Wireless monitoring device for main shaft bearing of wind turbine generator

Abstract

The invention discloses a wireless monitoring device for a main shaft bearing of a wind turbine generator. The system comprises a data monitoring module, a data receiving module and an upper computer control module. The data monitoring module directly obtains triaxial angular velocities wx, wy and wz, triaxial accelerations ax, ay and az of a bearing roller, temperature data T, voltages V1, V2 and V3 changed by pressure and a relative rotation angle alpha t of the roller, and calculates pressure F1, F2 and F3 borne by the roller and a position angle theta t of the roller relative to the bearing according to the obtained data. The data receiving module communicates with the upper computer control module to receive data of the data monitoring module and issue a command, the upper computer control module processes the received data and calculates the service state of the wind turbine generator bearing according to an analytic hierarchy process degradation evaluation model, and the device can give an alarm in advance before a major accident occurs to the bearing, so that the service life of the bearing is prolonged. Therefore, faults are effectively prevented, and the operation safety and reliability of the wind turbine generator are improved.

Description

Technical Field

[0001] The invention relates to the technical field of bearing monitoring, and in particular to a wireless monitoring device for a main shaft bearing of a wind turbine generator set. Background Art

[0002] As a new type of clean, efficient and renewable green energy, wind power generation can not only reduce the consumption of mineral resources, but also significantly reduce environmental pollution. However, wind turbines are usually installed in remote plains, mountains, intertidal zones and offshore areas. Since wind turbines are huge in size and difficult to climb, there are a large number of wind turbines in wind farms, and they are affected by harsh environments such as wind and sand, acid rain, exposure to the sun and sea salt corrosion for a long time. In addition, the machines are in a state of high-intensity and continuous operation all year round, and their rotating systems are prone to failure. Therefore, it is necessary to detect the status of the wind turbine bearings. However, since wind turbines are usually installed in remote plains, mountains, intertidal zones and offshore areas, each manual inspection requires a lot of labor. In order to solve the above problems, many wind power industry manufacturers have begun to study status monitoring systems.

[0003] Although the development direction of the existing intelligent monitoring system is clear, there are many problems in practical application. For example, as a rotating part, all sensors are placed on the outer ring of the bearing or the outer edge of the bearing seat, and the bearing status cannot be directly monitored. In addition, remote data transmission requires a lot of electricity and cannot be powered continuously for a long time. Therefore, it is necessary to design a high-precision, low-power and stable wireless monitoring device for large wind turbine bearings to solve the above problems. Summary of the invention

[0004] The present invention designs a wireless monitoring device for a main shaft bearing of a wind turbine generator set, aiming to solve the problem in the prior art that the internal data of the wind turbine generator set bearing during its service process cannot be monitored and how to evaluate the service status of the wind turbine generator set bearing.

[0005] The technical solution of the present invention is as follows.

[0006] The present invention provides a wireless monitoring device for a main shaft bearing of a wind turbine generator set, which comprises a data monitoring module, a data receiving module and a host computer control module;

[0007] The data monitoring module is cylindrical and is placed at the center opening of the roller. It carries a six-axis motion monitoring unit, a temperature sensor and three "π"-type pressure sensors. The data monitoring module directly obtains the three-axis angular velocity w of the bearing roller based on the sensor. x 、w y 、w z , triaxial acceleration a x 、a y 、a z, temperature data T, voltage V changed by pressure 1 、V 2 、V 3 , the relative rotation angle α of the roller t , calculate the pressure F on the roller based on the acquired data 1 、F 2 、F 3 and the roller position angle θ relative to the bearing t ;

[0008] Data receiving module, receiving data from the data monitoring module and issuing commands;

[0009] The host computer control module is used to process the received data, calculate the service status of the wind turbine bearing according to the hierarchical analysis degradation evaluation model, and control the working status of the data monitoring module.

[0010] In the present invention, the data monitoring module includes a monitoring terminal upper shell, a monitoring main board and a monitoring terminal lower shell which are arranged in sequence from top to bottom. A battery slot is arranged on the monitoring terminal upper shell, and three "π"-shaped pressure sensor placement slots are equidistantly placed on the central axis of the data monitoring module. Three "π"-shaped pressure sensors are placed in the slots. The three "π"-shaped pressure sensors are located on the monitoring main board. The three "π"-shaped pressure sensors are electrically connected to the monitoring main board by flying wires.

