, the service life of a bearing is prolonged, operation and maintenance cost is reduced, lubricating grease consumption is reduced, and environmental pollution is reduced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of wind power equipment maintenance and monitoring, and particularly relates to a wind turbine generator bearing lubrication system based on grease state online diagnosis. BACKGROUND

[0002] As the core equipment for converting wind energy into electrical energy, the reliability and stability of wind turbine generators are directly related to the power generation efficiency and economic benefits. In wind turbine generators, key rotating components such as main shaft bearings, yaw bearings, and variable pitch bearings bear huge and alternating loads, and the lubrication condition directly determines the service life of the bearings and the safe operation of the entire machine.

[0003] The existing online monitoring technology still has some obvious deficiencies when applied to wind turbine bearing grease management, mainly in the comprehensiveness of the monitoring object and the monitoring parameters, and the degree of combination with lubrication control.

[0004] The monitoring parameters of the existing online monitoring system often focus on a certain aspect, making it difficult to comprehensively and comprehensively evaluate the lubrication state of the bearing. However, single monitoring data often has limitations. Patent CN115060491A proposes a wind turbine bearing operation health degree evaluation method based on multi-source data, which integrates lubricating grease monitoring data (water content, temperature), vibration data, pre-tightening force data, temperature data and other information, and establishes a comprehensive evaluation model to judge the health state of the bearing. This fully demonstrates that single parameter monitoring cannot reveal the whole picture of the problem, and only through the fusion analysis of multi-source data can the lubrication state be more accurately diagnosed and potential failure risks be identified.

[0005] At present, the functions of most online monitoring systems still remain at the level of "monitoring" and "alarm", and the output results are mainly used to guide manual maintenance decisions, and they cannot form an effective closed-loop control with the automatic lubrication system. Patent CN119435326A proposes a wind turbine bearing lubrication system, which realizes the replacement of old grease while supplementing new grease through the linkage of inductive devices, control devices, grease filling devices and grease discharge devices, thereby completing the replacement of the grease inside the bearing. This represents an important direction of combining monitoring and control, but how to realize more intelligent and more refined lubrication control based on real-time and dynamic monitoring data is still a difficulty that needs to be overcome in current technology. SUMMARY

[0006] The present application aims to overcome the above-mentioned defects of existing wind turbine bearing lubrication technology and provide a new lubrication system integrating online monitoring, intelligent diagnosis and full-automatic control.

[0007] To solve the above problems, the application provides a wind turbine bearing lubrication system based on grease state online diagnosis. The system is a highly integrated electromechanical system, mainly composed of four parts of grease state online monitoring module, data analysis and diagnosis module, automatic lubrication execution module and central control and communication module. The modules work together to form a complete closed-loop control link from "perception-diagnosis-decision-execution-feedback".

[0008] A wind turbine bearing lubrication system based on grease state online diagnosis, comprising an online grease monitoring module for collecting bearing operation data;

[0009] Data analysis and diagnosis module: for receiving operation data, making diagnosis, and generating lubrication instructions;

[0010] Central control and communication module: for receiving data from the monitoring module and running the algorithm of the data analysis and diagnosis module;

[0011] Lubrication execution module: for injecting grease into the bearing and discharging waste grease.

[0012] Further, the online grease monitoring module includes grease property sensors, vibration sensors, pressure sensors and temperature sensors installed on the bearing seat.

[0013] Further, the grease property sensor is integrated with a MEMS resonant acceleration sensor, a capacitive sensor, a magnetic sensor and a moisture sensor.

[0014] Further, the data analysis and diagnosis module includes a multi-source data fusion algorithm module for integrating operation data;

[0015] Grease state evaluation model: establish a bearing grease state evaluation model, receive real-time fused multi-source data features, and output an index reflecting the current bearing lubrication state;

[0016] Lubrication demand decision unit: based on the output of the grease state evaluation model, the lubrication demand decision unit is responsible for formulating specific lubrication strategies;

[0017] Lubrication execution module: responsible for executing the lubrication instructions issued by the decision unit.

