Model-based fan gearbox temperature early warning method, medium, and device

Abstract

The application provides a model-based fan gear box temperature early warning method, medium and equipment, comprising: selecting a fan gear box with a typical structure, installing temperature sensors and working condition sensors at key positions of the gear box, and constructing a fan gear box physical model accordingly; obtaining running parameters of the gear box under different working conditions; obtaining a solving equation of the relationship between the gear box temperature and the running parameters based on a heat conduction differential equation and an energy balance equation of the fan gear box; obtaining temperature prediction values of the gear box under different working conditions based on the running parameters of the gear box and the solving equation of the relationship between the temperature and the running parameters, and selecting the highest prediction temperature as the maximum prediction temperature; and obtaining the early warning state and the failure risk degree of the gear box based on the maximum prediction temperature, a temperature early warning threshold calculation equation and a failure risk assessment equation. The application can effectively early warn the temperature of the fan gear box and guarantee the safe and stable operation of the fan gear box.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of temperature monitoring and early warning of wind power generation equipment, in particular to a model-based wind turbine gearbox temperature early warning method, medium and equipment. BACKGROUND

[0002] The wind turbine gearbox is a key component of the wind turbine generator set, and its operating state directly affects the stability and reliability of the entire wind turbine system. During the operation of the wind turbine, the temperature change of the gearbox is one of the important indicators reflecting its operating condition. Accurate monitoring and early warning of the gearbox temperature is crucial for preventing failures and improving the operating efficiency of the wind turbine.

[0003] Currently, there are various methods for monitoring and early warning of the temperature of the wind turbine gearbox. Some traditional methods are mainly based on experience threshold setting, and whether to issue an early warning is determined by simply comparing the actual temperature with the preset threshold. However, this approach lacks comprehensive consideration of the complex heat transfer process inside the gearbox and the changes in operating conditions, and the accuracy of early warning is limited. In addition, although some existing technologies attempt to establish a model for temperature prediction, the model is often oversimplified and does not fully consider the influence of multiple factors such as speed, load, and lubrication state under different operating conditions on the temperature, resulting in a deviation between the predicted results and the actual situation, making it difficult to achieve accurate and effective temperature early warning.

[0004] In the implementation of the embodiments of the present application, the inventors have found that the existing technology at least has the following problems or defects: the traditional experience threshold early warning method has insufficient accuracy, and the existing model prediction method does not comprehensively consider multiple operating condition factors, resulting in a large temperature prediction deviation, which cannot accurately and effectively early warn the temperature change of the wind turbine gearbox, affecting the safe and stable operation of the wind turbine. SUMMARY

[0005] The embodiments of the present application aim to provide a model-based wind turbine gearbox temperature early warning method, medium and equipment to solve the technical problems in the prior art.

[0006] The embodiments of the present application solve the technical problems thereof by adopting the following technical solutions:

[0007] In a first aspect, a model-based wind turbine gearbox temperature early warning method is provided, comprising:

[0008] A wind turbine gearbox with a typical structure is selected, and temperature sensors and operating condition sensors are installed at key positions of the gearbox, based on which a physical model of the wind turbine gearbox is constructed;

[0009] The operating parameters of the gearbox under different operating conditions are obtained, including but not limited to speed, load, and lubrication state parameters;

[0010] Based on the heat conduction differential equation and energy balance equation of the fan gearbox, the solving equation of the relationship between the gearbox temperature and the operating parameters is obtained;

[0011] Based on the operating parameters of the gearbox and the solving equation of the relationship between the temperature and the operating parameters, the predicted temperature values of the gearbox under different operating conditions are obtained, and the highest predicted temperature value is selected as the maximum predicted temperature;

[0012] Based on the maximum predicted temperature and the temperature warning threshold calculation equation and the fault risk assessment equation, the warning state and the fault risk degree of the gearbox are obtained.

[0013] Further, the operating condition sensors include a rotational speed sensor, a torque sensor, and an oil pressure sensor, including the following three installation positions:

[0014] The rotational speed sensor and the torque sensor are installed at the input shaft of the gearbox;

[0015] The oil pressure sensor is installed at the main oil pipe of the gearbox lubrication system;

[0016] The temperature sensor is installed at a suitable position of the gearbox body.

[0017] Further, the operating parameters of the gearbox under different operating conditions are obtained by the following formula:

[0018]

[0019] Q=k1×P+k2

[0020] In the formula, P is the power, n is the rotational speed, T is the torque, Q is the lubrication flow, and k1 and k2 are coefficients related to the structure of the gearbox.

