Wind driven generator gear box oil product monitoring method and system, equipment and medium

Abstract

The invention relates to the technical field of monitoring of related equipment of a wind driven generator, in particular to a wind driven generator gear box oil product monitoring method and system, equipment and a medium, and the method mainly comprises the steps that basic parameters are analyzed through an oil product fault diagnosis model, and a result whether current oil is qualified or not is output; and correcting the acquisition frequency of the current sensing module according to a result output by the oil product fault diagnosis model and an acquisition frequency correction model, and outputting a current early warning level according to a comprehensive evaluation value. Traditional off-line detection is changed into real-time on-line monitoring, real-time monitoring of the lubrication state can be achieved, abnormal changes of grease performance can be found in time, a basis is provided for preventive maintenance, through continuous monitoring and data analysis, the health condition of the gearbox can be evaluated more accurately, the maintenance period and strategy can be optimized, the maintenance cost is reduced, and the maintenance efficiency is improved. The frequency of manual sampling and off-line analysis can be reduced, and the operation cost and potential safety hazards are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine-related equipment monitoring, and in particular to a wind turbine gearbox oil quality monitoring method, system, equipment, and medium. Background Art

[0002] The wind turbine gearbox is the core transmission device of the wind power generation system. It undertakes the key task of converting the low speed of the wind rotor to the high speed required by the generator. Its performance directly affects the efficiency and life of the unit. The gearbox needs to withstand extreme torque, resulting in a high failure rate, so the quality of the grease inside it needs to be monitored.

[0003] Traditional gearbox lubrication condition monitoring relies primarily on periodic sampling and offline laboratory analysis, a method with significant limitations. The sampling and analysis process is time-consuming, making it difficult to detect abnormal changes in lubricant levels. Offline analysis cannot reflect the actual lubrication conditions of the gearbox during operation, and frequent sampling and maintenance increases operating costs and safety risks. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system, equipment and medium for monitoring oil quality in a wind turbine gearbox to solve the above-mentioned problems in the prior art.

[0005] The present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a method for monitoring oil quality in a wind turbine gearbox, comprising:

[0007] The basic parameters of the grease in the gearbox are obtained through the sensor module, and an oil fault diagnosis model is established. The basic parameters are analyzed by the oil fault diagnosis model to output the result of whether the current grease is qualified;

[0008] Establish an acquisition frequency correction model, correct the acquisition frequency of the current sensor module according to the output results of the oil fault diagnosis model and the acquisition frequency correction model, and output the corrected acquisition frequency;

[0009] If the current result is that the oil is qualified, the next basic parameter collection will be carried out according to the collection frequency;

[0010] If the current result is unqualified, a comprehensive evaluation is performed based on the current basic parameters to obtain a comprehensive evaluation value, and several warning levels of different severity are set. The current warning level is output based on the comprehensive evaluation value;

[0011] Send a first warning signal according to the current warning level, and wait for receiving a warning processing signal. If a warning processing signal is received, delete the current warning level;

[0012] If no warning processing signal is received and the next basic parameter collection time node has been reached according to the collection frequency, the current warning level will be corrected and a second warning signal will be sent.

[0013] Preferably, the establishment of the oil fault diagnosis model includes:

[0014] Obtain the historical parameters of the grease in the gearbox, preprocess the current basic parameters, normalize the historical parameters and the preprocessed basic parameters, and divide the normalized historical parameters into training sets and test sets;

[0015] Establish a neural network model, train the neural network model through the training set, output the trained neural network model, and adjust the learning rate through the test set to optimize the current neural network and output the optimal neural network model;

[0016] The normalized basic parameters are used to establish an input sequence, input the optimal neural network model, output the confidence level, and output the qualified result based on the confidence level.