[0011] In the present invention, the monitoring main board is an integrated circuit board, and the monitoring main board includes a communication circuit, a pressure signal acquisition circuit and a power supply circuit.

[0012] In the present invention, the fixing part of the "π"-type pressure sensor has four holes, the two middle ones are positioning pins, which use cylindrical pins; the two on both sides use threaded cylindrical pins, which are horizontally placed on the central axis of the data monitoring module, and the tails are equidistantly distributed in the center of the roller. The tail of the "π"-type pressure sensor is exposed and protrudes from the outside of the monitoring roller.

[0013] In the present invention, the data monitoring module, the data receiving module and the host computer control module are connected via a wireless communication module. Preferably, a Zigbee communication chip is used.

[0014] In the present invention, in the data monitoring module, the pressure F at both ends and in the middle of the roller is 1 、F 2 、F 3 , that is, the calculation formula of F is shown in formulas (1)-(3):

[0015]

[0016] Where:

[0017] f is the pressure on the inner surface of the roller detected by the “π” type pressure sensor, and V is the voltage that changes when the roller is subjected to pressure;

[0018] is the pressure action angle, r is the radius of the roller; R is the inner ring radius of the bearing, α t is the relative rotation angle of the roller at time t, provided by the six-axis motion monitoring unit, w z is the angular velocity of the roller around the Z axis, provided by the six-axis motion monitoring unit;

[0019] μ is the roller inner surface strain in millimeters and is calculated using formula (1) and Hooke's law as f=0.0042*μ.

[0020] In the present invention, the data receiving module includes a "mouth"-shaped body and a USB interface. The upper part of the "mouth"-shaped body is an RF transceiver unit and a signal enhancement antenna, and the lower bottom plate is a bus switching unit. The USB interface is directly connected to the bus switching unit. The bottom plate of the "mouth"-shaped body is placed with a host debugging switch and an intelligent monitoring system status display light.

[0021] In the present invention, the host computer control module remotely controls the working mode of the data monitoring module, and the working modes are respectively "sleep mode", "data storage mode" and "data sending mode".

[0022] In the present invention, in the host computer control module, the method for calculating the service status of the wind turbine bearing according to the hierarchical analysis degradation evaluation model is as follows:

[0023] Step 1: Establish a hierarchical structure, which consists of a target layer, a criterion layer, and an indicator layer; among which:

[0024] Target layer: bearing status score; Criterion layer: vibration level, wear degree; Index layer: acceleration peak a p , peak angular velocity w p , average pressure G, temperature T, eccentric load pressure ΔG;

[0025] Step 2: Establish the following criterion layer judgment matrix and calculate the criterion layer weight vector W 1 =[w 11 ,w 12 ];

[0026]

[0027] Where: p 1 It is the importance range of vibration level relative to the degree of wear, and its value is between 1 and 9; 11 ,w 12 are the weights of vibration level and wear degree respectively;

[0028] Step 3: Establish the following indicator layer judgment matrix and calculate the indicator layer weight vector W 2 =[w 21, w 22 ] and W 3 =[w 31, w 32 ,w 33 ]:(Introduction to English letters)

[0029]

[0030] in:

[0031] w 21 ,w 22 They are the peak acceleration a p , peak angular velocity w p The weight it occupies;

[0032] w 31 ,w 32 ,w 33 are the weights of average pressure G, temperature T, and eccentric load pressure ΔG;

[0033] p 2 is the peak acceleration a p Relative to the peak angular velocity w p The importance of the value range is 1 to 9;

[0034] p 3 is the importance of the average pressure G relative to the temperature T, ranging from 1 to 9;

[0035] p 4 is the importance of the average pressure G relative to the eccentric pressure ΔG, with a value range of 1 to 9;

[0036] p 5 is the importance of temperature T relative to the eccentric load pressure ΔG, with a value range of 1 to 9;