[0021] Further, the heat conduction differential equation of the fan gearbox is:

[0022]

[0023] In the formula, p is the material density of the gearbox, c is the specific heat capacity, T is the temperature, t is the time, k is the thermal conductivity, and q is the internal heat source term.

[0024] The energy balance equation is:

[0025] Q in -Q out +Q gen =Q st

[0026] In which, Q in is the input heat, Q out is the outgoing heat, Q gen is the internal generated heat, and Q st is the heat storage change.

[0027] Further, the solving equation of the relationship between the gear box temperature and the operation parameter is:

[0028] T=f(n,T env ,P,L)

[0029] Wherein, T is the gear box temperature, n is the rotating speed, T env is the environment temperature, P is the power, and L is the lubrication state parameter.

[0030]

[0031] In the formula, a ijkl is a coefficient determined by experiment or theoretical analysis, m, n, p, q are orders determined according to the complexity of the model.

[0032] Further, the solving equation of the relationship between the gear box temperature and the operation parameter is used to obtain the temperature prediction value of the gear box under different working conditions and select the maximum temperature prediction value from the temperature prediction value.

[0033] The operation parameter of the gear box under different working conditions obtained by the formula (3) is substituted into the solving equation of the relationship between the temperature and the operation parameter, the equation is solved, the temperature prediction value of the gear box under different working conditions is obtained, and the highest prediction temperature in the prediction temperature is selected as the maximum prediction temperature.

[0034] Further, the temperature early warning threshold calculation equation is:

[0035] T warn =T nom +k3σ

[0036] Wherein, T warn is the early warning temperature threshold, T nom is the normal working temperature mean value, k3 is the safety coefficient, and sigma is the normal working temperature standard deviation.

[0037] Further, the fault risk assessment equation is:

[0038]

[0039] In the formula, R is the fault risk degree between 0 and 1, k4 is the correction coefficient, and T max is the maximum prediction temperature.

[0040] In the second aspect of the application, a computer readable storage medium is provided, which includes instructions, when running on the computer, so that the computer executes the method in any one of the first aspect.

[0041] In a third aspect of the present application, an electronic device is provided, comprising at least one processor, a memory and an input-output unit; wherein the memory is configured to store a computer program, and the processor is configured to invoke the computer program stored in the memory to execute the method of any one of the first aspect.

[0042] The above embodiments of the present application have at least the following beneficial effects: the fan gearbox temperature early warning method can obtain a more accurate temperature and operation parameter relationship solving equation by constructing a physical model, combining a heat conduction differential equation and an energy balance equation, deeply analyzing the internal heat transfer process of the gearbox, and fully considering various operation parameters, thereby accurately calculating the temperature prediction value under different working conditions, providing a reliable basis for temperature early warning, improving the accuracy and reliability of early warning, effectively preventing the failure of the fan gearbox caused by abnormal temperature, and ensuring the stable operation of the fan system.

[0043] The method can calculate the maximum predicted temperature and the temperature early warning threshold equation and the fault risk assessment equation, not only can determine the early warning state of the gearbox in time, but also can quantitatively evaluate the fault risk degree, so that the operation and maintenance personnel can more intuitively understand the running condition of the gearbox, take corresponding measures in advance, such as optimizing the operation parameters, arranging the maintenance plan, etc., reduce the maintenance cost, prolong the service life of the gearbox, and improve the overall operation efficiency and economy of the fan. BRIEF DESCRIPTION OF DRAWINGS

[0044] One or more embodiments are illustrated by way of example in the figures that form a part of this patent document, these illustrative examples do not limit the embodiments, and elements having the same reference numbers designate analogous elements throughout the drawings, unless otherwise specified, the figures in the drawings do not constitute a proportional limit.

[0045] Figure 1 A flowchart of a model-based fan gearbox temperature early warning method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0046] For the convenience of understanding the present application, the present application will be described in more detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that when an element is described as "connected" to another element, it can be directly connected to the other element, or one or more intervening elements can be present therebetween. The terms "upper", "lower", "left", "right", "top", "bottom", "top", "bottom", and the like used in the specification indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", and the like are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0047] Unless otherwise defined, all technical and scientific terms used in the specification have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0048] The present application will be described in detail below in conjunction with the accompanying drawings and specific embodiments. Figure 1 The model-based fan gearbox temperature warning method 100 provided by the embodiments of the present application will be described in detail below in conjunction with specific embodiments.