[0018] Further, the lubrication execution module includes a lubrication pump, a progressive distributor and a grease filling and discharging device.

[0019] Further, the central control and communication module comprises a main controller, a remote monitoring platform and a communication interface; the main controller receives data from the grease online monitoring module and runs the algorithm of the data analysis and diagnosis module; the main controller exchanges data and transmits instructions with the local HMI, the remote monitoring platform and the wind turbine main control system through the communication interface.

[0020] Further, the temperature sensor is closely attached to the bearing outer ring or installed in the bearing seat.

[0021] Further, the pressure sensor is installed in the pipeline of the lubrication execution module.

[0022] Further, the grease online monitoring module further comprises a preprocessing unit for receiving analog or digital signals from various sensors and performing preliminary processing.

[0023] Further, the preliminary processing includes filtering, amplification and analog-to-digital conversion, decoding, verification, time synchronization marking and data cleaning.

[0024] By realizing online real-time diagnosis of the grease state of the bearing of the wind turbine, through integration of a miniaturized and highly reliable sensor array, the system can directly obtain a grease sample at or near the lubricated bearing part and perform analysis, thereby avoiding the hysteresis and inaccuracy caused by offline sampling. On the basis of realizing accurate diagnosis of the grease state, the second core purpose of the present application is to establish a full-automatic intelligent lubrication control strategy based on the diagnosis results. The reliability and accuracy of bearing lubrication are improved, and the service life of the bearing is prolonged.

[0025] The advantages of the present application are:

[0026] 1. Improve equipment reliability and reduce failure rate:

[0027] By realizing accurate and demand-based intelligent lubrication, premature bearing wear, pitting, spalling and other failures caused by insufficient or ineffective lubrication can be effectively avoided, thereby significantly improving the operation reliability of the wind turbine and reducing the unplanned downtime.

[0028] 2. Prolong the service life of the bearing and reduce the operation and maintenance cost:

[0029] Accurate lubrication management can maximize the performance of the grease and reduce the friction and wear of the bearing, thereby effectively prolonging the service life of the bearing. This will directly reduce the spare parts cost of the wind turbine and the high labor and hoisting cost required for replacing the bearing.

[0030] 3. Save lubricating grease consumption and reduce environmental pollution:

[0031] By avoiding excessive lubrication, the waste of lubricating grease can be significantly reduced. At the same time, the system is equipped with an automatic waste grease recycling function, which can effectively prevent the pollution of waste lubricating grease to the environment, and conforms to the concept of green development.

[0032] 4. Realize predictive maintenance, optimize operation and maintenance strategy:

[0033] The real-time state monitoring and intelligent diagnosis function provided by the application provides key data support for realizing predictive maintenance of the wind turbine generator. The operation and maintenance personnel can make maintenance plans in advance according to the early warning information of the system, and optimize the operation and maintenance resource allocation, so as to upgrade from the traditional "passive maintenance" and "periodic maintenance" mode to a more efficient and more economical "condition-based maintenance" mode.

[0034] 5. Improve power generation efficiency and increase economic benefits:

[0035] Good lubrication state of the bearing can reduce the friction loss of the transmission chain, so as to improve the power generation efficiency of the wind turbine generator to a certain extent. At the same time, reducing the downtime also means increasing the effective power generation time, directly improving the economic benefits of the wind farm. BRIEF DESCRIPTION OF DRAWINGS

[0036] Fig. 1 The overall structure principle diagram of the wind turbine generator bearing lubrication system based on the grease state online diagnosis provided by the application is provided;

[0037] Fig. 2 The principle diagram of the online monitoring module provided by the application is provided;

[0038] Fig. 3 The working flowchart of the wind turbine generator bearing lubrication system based on the grease state online diagnosis provided by the application is provided. DETAILED DESCRIPTION The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0039] As Figs. 1-3 shown, the embodiment provides a wind turbine generator bearing lubrication system based on grease state online diagnosis, and the working method thereof is a dynamic, self-adaptive closed-loop control process, mainly including four core steps of multi-source data acquisition and fusion, grease state online diagnosis, intelligent lubrication decision and control, and lubrication effect feedback and closed-loop optimization.