[0037] Step 4: Data calculation and standardization

[0038] According to the three-axis angular velocity obtained by the data monitoring module, w x 、w y 、w z , triaxial acceleration a x 、a y 、a z , temperature data T, three pressure data F 1 , F 2 , F 3 , further calculate the index layer parameters: velocity peak a p , angular velocity peak w p, average pressure G and eccentric load pressure ΔG;

[0039] a p =max{a x ,a y ,a z},

[0040] w p =max{w x ,w y ,w z},

[0041]

[0042] ΔG=max{F 1 ,F 2 ,F}-min{F 1 ,F 2 ,F};

[0043] Then standardize the indicator layer parameters through the data standardization formula:

[0044]

[0045] Among them, x is the index layer parameter;

[0046] Step 5. Calculate n based on the standardized index layer parameters i Current status rating of bearing set:

[0047] n i =w 11 ×([a p ,w p ]×W 2 T )+w 12 ×([G,T,ΔG]=W 3 T )

[0048] Step 6: Calculate the current state of the bearing

[0049] Each time the data receiving module receives a set of data, it will calculate an n i , calculate the bearing state N at this time according to the degradation formula:

[0050]

[0051] The health status of the bearing is determined by the size of N; when N is between 0.0 and 0.35, the bearing service status is "healthy"; when N is between 0.35 and 0.60, the bearing service status is "good"; when N is between 0.60 and 0.85, the bearing service status is "abnormal"; when N is between 0.85 and 1.00, the bearing service status is "serious".

[0052] Compared with the prior art, the present invention has the following advantages:

[0053] (1) Wind turbine bearing monitoring system of the present invention: Different from the traditional monitoring system, the data monitoring module of the present invention is cylindrical, small in size, and embedded inside the roller. Under the premise of ensuring the strength of the roller, the design realizes that the terminal rolls with the roller and directly monitors various status data inside the bearing.

[0054] (2) High-precision stress detection: An independently designed “π”-type pressure sensor is used inside the detection terminal. The “π”-type pressure sensor directly outputs voltage. These components are evenly distributed inside the detection terminal and can monitor the tiny stresses and changes inside the bearing roller. The detectable range is 2N to 40N. The fitting formula (1) is obtained through experiments. Based on formula (1), the output voltage signal of the strain gauge can be converted into pressure data. Finally, the pressure on the outer surface of the bearing roller can be obtained according to formulas (1)-(3).

[0055] (3) Low power consumption: The present invention adopts a low-power microcontroller and a Zigbee communication chip. Through the sleep mode of the microcontroller and the efficient data transmission protocol and low-power standby mode of the Zigbee chip, the host can control the monitoring terminal to enter the working or sleep mode, thereby significantly extending the service life of the monitoring terminal.

[0056] (4) The present invention uses the acceleration peak value a based on the hierarchical analysis degradation evaluation model p , peak angular velocity w p , average pressure G, temperature T, and eccentric load pressure ΔG data are used to evaluate the bearing condition, which is divided into four states: "excellent", "good", "abnormal", and "serious". This method has high evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 : This is the overall assembly structure diagram of the monitoring terminal.

[0058] Figure 2 : This is the structural diagram of the data receiving module.

[0059] Figure 3 :A physical picture of the monitoring motherboard.

[0060] Figure 4: Overall left view of the data monitoring module (monitoring terminal).

[0061] Figure 5 : Top view of the “π” type pressure sensor.

[0062] Figure 6 : It is the working principle diagram.

[0063] Figure 7 : Three-dimensional diagram of roller.

[0064] Reference numerals:

[0065] 1-monitoring terminal upper shell; 2-monitoring terminal lower shell; 3-monitoring mainboard; 4-battery slot; 5-"π" type pressure sensor; 6-analog-to-digital conversion unit; 7-signal transceiver unit; 8-six-axis motion monitoring unit; 9-data storage unit placement slot; 10-"π" type pressure sensor placement slot; 11-data storage unit; 12-main control unit; 13-monitoring mainboard power supply terminal; 14-bus adapter unit; 15-USB interface; 16-signal enhancement antenna; 17-RF transceiver unit; 18-host debugging switch; 19-intelligent monitoring system status display light; 20-"mouth" type main body. DETAILED DESCRIPTION

[0066] The features and exemplary embodiments of various aspects of the present invention will be described in detail below. In the detailed description below, many specific details are proposed to provide a comprehensive understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present invention by illustrating examples of the present invention. In the accompanying drawings and the following description, at least part of the known and prior art is not shown to avoid unnecessary ambiguity of the present invention; and, for clarity, the size and shape of the part may be exaggerated. The features or characteristics described below may be combined in one or more embodiments in any suitable manner. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or quantity.