[0017] Preferably, the neural network model includes a long short-term memory network, and the long short-term memory network includes a forget gate, an input gate, candidate memory cells and an output gate;

[0018] Also includes:

[0019] F t =σ(W f x t +W f h t-1 +b f )

[0020] I t =σ(W i x t +W i h t-1 +b i )

[0021] Q t =tanh(W c x t +W c h t-1 +b c )

[0022] O t =σ(W o x t +W o h t-1 +b o )

[0023] Ct =F t ⊙C t-1 +I t ⊙Q t

[0024] H=O t ⊙tanh(C t )

[0025] Where, F t , I t , Q t , O t 、C t and H are the forget gate, input gate, candidate memory cell, output gate, memory cell, and the hidden state of the new round, σ is the activation function, and W f 、W i 、W c 、W o is the weight matrix, x t is the input of the tth time step of the input sequence, h t-1 is the hidden state of the t-1th time step, b f 、b i 、b c and b o For bias.

[0026] Preferably, the method further includes establishing a deformation long short-term memory network including:

[0027] When i is an odd number:

[0028]

[0029] When i is an even number:

[0030]

[0031] Where, For the i-th round The result after interaction, Q i and R i is the interaction matrix, For the i-th round The result after interaction, when i=1,

[0032] Preferably, the establishing of the acquisition frequency correction model includes:

[0033]

[0034] Where η x is the corrected acquisition frequency, η zis the initial acquisition frequency, λ is the calculation coefficient, when the result is unqualified, λ=1, when the result is qualified, λ=-1, ε a is the qualified grease viscosity, ε b is the current grease viscosity, μ a is the dielectric constant of qualified grease, μ b is the current grease dielectric constant, ρ a is the qualified grease density, ρ b is the current oil density, δ a is the qualified oil moisture content, δ b is the current moisture content of oil.

[0035] Preferably, the comprehensive evaluation based on the current basic parameters includes:

[0036]

[0037] Where G x It is a comprehensive evaluation value.

[0038] Preferably, the setting of several warning levels of different severity and outputting the current warning level according to the comprehensive evaluation value includes:

[0039] Setting a first evaluation value threshold G1 and a second evaluation value threshold G2;

[0040] When G x ≤G1, the output current warning level is the first level, when G1 <G x When ≤G2, the output current warning level is the second level. x >G2, the output current warning level is the third level.

[0041] In a second aspect, the present invention further provides a wind turbine gearbox oil quality monitoring system, comprising:

[0042] The analysis module is configured to obtain basic parameters of the grease in the gearbox through the sensor module, establish an oil fault diagnosis model, and analyze the basic parameters through the oil fault diagnosis model to output a result of whether the current grease is qualified;

[0043] The acquisition frequency adjustment module is configured to establish an acquisition frequency correction model, correct the acquisition frequency of the current sensor module according to the results output by the oil fault diagnosis model and the acquisition frequency correction model, and output the corrected acquisition frequency;

[0044] The alarm module is configured to, if the current result is that the oil and fat is qualified, continue to collect the next basic parameters according to the collection frequency; if the current result is unqualified, conduct a comprehensive evaluation based on the current basic parameters to obtain a comprehensive evaluation value, set several warning levels of different severity, and output the current warning level based on the comprehensive evaluation value; send a first warning signal based on the current warning level and wait for the receipt of a warning processing signal. If the warning processing signal is received, the current warning level is deleted; if the warning processing signal is not received and the next basic parameter collection time node has arrived according to the collection frequency, the current warning level is corrected and a second warning signal is sent;

[0045] The main control device is connected to the analysis module, the acquisition frequency adjustment module and the alarm module, and is used to execute the above-mentioned method for monitoring oil quality of a wind turbine gearbox.

[0046] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for monitoring oil quality in a wind turbine gearbox when executing the computer program.

[0047] In a fourth aspect, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for monitoring oil quality in a wind turbine gearbox is implemented.