[0040] The online grease monitoring module includes a bearing operation data acquisition module, which is responsible for real-time acquisition of various types of raw data related to the lubrication state of the bearing. The core of this module is a multi-parameter sensor unit and a data acquisition and preprocessing unit.

[0041] The data analysis and diagnosis module is used to receive operation data, perform diagnosis, and generate lubrication instructions.

[0042] The central control and communication module is used to receive data from the monitoring module and run the algorithm of the data analysis and diagnosis module.

[0043] The lubrication execution module is used to inject grease into the bearing and discharge waste grease.

[0044] In another embodiment of the present application, in order to comprehensively evaluate the lubrication state, the system integrates multiple types of sensors to obtain multi-source heterogeneous data. The online grease monitoring module includes grease property sensors, vibration sensors, pressure sensors, and temperature sensors installed on the bearing seat, which can real-time collect key data such as temperature, vibration, pressure, and consistency, water content, and contamination of the bearing. The data acquisition and preprocessing unit converts these raw signals into digital signals and sends them to the central controller through an industrial bus (such as CAN);

[0045] Grease property sensors: This sensor unit can directly analyze the lubricating grease in the bearing with multiple parameters. Sensors using piezoelectric resonant MEMS technology can measure the viscosity (or consistency), density, dielectric constant, and temperature of the lubricating grease. Sensors using the principle of capacitance method can monitor the relative dielectric constant of the lubricating grease, thereby inferring its dilution or contamination level. Sensors using electromagnetic induction principle can detect the number and size distribution of ferromagnetic and non-ferromagnetic metal particles in the lubricating grease, directly reflecting the wear state of the bearing. In addition, a special moisture sensor can be integrated to monitor the water content in the lubricating grease and prevent emulsification risk.

[0046] Bearing operation state sensors: including vibration sensors and temperature sensors installed on the bearing seat. Vibration sensors are used to capture the vibration signals of the bearing, which can be analyzed in time and frequency domains to diagnose whether the bearing has mechanical faults such as damage or misalignment. Temperature sensors are used to monitor the working temperature of the bearing, and abnormal temperature rise is often a direct manifestation of poor lubrication or excessive load.

[0047] Environment and working condition sensors: including environmental temperature sensors, humidity sensors, and operating condition data such as wind speed, wind direction, main shaft speed, and generator power obtained from the fan main control system. These external parameters are crucial for accurately evaluating the degradation rate of the lubricating grease and the actual load of the bearing, and are the basis for multi-source data fusion diagnosis.

[0048] After the system is started, it first enters the multi-source data collection phase. Various types of sensors installed on the bearing and its surroundings continuously collect data at a preset frequency (e.g., once every second or minute). These data include:

[0049] 1) Grease itself state data: grease consistency, moisture content, metal particle concentration, dielectric constant, and temperature provided by grease property sensors.

[0050] 2) Bearing operating state data: bearing vibration acceleration, speed, displacement signals, and outer ring or seat temperature provided by vibration sensors and temperature sensors.

[0051] 3) Environmental and working condition data: environmental temperature, humidity, wind speed, wind direction, main shaft speed, generator power, etc. provided by environmental sensors and fan main control system.

[0052] The collected raw data is sent to the data collection and preprocessing unit. Here, the system synchronizes, cleans, and extracts features from the data. For example, the vibration signal is subjected to fast Fourier transform (FFT) or wavelet transform to extract the energy or amplitude of a specific frequency band as a feature; temperature and pressure data are smoothed to remove transient peak interference. The processed multi-source heterogeneous data is fused into a unified feature vector that can fully reflect the bearing lubrication state, providing high-quality input for the next step of diagnosis.