[0049] Figure 1 is a flowchart of the model-based fan gearbox temperature warning method provided by the present application. The model-based fan gearbox temperature warning method provided by one embodiment of the present application comprises:

[0050] Step 101, selecting a fan gearbox with a typical structure, installing temperature sensors and working condition sensors at key parts of the gearbox, and constructing a fan gearbox physical model accordingly;

[0051] Step 102, obtaining the operating parameters of the gearbox under different working conditions, including but not limited to speed, load, and lubrication state parameters;

[0052] Step 103, based on the heat conduction differential equation and the energy balance equation of the fan gearbox, obtaining the solving equation of the relationship between the temperature of the gearbox and the operating parameters;

[0053] Step 104, based on the operating parameters of the gearbox and the solving equation of the relationship between the temperature and the operating parameters, obtaining the temperature prediction value of the gearbox under different working conditions and selecting the highest prediction temperature as the maximum prediction temperature from among them;

[0054] Step 105, based on the maximum prediction temperature, the temperature warning threshold equation, and the fault risk assessment equation, obtaining the warning state and the fault risk degree of the gearbox.

[0055] It should be noted that the present embodiment relates to a model-based fan gearbox temperature warning method. First, a fan gearbox with a typical structure is selected, and temperature sensors and working condition sensors are installed at key positions of the gearbox. The fan gearbox here refers to a key mechanical component for transmitting power and increasing speed in a wind turbine generator set, while the temperature sensors and working condition sensors are devices for monitoring the operating state and temperature changes of the gearbox.

[0056] Specifically, the working condition sensors include a speed sensor, a torque sensor, and an oil pressure sensor, which are installed at the input shaft of the gearbox, the main oil pipe of the lubrication system, and appropriate positions of the gearbox body, respectively.

[0057] More specifically, these sensors are used to obtain operating parameters of the gearbox under different working conditions, such as speed, load, and lubrication state parameters. For example, the speed sensor can measure the real-time speed of the gearbox, the torque sensor can measure the torque transmitted to the gearbox, and the oil pressure sensor can monitor the oil pressure of the lubrication system.

[0058] Preferably, the specific parameter settings of these sensors can be adjusted according to the specific model and operating conditions of the gearbox. For example, the speed sensor can be set to measure a speed range of 0-1000 revolutions per minute, the torque sensor can be set to measure a torque range of 0-10000 Newton meters, and the oil pressure sensor can be set to measure an oil pressure range of 0-10 bars.

[0059] Further, wireless transmission or wired transmission can be selected to collect sensor data according to actual needs, to realize real-time monitoring of the fan gearbox.

[0060] In some embodiments, the working condition sensors include a speed sensor, a torque sensor, and an oil pressure sensor, including the following three installation positions:

[0061] The speed sensor and the torque sensor are installed at the input shaft of the gearbox;

[0062] The oil pressure sensor is installed at the main oil pipe of the gearbox lubrication system;

[0063] The temperature sensor is installed at appropriate positions of the gearbox body.

[0064] It should be noted that the present embodiment describes the specific installation position of the working condition sensor in the fan gearbox temperature early warning method. The working condition sensor includes a rotational speed sensor, a torque sensor, and an oil pressure sensor, which are installed at different key positions of the gearbox to monitor its operating state. The rotational speed sensor is used to measure the rotational speed of the gearbox, the torque sensor is used to measure the torque load of the gearbox, and the oil pressure sensor is used to monitor the oil pressure of the gearbox lubrication system.

[0065] Specifically, the rotational speed sensor and the torque sensor are installed at the input shaft of the gearbox, so that the rotational speed and torque of the gearbox can be accurately measured when the gearbox receives the initial power from the wind turbine. The oil pressure sensor is installed at the main oil pipe of the gearbox lubrication system to monitor the pressure of the lubricating oil and ensure that the gearbox is properly lubricated. The temperature sensor is installed at an appropriate position of the gearbox body to monitor the temperature change inside the gearbox.

[0066] Further, the specific parameter settings of these sensors should be determined according to the design and operation requirements of the gearbox, for example, the rotational speed sensor may need to be able to measure 0-3600 revolutions per minute, the torque sensor may need to be able to measure 0-50000 Newton meters of torque, and the oil pressure sensor may need to be able to measure 0-15 bar of oil pressure.

[0067] More specifically, in addition to the above installation positions, other installation schemes can also be considered, for example, in order to more accurately monitor the temperature distribution of the gearbox, multiple temperature sensors can be installed at different positions of the gearbox.