[0048] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0049] The method provided by the present invention mainly includes analyzing basic parameters through an oil fault diagnosis model to output a result indicating whether the current oil is qualified, correcting the current sensor module's acquisition frequency based on the results output by the oil fault diagnosis model and the acquisition frequency correction model, and outputting the current warning level based on the comprehensive evaluation value; sending a first warning signal based on the current warning level, and waiting to receive a warning processing signal. Through the above method, traditional offline detection is changed to real-time online monitoring, which can achieve real-time monitoring of lubrication status, timely detect abnormal changes in oil performance, and provide a basis for preventive maintenance. Through continuous monitoring and data analysis, the health status of the gearbox can be more accurately assessed, maintenance cycles and strategies can be optimized, maintenance costs can be reduced, the frequency of manual sampling and offline analysis can be reduced, and operating costs and safety hazards can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 It is a control flow diagram of the present invention;

[0052] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0054] The terms "first," "second," and so on, in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. The naming or numbering of steps in this application does not necessarily imply that the steps in the method flow must be executed in the chronological or logical order indicated by the naming or numbering. Named or numbered process steps may be executed in a different order based on the desired technical objectives, as long as the same or similar technical effects are achieved.

[0055] Independently described modules or submodules may or may not be physically separate; they may be implemented in software or hardware. Some modules or submodules may be implemented in software, with the processor invoking the software to implement the functionality of these modules or submodules, while other modules or submodules may be implemented in hardware, such as hardware circuits. Furthermore, some or all of the modules may be selected based on actual needs to achieve the objectives of the present application.

[0056] Please refer to Figure 1-Figure 2 The present invention provides a method for monitoring oil quality in a wind turbine gearbox, comprising:

[0057] S101: obtaining basic parameters of the grease in the current gearbox through the sensor module, and establishing an oil fault diagnosis model. The oil fault diagnosis model is used to analyze the basic parameters and output a result of whether the current grease is qualified;

[0058] In this embodiment, a direct judgment of the online model is adopted to make a judgment result on the current grease condition, thus avoiding the disadvantages of offline detection. The sensing module can use a sensor that can collect corresponding basic parameters.

[0059] S102: Establishing an acquisition frequency correction model, correcting the acquisition frequency of the current sensor module according to the output of the oil fault diagnosis model and the acquisition frequency correction model, and outputting the corrected acquisition frequency;

[0060] Since the collection frequency of the collection module will increase the load of the entire inspection and monitoring equipment, it is necessary to propose a more reasonable way to set the collection frequency. For example, when the oil is unqualified, it is necessary to increase the collection frequency and provide multiple feedbacks in a short period of time to avoid potential risks. When the oil is qualified, the current collection frequency can be appropriately reduced to reduce the burden on the current monitoring equipment and achieve the purpose of energy saving.

[0061] S103: If the current result is that the oil is qualified, continue to collect the next basic parameters according to the collection frequency;

[0062] S104: If the current result is unqualified, a comprehensive evaluation is performed based on the current basic parameters to obtain a comprehensive evaluation value, and several warning levels of different severity are set. The current warning level is output based on the comprehensive evaluation value;

[0063] Different warning levels can reflect the severity of the current situation and can prompt staff to respond differently to deal with the current situation.

[0064] S105: Sending a first warning signal according to the current warning level and waiting to receive a warning processing signal. If a warning processing signal is received, deleting the current warning level;

[0065] S106: If no warning processing signal is received and the next basic parameter collection node is reached according to the collection frequency, the current warning level is corrected and a second warning signal is sent.

[0066] Among them, if no signal for the current processing has been received, there is always a safety hazard, and the current warning level needs to be modified towards a more serious warning level to remind the staff again of the current serious situation.

[0067] The method provided by the present invention mainly includes analyzing basic parameters through an oil fault diagnosis model to output a result indicating whether the current oil is qualified, correcting the current sensor module's acquisition frequency based on the results output by the oil fault diagnosis model and the acquisition frequency correction model, and outputting the current warning level based on the comprehensive evaluation value; sending a first warning signal based on the current warning level, and waiting to receive a warning processing signal. Through the above method, traditional offline detection is changed to real-time online monitoring, which can achieve real-time monitoring of lubrication status, timely detect abnormal changes in oil performance, and provide a basis for preventive maintenance. Through continuous monitoring and data analysis, the health status of the gearbox can be more accurately assessed, maintenance cycles and strategies can be optimized, maintenance costs can be reduced, the frequency of manual sampling and offline analysis can be reduced, and operating costs and safety hazards can be reduced.