[0053] In another embodiment of the present application, the software functions of the data analysis and diagnosis module are implemented in the main controller, which is responsible for processing and analyzing the data collected by the monitoring module, diagnosing through fusion algorithms and evaluation models, and finally generating lubrication instructions by the decision unit;

[0054] The multi-source data fusion algorithm module: To overcome the problem of one-sidedness of single data source information, the system uses advanced data fusion algorithms to effectively integrate data from different sensors and different types. For example, weighted average, Kalman filtering, etc. can be used to fuse redundant data, improving the accuracy and reliability of the data. For heterogeneous data such as vibration signals, temperature values, and grease physicochemical indicators, feature-level fusion or decision-level fusion methods can be used. For example, the root mean square (RMS) and peak factor can be extracted from the vibration signal, together with the viscosity and particle concentration of the grease, to form a multi-dimensional feature vector as input for the subsequent evaluation model. This fusion strategy can more comprehensively depict the lubrication and health status of the bearing.

[0055] Oil state evaluation model: This model is based on machine learning or deep learning algorithms, such as support vector machines (SVM), neural networks (NN), or fuzzy logic systems. The training process of the model requires a large amount of historical data, including characteristic samples of various monitoring parameters of bearings in different health states, such as normal, poor lubrication, early wear, and severe damage. Through training, the model can learn the complex nonlinear relationship between various monitoring parameters and bearing lubrication state. In actual operation, the model receives real-time fused multi-source data features and outputs a quantitative "lubrication health index" or "lubrication demand level", which intuitively reflects the current lubrication state of the bearing;

[0056] Lubrication demand decision unit: Based on the output of the oil state evaluation model, the lubrication demand decision unit is responsible for formulating specific lubrication strategies. This unit has built-in algorithms. For example, different LHI thresholds can be set. When LHI is below a certain threshold, different levels of lubrication actions are triggered. If LHI only decreases slightly, the system may only increase the next grease injection amount; if LHI continues to decline and reaches the medium risk level, the system may shorten the lubrication period; if LHI drops sharply and is accompanied by a sharp rise in vibration and abrasive particle concentration, the system may judge that there is a severe lubrication failure or bearing damage, and at the same time, an emergency lubrication or grease replacement program is started, and the operation and maintenance personnel are notified for manual intervention;

[0057] For example:

[0058] 1) If the diagnosis result is "normal", the system may maintain the original lubrication plan unchanged.

[0059] 2) If the diagnosis result is "slightly insufficient", the system may decide to appropriately increase the grease injection amount by 10% in the next scheduled lubrication cycle.

[0060] 3) If the diagnosis result is "severe deficiency", the system will immediately start an additional lubrication cycle and may shorten the subsequent lubrication interval.

[0061] 4) If the diagnosis result is "need to replace" (usually accompanied by a sharp rise in abrasive particle concentration and moisture), the system will start the "grease replacement" program: first, the grease discharge device is started to discharge the old grease in the bearing; then, the intelligent lubrication pump is started to inject a predetermined amount of new grease into the bearing; finally, all actions are stopped, and the maintenance operation is recorded.

[0062] Once the decision is made, the control command is immediately issued to the automatic lubrication execution module, which is accurately executed by the intelligent lubrication pump, distributor, etc.

[0063] Lubrication execution module: receives instructions from the main controller, lubrication pump starts, grease is pumped out, after accurate distribution through progressive distributor, it is injected into the bearing; at the same time, the grease filling and discharging device discharges waste grease according to the instruction, realizes the renewal of lubricating grease;

[0064] The lubrication pump is precisely controlled by the central controller, which is usually an electric plunger pump. The start-stop, running time and speed of its motor can be precisely controlled. The controller can accurately achieve the required grease injection amount by controlling the running time of the pump according to the calculation of the decision unit. The pump body also integrates the necessary safety valve and check valve to prevent excessive pressure and grease backflow;

[0065] The progressive distributor is the key component to ensure that the lubricating grease can be quantitatively and orderly delivered to each lubrication point. Its working principle is to use the pressure of lubricating grease to push the internal piston to move in sequence, so as to discharge the grease from each outlet in turn. This design ensures that each lubrication point can get accurate lubricant dosage, avoiding uneven distribution caused by different lengths or resistances of pipelines.