[0068] Further, the installation position of the sensor can also be adjusted according to the specific structure and maintenance channel of the gearbox to facilitate installation and maintenance. In the selection of sensors, high-precision sensors can be considered to improve the accuracy of monitoring data, or sensors with self-diagnosis function can be considered to provide timely alarm when the sensor fails. These improvements help to improve the reliability and effectiveness of the fan gearbox temperature early warning method.

[0069] In some embodiments, the operating parameters of the gearbox under different working conditions are obtained by the following formula:

[0070]

[0071] Q=k1xP+k2

[0072] In the formula, P is the power, n is the rotational speed, T is the torque, Q is the lubrication flow, and k1, k2 are coefficients related to the structure of the gearbox.

[0073] It is necessary to explain that the present embodiment details how to obtain the operating parameters of the fan gearbox under different working conditions. These parameters include rotational speed, torque, power and lubrication flow, which are key indicators for evaluating the performance and health condition of the gearbox. Here, power refers to the energy output by the gearbox during operation, rotational speed refers to the speed of the gearbox rotation, torque refers to the rotational torque transmitted by the gearbox, and lubrication flow refers to the flow of lubricating oil supplied to the gearbox by the lubrication system.

[0074] Specifically, power can be calculated by the product of rotational speed and torque, i.e. formula

[0075]

[0076] where P is power, N is rotational speed, and T is torque. Lubrication flow can be determined by another formula Q = Q1 x N + Q2, where Q is lubrication flow, and Q1 and Q2 are coefficients related to the structure of the gearbox. These coefficients can be determined according to the design parameters and historical operation data of the gearbox to ensure that the calculated power and lubrication flow accurately reflect the actual operating state of the gearbox.

[0077] Preferably, to improve the accuracy of parameter acquisition, high-precision sensors can be used to measure rotational speed and torque, and advanced data acquisition systems can be used to process these data. In addition, real-time monitoring system can be used to dynamically adjust the values of Q1 and Q2 to adapt to different working conditions and environmental changes.

[0078] Further, as an alternative, machine learning-based algorithms can also be used to predict lubrication flow, which can intelligently adjust the prediction model of lubrication flow according to historical data and current operating parameters, thereby improving the adaptability and accuracy of the early warning system. Through these detailed operation steps and alternative solutions, the temperature early warning system of the fan gearbox can be more accurate and reliable.

[0079] In some embodiments, the heat conduction differential equation of the fan gearbox is:

[0080]

[0081] where ρ is the density of the gearbox material, c is the specific heat capacity, T is the temperature, t is the time, k is the thermal conductivity, and q is the internal heat source term (including friction heat generation, etc.).

[0082] The energy balance equation is:

[0083] Q un -Q out +Q gen =Q st

[0084] where Qin Qin is the input heat (such as frictional heat), Q out Qout is the outgoing heat (such as heat dissipation through the housing), Q gen Qgen is the internally generated heat (such as heat generated by lubricant agitation), Q st Qs is the heat storage change.

[0085] It is noted that the present embodiment describes the key mathematical models in the fan gearbox temperature early warning method, namely the heat conduction differential equation and the energy balance equation. These equations are used to describe the heat transfer and energy conversion processes within the gearbox. The heat conduction differential equation is a mathematical expression that describes how heat changes over time and location within the gearbox material, while the energy balance equation is an equation that describes the relationship between energy input, output, and storage within the gearbox. The internal heat source term refers to the heat generated within the gearbox due to factors such as friction.

[0086] Specifically, the heat conduction differential equation can be expressed as

[0087]

[0088] where ρ is the density of the gearbox material, c is the specific heat capacity, T is the temperature, t is the time, k is the thermal conductivity, is the Laplacian operator, representing the spatial gradient of temperature, and Q is the internal heat source term, including frictional heat generation, etc.

[0089] Further, the energy balance equation can be expressed as

[0090]

[0091] where Qin is the input heat, Qout is the outgoing heat, Qgen is the internally generated heat, Qs is the heat storage change. The specific settings of these parameters need to be determined according to the material properties of the gearbox and the actual operating conditions.

[0092] Preferably, in order to more accurately simulate the heat conduction and energy balance of the gearbox, numerical methods such as finite element analysis can be used to solve these equations. In addition, experimental data can be combined to calibrate model parameters such as the specific values of thermal conductivity k, specific heat capacity c, and internal heat source term Q.