[0067] Example 1

[0068] See also Figure 1 to Figure 7The present invention provides a wireless monitoring device for the main shaft bearing of a wind turbine generator set, comprising a data monitoring module, a data receiving module and a host computer control module. The data monitoring module is placed inside a cone-shaped wind turbine bearing roller with a through hole, and can monitor the speed of the bearing position, the pressure and temperature signals, and transmit the data wirelessly with the data receiving module through a signal transceiver unit, and send the monitoring data to the data receiving module; the data receiving module is connected to a PC, and the working state of the data monitoring module is controlled by the host computer control module, and the service state of the wind turbine bearing is evaluated by a hierarchical analysis degradation model.

[0069] The data monitoring module includes three main parts: the upper shell 1 of the monitoring terminal, the monitoring main board 3 and the lower shell 2 of the monitoring terminal. The left edge of the upper shell 1 of the monitoring terminal is provided with a battery slot 4. Three "π" type pressure sensor placement slots 10 are equidistantly arranged in the central position of the data monitoring module. Three "π" type pressure sensors 5 are placed in the slots. The tail ends of the three "π" type pressure sensors 5-a, 5-b, and 5-c are exposed and protrude from the shell, which are used to detect the deformation and pressure of the inner surface of the roller. There are 4 pins in the "π" type pressure sensor placement slots 10-a, 10-b, and 10-c. The two in the middle are positioning pins. Using cylindrical pins can accurately locate the position. The two on both sides are threaded cylindrical pins, which are used to fix the "π" type pressure sensor 5. The tail of the "π" type pressure sensor is exposed.

[0070] The monitoring mainboard 3 is fixed in the upper shell 1 and the lower shell 2 of the monitoring terminal. The back of the monitoring mainboard 3 is close to the lower shell 2 of the monitoring terminal. There is a certain space between the front of the monitoring mainboard 3 and the upper shell 1 of the monitoring terminal for placing flying wires and various electronic units. The monitoring mainboard 3 is an integrated circuit board, including a communication circuit, a pressure signal acquisition circuit, and a power supply circuit, which can ensure the communication between various units and the transmission of signals; the front of the monitoring mainboard 3 is embedded with a signal transceiver unit 7, a main control unit 12, a six-axis motion monitoring unit 8, a data storage unit 11, an analog-to-digital conversion unit 6 (6-a, 6-b, 6-c) and a monitoring mainboard power supply terminal 13; a temperature sensor is integrated on the main control unit 12, and a six-axis motion monitoring unit 8

[0071] The signal transceiver unit 7 is placed at the leftmost end of the main card and slightly protrudes. The main control unit 12 is a control unit used to control the entire data monitoring module and cooperate with the signal transceiver unit 7 to communicate with the data receiving module; the six-axis motion monitoring unit 8 can detect the three-axis angular velocity w of the roller x 、w y 、w z And the three-axis acceleration data a x 、a y 、a zThe data storage unit 11 is a memory chip for storing various data. The signal transceiver unit 7 includes an antenna and a radio frequency (RF) chip. It receives commands sent by the data receiving module and transmits them to the main control unit 12, so that the upper control module can communicate with the data monitoring module. The monitoring mainboard power supply terminal 13 is directly connected to the battery slot 4 to power the entire monitoring mainboard 3. The signal transceiver unit 7 is placed on the far right of the monitoring mainboard 3 and 1 / 3 protrudes from the periphery of the board. The protruding part can enhance the signal strength to ensure the quality of signal transmission.