[0066] In order to realize more advanced lubrication management, the system can also integrate an automatic waste grease discharging device. The device is usually composed of a grease discharging pump or a grease discharging port controlled by a solenoid valve, connected to the bearing grease discharging channel. When the system needs to update the grease, it will first start the grease discharging device to discharge the old grease in the bearing, and then start the grease injection pump to supplement the new grease. This mode of discharging first and then injecting can more effectively clean the inside of the bearing and maintain the health of the lubrication environment, which is an important means to improve the lubrication effect.

[0067] In another embodiment of the present application, the central control and communication module is the "nerve center" of the system, responsible for coordinating the work of each module and interacting with the outside;

[0068] The main controller is the core hardware of the whole system, usually using industrial-grade programmable logic controller (PLC) or high-performance microcontroller (MCU). It runs embedded software, responsible for all core tasks such as data acquisition, algorithm operation, logic control and fault diagnosis. Its stability and reliability are directly related to the success or failure of the whole system.

[0069] To realize remote monitoring and data uploading, the system is equipped with multiple communication interfaces. For example, data interaction with the main control system of the wind turbine can be realized through CAN bus or Modbus RTU / TCP protocol to obtain operating condition data and upload the status and alarm information of the lubrication system to the main control system. At the same time, the system can also send detailed monitoring data and diagnostic reports to the remote cloud monitoring platform through the 4G / 5G wireless communication module for analysis and decision-making by operation and maintenance experts. CAN bus is widely used in industrial control field due to its high reliability, real-time performance and multi-master station capability, which is very suitable for the needs of the system.

[0070] To facilitate on-site operation and debugging of the operation and maintenance personnel, the system can be equipped with a local human-machine interface (HMI), usually a touch screen. Through the HMI, real-time monitoring data can be viewed, lubrication parameters can be modified, lubrication actions can be manually controlled, and historical alarm records can be viewed. At the same time, the remote monitoring platform provides more powerful data analysis and visualization functions, which can generate trend charts, health reports, and centrally manage all lubrication systems in the entire wind farm to realize truly intelligent operation and maintenance.

[0071] In another embodiment of the present application, the temperature sensor uses PT100 or thermocouple elements, which are closely attached to the bearing outer ring or installed in the bearing seat to real-time perceive the working temperature of the bearing.

[0072] In another embodiment of the present application, the pressure sensor is installed in the pipeline of the lubrication execution module to monitor the oil supply pressure for judging whether the system is blocked or leaked.

[0073] In another embodiment of the present application, the oil online monitoring module further includes a preprocessing unit responsible for receiving analog or digital signals from various sensors and performing preliminary processing. It is usually composed of high-performance data acquisition cards (DAQ) or embedded microprocessors (MCU). Its main functions include: filtering, amplifying and analog-to-digital conversion (A / D) of analog signals; decoding and checking of digital signals; time synchronization marking of the collected raw data to ensure consistency of multi-source data in subsequent fusion analysis; and preliminary data cleaning such as removing obvious outliers and noise to provide high-quality input data for subsequent data analysis and diagnosis modules.

[0074] The working process is as follows:

[0075] (1) System initialization: after the system is powered on, self-checking and initialization are performed first, including checking the hardware state of each module, calibrating the sensors, establishing the communication link and loading the preset parameters.

[0076] (2) Multi-source data acquisition: After the system enters the normal working cycle, the first step is to start multi-source data acquisition by the oil condition online monitoring module. Temperature, vibration, pressure, and oil property sensors collect data synchronously at a preset frequency (e.g., every 30 seconds).

[0077] (3) Data transmission and preprocessing: The collected raw data is sent to the central controller through the CAN bus. After receiving the data, the central controller performs necessary preprocessing, such as filtering, denoising, feature extraction, etc.

[0078] (4) Multi-source data fusion: The preprocessed multi-source heterogeneous data is sent to the data fusion algorithm module. This module fuses data from different sensors to generate a comprehensive feature vector that can more comprehensively and robustly describe the overall state of the lubrication system.

[0079] Oil condition online diagnosis: The fused comprehensive feature vector is input into the oil condition evaluation model. Based on machine learning algorithms, this model evaluates the health status of the lubricating grease and outputs a clear health level (e.g., "excellent, good, medium, poor") or health index.