[0068] In an exemplary embodiment of the present invention, establishing an oil product fault diagnosis model includes:

[0069] Obtain the historical parameters of the grease in the gearbox, preprocess the current basic parameters, normalize the historical parameters and the preprocessed basic parameters, and divide the normalized historical parameters into training sets and test sets;

[0070] Establish a neural network model, train the neural network model through the training set, output the trained neural network model, and adjust the learning rate through the test set to optimize the current neural network and output the optimal neural network model;

[0071] The normalized basic parameters are used to establish an input sequence, input the optimal neural network model, output the confidence level, and output the qualified result based on the confidence level.

[0072] Specifically, the neural network model includes a long short-term memory network, and the long short-term memory network includes a forget gate, an input gate, candidate memory cells, and an output gate;

[0073] Also includes:

[0074] F t =σ(W f x t +W f h t-1 +b f )

[0075] I t =σ(W i x t +W i h t-1 +b i )

[0076] Q t =tanh(Wc x t +W c h t-1 +b c )

[0077] O t =σ(W o x t +W o h t-1 +b o )

[0078] C t =F t ⊙C t-1 +I t ⊙Q t

[0079] H=O t ⊙tanh(C t )

[0080] Where, F t , I t , Q t , O t 、C t and H are respectively the forget gate, input gate, candidate memory cell, output gate, memory cell, and the hidden state of the new round, σ is the activation function, and W f 、W i 、W c 、W o is the weight matrix, x t is the input of the tth time step of the input sequence, h t-1 is the hidden state at the t-1th time step, b f 、b i 、b c and b o For bias.

[0081] The forget gate controls the forget state of the upper layer, taking in the current moment's information as input. The previous moment's hidden state, after being processed by the activation function, generates an input between 0 and 1. This is then multiplied by the memory cell data. If this value approaches 0, the memory value is ignored.

[0082] Input gate: Similar to the forget gate, it is first processed by the activation function to obtain a value between 0 and 1. When the input gate is close to 0 and the forget gate is close to 1, the elements of the memory cell are saved.

[0083] Candidate memory cells: Unlike the forget gate, the candidate memory cells replace the activation function with the tanh function, and the resulting value is between -1 and 1.

[0084] Output gate: controls the flow of memory cells to the hidden state of the next time.

[0085] In this embodiment, the range of data normalization is -1 to 1. When the confidence value finally output by the neural network model is less than 0, it is judged that the current oil is unqualified. When the confidence value is approximately equal to 0, it is judged that the current oil is qualified.

[0086] Secondly, in the above model, h t-1 and x t There is no interaction between them, which has a certain impact on the performance of the network. Therefore, it also includes the establishment of a deformation long short-term memory network including:

[0087] When i is an odd number:

[0088]

[0089] When i is an even number:

[0090]

[0091] Where, For the i-th round The result after interaction, Q i and R i is the interaction matrix, For the i-th round The result after interaction, when i=1,

[0092] By interacting with the input and hidden states, we can optimize the performance of the network, improve the effect of the model, and have a positive impact on the entire network.

[0093] In an exemplary embodiment of the present invention, establishing an acquisition frequency correction model includes:

[0094]

[0095] Where η x is the corrected acquisition frequency, η z is the initial acquisition frequency, λ is the calculation coefficient, when the result is unqualified, λ=1, when the result is qualified, λ=-1, ε a is the qualified grease viscosity, ε b is the current grease viscosity, μ a is the dielectric constant of qualified grease, μ b is the current grease dielectric constant, ρ a is the qualified grease density, ρ b is the current oil density, δ a is the qualified oil moisture content, δ b is the current moisture content of oil.