[0072] The data receiving module includes a "mouth"-shaped body 20 and a USB interface 15. The upper part of the "mouth"-shaped body 20 is an RF transceiver unit 17 and a signal enhancement antenna 16, which are used to communicate wirelessly with the data monitoring module and issue commands. The lower part of the body is a bus adapter unit 14, and the USB interface 15 is directly connected to the bus adapter unit 14, which is used to connect to the PC end to connect to the upper computer control module. The bottom plate of the "mouth"-shaped body 20 is placed with a host debugging switch 18 and an intelligent monitoring system status display light 19, which are used to display the three working states of the data monitoring module.

[0073] The data receiving module is mainly used to cooperate with the host computer control module to issue three working mode commands and receive data sent by the data monitoring module.

[0074] The functions of the upper computer control module include: remote control data monitoring module, including "sleep mode", "data storage mode" and "data sending mode"; hierarchical analysis degradation evaluation model; data processing model and formula.

[0075] The sleep mode is a low-power mode of the entire hardware, which turns off all sensor clocks and only retains the low-frequency working mode of the main control unit 12 and the signal transceiver unit 7; the "data storage mode" is that only the main control unit 12 and various sensors work and only store data without sending data; the "sending data mode" is the data transmission state, sending the data storage unit 11 to the upper computer control module.

[0076] In the present invention, the specific working process of the wireless monitoring device for the main shaft bearing of a wind turbine generator set is as follows:

[0077] When using the wind turbine main shaft bearing device, according to Figure 7 As shown, the data monitoring module is fixed at the center of the perforated bearing roller and is in an initialized "sleep state". The data receiving module is connected to the PC through a USB interface and communicates with the upper control module through serial port communication. The data receiving module uses a three-color LED light to determine the state of the data monitoring module.

[0078] The data monitoring module is initially in "sleep mode", in which the sensors of the monitoring terminal and various non-main control units are all turned off, consuming very little energy. The entire data monitoring module is in standby mode, waiting for the data receiving module to issue other commands.

[0079] When the "monitoring mode" command is sent, the data receiving module sends the command in the Zigbee command format. The signal transceiver unit first receives the command and wakes up the main control unit. The main control unit starts working and collects data by controlling three "π" type pressure sensors and the six-axis motion monitoring unit. The collected data is then transmitted to the monitoring terminal data storage unit (which can be an SD card) for storage.

[0080] When the "transmission data mode" command is sent, the data receiving module sends a Zigbee format command. The signal transceiver unit first receives the command, wakes up the main control chip and the battery unit, and the main control unit starts working. The main control unit communicates with the storage unit, extracts data from the storage unit, sends it to the main control unit register, and deletes the sent content at the same time. The data stored in the storage unit is transmitted to the upper computer control module through the signal transceiver unit.

[0081] When the host computer control module receives the three-axis angular velocity w x 、w y 、w z and the three-axis acceleration a x 、a y 、a z , temperature data T, pressure data F 1 、F 2 、F 3 Finally, it combines the hierarchical analysis degradation model to judge the four states of the bearing: "excellent", "good", "abnormal" and "serious".

[0082] When the data monitoring module has completed transmitting the monitoring data for a period of time, the host computer control module sends a "sleep command" through the data receiving module. After receiving the command, the data monitoring module enters the sleep standby state again and waits for the next command.

[0083] In the present invention, in the data monitoring module, the three "π"-type pressure sensors output voltage data. Through the "π"-type pressure sensors set at specific positions, based on the conversion of voltage and pressure, the pressure data at both ends and in the middle of the roller can be measured respectively, and the stress state of the bearing roller can be better analyzed. The conversion formula of voltage and pressure is obtained by the following steps:

[0084] S1: Prepare experimental equipment: "π" type pressure sensor, material GCr15SiMn, and professional pressure testing machine.

[0085] S2: Use a professional pressure testing machine to apply preset stress to the material, and record the preset pressure each time to obtain the corresponding voltage signal data. The preset pressure starts from 2N and increases by 1N each time.

[0086] S3: Repeat step S2 three times, increasing from 2N to 40N each time.

[0087] S4: The data are fitted using the least squares method by taking the average of three data runs, where the jump part is replaced by a linear function.

[0088] S5: Perform residual analysis on the fitting results, evaluate the fitting accuracy of the formula, and analyze the results.