[0080] (5) Lubrication demand decision: Based on the diagnosis results, the lubrication demand decision unit makes decisions. If the lubrication status is good, it returns to "continuous monitoring"; if the lubrication status is poor, it proceeds to the next step.

[0081] (6) Intelligent lubrication decision and control: When lubrication is needed, the decision unit intelligently calculates the best lubrication scheme, including lubrication amount, lubrication method (supplement or update), etc., based on the preset lubrication strategy and current diagnosis results, and generates corresponding control instructions.

[0082] (7) Execute lubrication operation: After receiving the control instructions, the automatic lubrication execution module accurately performs lubrication operations, such as starting the intelligent lubrication pump, controlling the progressive distributor, starting the waste grease discharge device, etc.

[0083] (8) Lubrication effect feedback and closed-loop optimization: After the lubrication operation is completed, the system continues to monitor various parameters to evaluate the lubrication effect. The evaluation results are fed back to the diagnosis module for model and strategy optimization, forming a continuous iteration and self-optimization closed-loop control cycle.

Claims

1. A wind turbine bearing lubrication system based on online grease condition diagnosis, characterized in that: Includes an online grease monitoring module: used to collect bearing operating data; Data analysis and diagnostic module: used to receive operational data, perform diagnostics, and generate lubrication instructions; Central control and communication module: Used to receive data from the monitoring module and run the algorithms of the data analysis and diagnostic module; Lubrication actuator module: used to inject grease into the bearing and remove waste grease.

2. The wind turbine bearing lubrication system based on online grease condition diagnosis according to claim 1, characterized in that: The online grease monitoring module includes a grease characteristic sensor, a vibration sensor, a pressure sensor, and a temperature sensor installed on the bearing housing.

3. The wind turbine bearing lubrication system based on online grease condition diagnosis according to claim 2, characterized in that: The oil and grease characteristic sensor integrates a MEMS resonant accelerometer, a capacitive sensor, a magnetic sensor, and a moisture sensor.

4. The wind turbine bearing lubrication system based on online grease condition diagnosis according to claim 1, characterized in that: The data analysis and diagnostic module includes Multi-source data fusion algorithm module: used to integrate runtime data; Grease condition assessment model: Establish a bearing grease condition assessment model, receive multi-source data features after real-time fusion, and output an index reflecting the current bearing lubrication condition; Lubrication Demand Decision Unit: Based on the output of the grease condition assessment model, the lubrication demand decision unit is responsible for formulating specific lubrication strategies. Lubrication execution module: responsible for executing lubrication commands issued by the decision-making unit.

5. The wind turbine bearing lubrication system based on online grease condition diagnosis according to claim 4, characterized in that: The lubrication execution module includes: a lubrication pump, a progressive distributor, and a grease filling and discharging device.

6. The wind turbine bearing lubrication system based on online grease condition diagnosis according to claim 1, characterized in that: The central control and communication module includes The system includes a main controller, a remote monitoring platform, and a communication interface. The main controller receives data from the online oil monitoring module and runs algorithms from the data analysis and diagnostic module. The main controller exchanges data and sends / receives commands with the local HMI, the remote monitoring platform, and the wind turbine main control system through the communication interface.

7. The wind turbine bearing lubrication system based on online grease condition diagnosis according to claim 2, characterized in that: The temperature sensor is fitted tightly against the outer ring of the bearing or installed inside the bearing housing.

8. The wind turbine bearing lubrication system based on online grease condition diagnosis according to claim 2, characterized in that: The pressure sensor is installed in the piping of the lubrication actuator module.

9. The wind turbine bearing lubrication system based on online grease condition diagnosis according to claim 1, characterized in that: The online oil monitoring module also includes a preprocessing unit, which receives analog or digital signals from various sensors and performs preliminary processing.

10. The wind turbine bearing lubrication system based on online grease condition diagnosis according to claim 9, characterized in that: The preliminary processing includes filtering, amplification and analog-to-digital conversion, decoding, verification, time synchronization marking, and data cleaning.

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