[0096] Through the above model, the most important centralized parameters for oil quality evaluation are selected, and the difference between the current data and the standard qualified data is obtained by calculation. The collection frequency is dynamically adjusted based on the difference, so that the final collection frequency is within a more reasonable range.

[0097] Secondly, a comprehensive evaluation based on the current basic parameters includes:

[0098]

[0099] Where G x It is a comprehensive evaluation value.

[0100] Preferably, the setting of several warning levels of different severity and outputting the current warning level according to the comprehensive evaluation value includes:

[0101] Setting a first evaluation value threshold G1 and a second evaluation value threshold G2;

[0102] When G x ≤G1, the output current warning level is the first level, when G1 <G x When ≤G2, the output current warning level is the second level. x >G2, the output current warning level is the third level.

[0103] In a second aspect, the present invention further provides a wind turbine gearbox oil quality monitoring system, comprising:

[0104] The analysis module is configured to obtain basic parameters of the grease in the gearbox through the sensor module, establish an oil fault diagnosis model, and analyze the basic parameters through the oil fault diagnosis model to output a result of whether the current grease is qualified;

[0105] The acquisition frequency adjustment module is configured to establish an acquisition frequency correction model, correct the acquisition frequency of the current sensor module according to the results output by the oil fault diagnosis model and the acquisition frequency correction model, and output the corrected acquisition frequency;

[0106] The alarm module is configured to, if the current result is that the oil and fat is qualified, continue to collect the next basic parameters according to the collection frequency; if the current result is unqualified, conduct a comprehensive evaluation based on the current basic parameters to obtain a comprehensive evaluation value, set several warning levels of different severity, and output the current warning level based on the comprehensive evaluation value; send a first warning signal based on the current warning level and wait for the receipt of a warning processing signal. If the warning processing signal is received, the current warning level is deleted; if the warning processing signal is not received and the next basic parameter collection time node has arrived according to the collection frequency, the current warning level is corrected and a second warning signal is sent;

[0107] The main control device is connected to the analysis module, the acquisition frequency adjustment module and the alarm module, and is used to execute the above-mentioned method for monitoring oil quality of a wind turbine gearbox.

[0108] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, optical disks, and other media that can store program code.

[0110] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for monitoring oil quality in a wind turbine gearbox, characterized in that: include: The basic parameters of the grease in the gearbox are obtained through the sensor module, and an oil fault diagnosis model is established. The basic parameters are analyzed by the oil fault diagnosis model to output the result of whether the current grease is qualified; Establish an acquisition frequency correction model, correct the acquisition frequency of the current sensor module according to the output results of the oil fault diagnosis model and the acquisition frequency correction model, and output the corrected acquisition frequency; If the current result is that the oil is qualified, the next basic parameter collection will be carried out according to the collection frequency; If the current result is unqualified, a comprehensive evaluation is performed based on the current basic parameters to obtain a comprehensive evaluation value, and several warning levels of different severity are set. The current warning level is output based on the comprehensive evaluation value; Send a first warning signal according to the current warning level, and wait for receiving a warning processing signal. If a warning processing signal is received, delete the current warning level; If no warning processing signal is received and the next basic parameter collection time node has been reached according to the collection frequency, the current warning level will be corrected and a second warning signal will be sent.

2. A method for monitoring oil quality of a wind turbine gearbox according to claim 1, characterized in that: The establishment of the oil product fault diagnosis model includes: Obtain the historical parameters of the grease in the gearbox, preprocess the current basic parameters, normalize the historical parameters and the preprocessed basic parameters, and divide the normalized historical parameters into training sets and test sets; Establish a neural network model, train the neural network model through the training set, output the trained neural network model, and adjust the learning rate through the test set to optimize the current neural network and output the optimal neural network model; The normalized basic parameters are used to establish an input sequence, input the optimal neural network model, output the confidence level, and output the qualified result based on the confidence level.