[0089] The final pressure calculation formula is shown in formula (1), specifically:

[0090]

[0091] Furthermore, the present invention is based on the force on the inner surface of the bearing roller, and obtains the calculation formula for the force on the outer surface of the bearing roller through the following steps:

[0092] S1: Prepare experimental equipment, TM-C002 hole roller, material G20Cr2Ni4A, professional line pressure testing machine, deformation measuring instrument;

[0093] S2: Use a professional pressure testing machine to apply preset stress to the material at different angles, and record the preset pressure and angle each time to obtain the corresponding deformation data matrix. The preset pressure starts from 100N and increases by 50N each time. The preset angle starts from 0° (roller pressure action angle) and increases by 5° each time.

[0094] S3: Repeat step S2 three times, increasing the pressure to 1000N and the angle to 180°;

[0095] S4: The data were fitted using polynomial regression by taking the average of the three data runs;

[0096] S5: Use R-squared to evaluate the fitting effect and analyze whether the fitting results are reasonable.

[0097] The final calculation formula of the bearing roller force is shown in formula (3):

[0098]

[0099] in:

[0100] μ is the strain on the inner surface of the roller in millimeters. It is calculated by formula (1) and Hooke's law to obtain f = 0.0042*μ.

[0101] is the pressure action angle, calculated by formula (2).

[0102]

[0103] in:

[0104] The radius of the roller; R is the inner ring radius of the bearing;

[0105] α t The relative rotation angle of the roller at time t is provided by the six-axis motion monitoring unit;

[0106] w z The angular velocity of the roller around the Z axis is provided by a six-axis motion monitoring unit;

[0107] In the present invention, further, the upper control module judges the service status of the bearing based on the acquired data and the hierarchical analysis degradation evaluation model, and the specific steps are as follows:

[0108] Step 1: Establish a hierarchical structure, which consists of a target layer, a criterion layer, and an indicator layer; among which:

[0109] Target layer: bearing status score; Criterion layer: vibration level, wear degree; Index layer: acceleration peak a p , peak angular velocity w p , average pressure G, temperature T, eccentric load pressure ΔG;

[0110] Step 2: Establish the following criterion layer judgment matrix and calculate the criterion layer weight vector W 1 =[w 11 ,w 12 ];

[0111]

[0112] Where: p 1 It is the importance range of vibration level relative to the degree of wear, and its value is between 1 and 9; 11 ,w 12 are the weights of vibration level and wear degree respectively;

[0113] Step 3: Establish the following indicator layer judgment matrix and calculate the indicator layer weight vector W 2 =[w 21, w 22 ] and W 3 =[w 31, w 32 ,w 33 ]:(Introduction to English letters)

[0114]

[0115] in:

[0116] w 21 ,w 22 They are the peak acceleration a p , peak angular velocity w p The weight it occupies;

[0117] w 31 ,w 32 ,w 33 are the weights of average pressure G, temperature T, and eccentric load pressure ΔG;

[0118] p 2 is the peak acceleration a p Relative to the peak angular velocity w p The importance of the value range is 1 to 9;

[0119] p 3 is the importance of the average pressure G relative to the temperature T, ranging from 1 to 9;

[0120] p 4 is the importance of the average pressure G relative to the eccentric pressure ΔG, with a value range of 1 to 9;

[0121] p 5 is the importance of temperature T relative to the eccentric load pressure ΔG, with a value range of 1 to 9;

[0122] Step 4: Data calculation and standardization

[0123] According to the three-axis angular velocity obtained by the data monitoring module, w x 、w y 、w z , triaxial acceleration a x 、a y 、a z , temperature data T, three pressure data F 1 , F 2 , F 3 , further calculate the index layer parameters: velocity peak a p , angular velocity peak w p , average pressure G and eccentric load pressure ΔG;

[0124] a p =max{a x , a y , a z},

[0125] w p =max{w x , w y , w z},

[0126]

[0127] ΔG=max{F 1 , F 2 , F}-min{F 1 , F 2 , F};

[0128] Then standardize the indicator layer parameters through the data standardization formula:

[0129]

[0130] Among them, x is the index layer parameter;

[0131] Step 5. Calculate n based on the standardized index layer parameters i Current status rating of bearing set:

[0132] n i =w 11 ×([a p , w p ]×W 2 T )+w 12 ×([G, T, ΔG]×W 3 T )

[0133] Step 6: Calculate the current state of the bearing

[0134] Each time the data receiving module receives a set of data, it will calculate an n i , calculate the state N of the bearing at this time according to the degradation formula;

[0135]

[0136] The health status of the bearing is determined by the size of N, as shown in Table 1:

[0137] Table 1

[0138] N 0.0～0.35 0.35～0.60 0.60～0.85 0.85～1.00 Health status healthy good abnormal serious

[0139] The model was initially validated by testing two different data sets. One experimental data set was measured at normal speed and pressure, and the bearing status was healthy. The other data set was simulated by adding multiple sets of heavy loads, high speeds, etc., and the bearing status was found to be between abnormal and severe. The experimental results are basically consistent with the predictions.

[0140] The contents not described in detail in this specification belong to the prior art known to the professional and technical personnel in this field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for the technical solutions described in the aforementioned embodiments to be modified, or some of the technical features thereof to be replaced by equivalents, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wireless monitoring device for a main shaft bearing of a wind turbine generator set, characterized in that: It includes a data monitoring module, a data receiving module and a host computer control module; The data monitoring module is cylindrical and is placed at the center opening of the roller. It carries a six-axis motion monitoring unit, a temperature sensor and three "π"-type pressure sensors. The data monitoring module directly obtains the three-axis angular velocity w of the bearing roller based on the sensor. x 、w y 、w z , triaxial acceleration a x 、a y 、a z , temperature data T, voltages V1, V2, V3 that change due to pressure, relative rotation angle α of the roller t , and calculate the pressure F1, F2, F3 on the roller and the position angle θ of the roller relative to the bearing based on the acquired data t ; Data receiving module, receiving data from the data monitoring module and issuing commands; The host computer control module is used to process the received data, calculate the service status of the wind turbine bearing according to the hierarchical analysis degradation evaluation model, and control the working status of the data monitoring module.

2. The wireless monitoring device for the main shaft bearing of a wind turbine generator set according to claim 1 is characterized in that: The data monitoring module includes a monitoring terminal upper shell, a monitoring main board and a monitoring terminal lower shell which are arranged in sequence from top to bottom. A battery slot is arranged on the monitoring terminal upper shell. Three "π"-shaped pressure sensor placement slots are equidistantly placed on the central axis of the data monitoring module. Three "π"-shaped pressure sensors are placed in the slots. Three "π"-shaped pressure sensors are located on the monitoring main board. The three "π"-shaped pressure sensors are electrically connected to the monitoring main board by flying wires.

3. The wireless monitoring device for the main shaft bearing of a wind turbine generator set according to claim 2 is characterized in that: The monitoring main board is an integrated circuit board, which includes a communication circuit, a pressure signal acquisition circuit and a power supply circuit.

4. The wireless monitoring device for the main shaft bearing of a wind turbine generator set according to claim 2 is characterized in that: The "π"-shaped pressure sensor fixing part has four holes, the two middle ones are positioning pins, which use cylindrical pins; the two on both sides use threaded cylindrical pins, which are horizontally placed on the central axis of the data monitoring module, and the tails are equidistantly distributed in the center of the roller. The tail of the "π"-shaped pressure sensor is exposed and protrudes from the outside of the monitoring roller.

5. The wireless monitoring device for the main shaft bearing of a wind turbine generator set according to claim 1 is characterized in that: The data monitoring module, the data receiving module and the host computer control module are connected via a wireless communication module.

6. The wireless monitoring device for the main shaft bearing of a wind turbine generator set according to claim 1 is characterized in that: In the data monitoring module, the pressures F1, F2, and F3 at both ends and in the middle of the roller, that is, the calculation formula of F, are shown in equations (1)-(3): Where: f is the pressure on the inner surface of the roller detected by the "π" type pressure sensor, and V is the voltage that changes when the roller is subjected to pressure; is the pressure action angle, r is the radius of the roller; R is the inner ring radius of the bearing, α t is the relative rotation angle of the roller at time t, provided by the six-axis motion monitoring unit, w z is the angular velocity of the roller around the Z axis, provided by the six-axis motion monitoring unit; μ is the roller inner surface strain in millimeters and is calculated using formula (1) and Hooke's law as f=0.0042*μ.