3. A method for monitoring oil quality of a wind turbine gearbox according to claim 2, characterized in that: The neural network model includes a long short-term memory network, and the long short-term memory network includes a forget gate, an input gate, candidate memory cells and an output gate; Also includes: F t =σ(W f x t +W f h t-1 +b f ) I t =σ(W i x t +W i h t-1 +b i ) Q t =tanh(W c x t +W c h t-1 +b c ) O t =σ(W o x t +W o h t-1 +b o ) C t =F t ⊙C t-1 +I t ⊙Q t H=O t ⊙tanh(C t ) Where, F t , I t , Q t , O t 、C t and H are the forget gate, input gate, candidate memory cell, output gate, memory cell, and the hidden state of the new round, σ is the activation function, and W f 、W i 、W c 、W o is the weight matrix, x t is the input of the tth time step of the input sequence, h t-1 is the hidden state at the t-1th time step, b f 、b i 、b c and b o For bias.

4. A method for monitoring oil quality of a wind turbine gearbox according to claim 3, characterized in that: It also includes the establishment of a deformation long short-term memory network including: When i is an odd number: When i is an even number: Where, For the i-th round The result after interaction, Q i and R i is the interaction matrix, For the i-th round The result after interaction, when i=1, 5. The method for monitoring oil quality of a wind turbine gearbox according to claim 4, characterized in that: The establishment of the acquisition frequency correction model comprises: Where η x is the corrected acquisition frequency, η z is the initial acquisition frequency, λ is the calculation coefficient, when the result is unqualified, λ=1, when the result is qualified, λ=-1, ε a is the qualified grease viscosity, ε b is the current grease viscosity, μ a is the dielectric constant of qualified grease, μ b is the current grease dielectric constant, ρ a is the qualified grease density, ρ b is the current oil density, δ a is the qualified oil moisture content, δ b is the current moisture content of oil.

6. A method for monitoring oil quality of a wind turbine gearbox according to claim 5, characterized in that: The comprehensive evaluation based on the current basic parameters includes: Where G x It is a comprehensive evaluation value.

7. The method for monitoring oil quality of a wind turbine gearbox according to claim 6, characterized in that: The aforementioned method of setting a number of warning levels of different severity and outputting the current warning level according to the comprehensive evaluation value includes: Setting a first evaluation value threshold G1 and a second evaluation value threshold G2; When G x ≤G1, the output current warning level is the first level, when G1 <G x When ≤G2, the output current warning level is the second level. x >G2, the output current warning level is the third level.

8. A wind turbine gearbox oil quality monitoring system, characterized in that: include: The analysis module is configured to obtain basic parameters of the grease in the gearbox through the sensor module, establish an oil fault diagnosis model, and analyze the basic parameters through the oil fault diagnosis model to output a result of whether the current grease is qualified; The acquisition frequency adjustment module is configured to establish an acquisition frequency correction model, correct the acquisition frequency of the current sensor module according to the results output by the oil fault diagnosis model and the acquisition frequency correction model, and output the corrected acquisition frequency; The alarm module is configured to continue collecting basic parameters according to the collection frequency if the current result shows that the oil is qualified; if the current result shows that the oil is unqualified, it will conduct a comprehensive evaluation based on the current basic parameters to obtain a comprehensive evaluation value, set several warning levels of different severity, and output the current warning level based on the comprehensive evaluation value; Send a first warning signal according to the current warning level, and wait for receiving a warning processing signal. If a warning processing signal is received, delete the current warning level; If no warning processing signal is received and the next basic parameter collection time node has arrived according to the collection frequency, the current warning level will be revised and a second warning signal will be sent; A main control device is connected to the analysis module, the acquisition frequency adjustment module and the alarm module, and is used to execute the wind turbine gearbox oil quality monitoring method according to any one of claims 1 to 7.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, a method for monitoring oil quality in a wind turbine gearbox according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for monitoring oil quality of a wind turbine gearbox according to any one of claims 1 to 7 is implemented.

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