7. The wireless monitoring device for the main shaft bearing of a wind turbine generator set according to claim 1, characterized in that: The data receiving module includes a "mouth"-shaped body and a USB interface. The upper part of the "mouth"-shaped body is an RF transceiver unit and a signal enhancement antenna, and the lower bottom plate is a bus switching unit. The USB interface is directly connected to the bus switching unit. The bottom plate of the "mouth"-shaped body is placed with a host debugging switch and an intelligent monitoring system status display light.

8. The wireless monitoring device for the main shaft bearing of a wind turbine generator set according to claim 1, characterized in that: The upper computer control module remotely controls the working mode of the data monitoring module, and the working modes are "sleep mode", "data storage mode" and "data sending mode".

9. The wireless monitoring device for the main shaft bearing of a wind turbine generator set according to claim 1, characterized in that: In the host computer control module, the method for calculating the service status of the wind turbine bearing according to the hierarchical analysis degradation evaluation model is as follows: Step 1: Establish a hierarchical evaluation structure, which consists of the target layer, the criterion layer and the indicator layer in sequence; among which: The target layer is the bearing condition score, which indicates the final result that the entire model hopes to achieve; the criterion layer includes vibration level and wear degree, which indicates that the health status of the bearing is measured from different angles; the indicator layer includes acceleration peak a p , peak angular velocity w p , average pressure G, temperature T, and eccentric load pressure ΔG are specific quantitative standards for various aspects of the criterion layer and are the basic data layer; Step 2: Establish the following criterion layer judgment matrix and calculate the criterion layer weight vector W1=[w 11 ,w 12 ]; Where: p1 is the importance range of vibration level relative to wear degree, and its value is between 1 and 9; w 11 ,w 12 are the weights of vibration level and wear degree respectively; Step 3: Establish the following indicator layer judgment matrix and calculate the indicator layer weight vector W2 = [w 21, w 22 ] and W3=[w 31, w 32 ,w 33 ]: in: w 21 ,w 22 They are the peak acceleration a p , peak angular velocity w p The weight it occupies; w 31 ,w 32 ,w 33 are the weights of average pressure G, temperature T, and eccentric load pressure ΔG; p2 is the peak acceleration a p Relative to the peak angular velocity w p The importance of the value range is 1 to 9; p3 is the importance of the average pressure G relative to the temperature T, ranging from 1 to 9; p4 is the importance of the average pressure G relative to the eccentric pressure ΔG, with a value range of 1 to 9; p5 is the importance of temperature T relative to the eccentric load pressure ΔG, ranging from 1 to 9; Step 4: Data calculation and standardization According to the three-axis angular velocity obtained by the data monitoring module, w x 、w y 、w z , triaxial acceleration a x 、a y 、a z , temperature data T, three pressure data F1, F2, F3, further calculate the index layer parameters: velocity peak a p , angular velocity peak w p , average pressure G and eccentric load pressure ΔG; a p =max{a x ,a y ,a z }, w p =max{w x ,w y ,w z }, G=max{F1,F2,F}-min{F1,F2,F}; Then standardize the indicator layer parameters through the data standardization formula: Among them, x is the index layer parameter; Step 5. Calculate n based on the standardized index layer parameters i Current status rating of bearing set: n i =w 11 ×([a p ,w p ]×W2 T )+w 12 ×([G,T,ΔG]×W3 T ) Step 6: Calculate the current state of the bearing Each time the data receiving module receives a set of data, it will calculate an n i , calculate the bearing state N at this time according to the degradation formula: The health status of the bearing is determined by the size of N. When N is between 0.0 and 0.35, the bearing service status is "healthy". When N is between 0.35 and 0.60, the bearing service condition is "good"; when N is between 0.60 and 0.85, the bearing service condition is "abnormal"; when N is between 0.85 and 1.00, the bearing service condition is "serious".

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