[0093] More specifically, different thermal conductivity and specific heat capacity values can be applied to different parts of the gearbox to reflect changes in material properties. As an alternative, machine learning techniques can also be used to predict the temperature distribution of the gearbox by training a model, which can reduce the dependence on physical models and potentially provide faster prediction results. Through these refined operational steps and alternatives, the accuracy and practicality of the temperature warning method can be improved.

[0094] In some embodiments, the equation for solving the relationship between the gearbox temperature and the operating parameters is:

[0095] T = f(n, T env , P, L)

[0096] where T is the gearbox temperature, n is the rotational speed, T env is the ambient temperature, P is the power, and L is the lubrication state parameter (such as oil film thickness, oil quality, and related parameters).

[0097]

[0098] where a ijkl is a coefficient determined by experiment or theoretical analysis, and m, n, p, q are orders determined according to the complexity of the model.

[0099] It should be noted that the present embodiment details how to solve the temperature of the gearbox based on the operating parameters of the gearbox and the relationship between the temperature and the operating parameters. The gearbox temperature here refers to the actual temperature of the gearbox during operation, and the operating parameters include rotational speed, ambient temperature, power, and lubrication state parameters. The lubrication state parameter involves the physical and chemical properties of the lubricating oil, such as oil film thickness and oil quality, which are crucial for the thermal management and performance of the gearbox.

[0100] Specifically, the relationship between the gearbox temperature and the operating parameters can be described by a function, i.e.

[0101] T = f(N, T env , P, λ)

[0102] where T represents the gearbox temperature, N represents the rotational speed, T env represents the ambient temperature, P represents the power, and λ represents the lubrication state parameter.

[0103] More specifically, this function can be determined through experimental data fitting or theoretical analysis, where the parameters a, b, c, d are coefficients determined by experiment or theoretical analysis, and m, n, p, q are orders determined according to the complexity of the model. The specific settings of these parameters need to be adjusted according to the specific design and operating conditions of the gearbox to ensure the accuracy and applicability of the model.

[0104] Preferably, to improve the prediction accuracy of the model, nonlinear regression analysis can be used to determine the coefficients and orders in the function. In addition, the differences in temperature influence at different stages such as starting, running and stopping of the gearbox can be considered, and different temperature prediction models can be established respectively. More specifically, a real-time data feedback mechanism can be introduced to dynamically adjust the model parameters to adapt to changes in gearbox operating conditions.

[0105] Further, as an alternative, artificial intelligence techniques such as neural networks can also be explored to build more complex temperature prediction models that can handle more variables and non-linear relationships, providing more accurate temperature predictions. Through these detailed operational steps and alternative solutions, the adaptability and accuracy of the temperature warning system can be enhanced.

[0106] In some embodiments, the solving equation of the relationship between the temperature and the operating parameters of the gearbox based on the operating parameters of the gearbox and the relationship between the temperature and the operating parameters, obtaining the temperature prediction values of the gearbox under different operating conditions, and selecting the maximum temperature prediction value from them includes:

[0107] Substituting the operating parameters of the gearbox under different operating conditions obtained by formula (3) into the solving equation of the relationship between the temperature and the operating parameters, solving the equation, obtaining the temperature prediction values of the gearbox under different operating conditions, and selecting the highest predicted temperature value among them as the maximum predicted temperature.

[0108] It should be noted that the present embodiment describes how to use the solving equation of the relationship between the temperature and the operating parameters of the gearbox based on the operating parameters of the gearbox to obtain the temperature prediction values of the gearbox under different operating conditions, and select the highest predicted temperature value as the maximum predicted temperature. The temperature prediction value here refers to the future temperature of the gearbox predicted by a mathematical model based on current and historical operating data. The maximum predicted temperature refers to the highest temperature among all predicted values, which is used to assess the highest temperature risk that the gearbox may face.

[0109] Specifically, first, the operating parameters of the gearbox under different operating conditions obtained by formula (3) are substituted into the solving equation of the relationship between the temperature and the operating parameters. This substitution process involves specific mathematical operations, in which the operating parameters include rotational speed, environmental temperature, power and lubrication state parameters, etc.

[0110] Further, the specific values of these parameters need to be set according to the actual monitored data, for example, the rotational speed may be several hundred to several thousand revolutions per minute, the environmental temperature may be from minus several tens of degrees to plus several tens of degrees, the power may be in the order of kilowatts, and the lubrication state parameters involve the viscosity, temperature and pressure of the lubricating oil, etc. Through the substitution of these parameters, the temperature prediction values of the gearbox under different operating conditions can be solved.

[0111] Preferably, to improve the accuracy of temperature prediction, numerical simulation software can be used to assist in solving equations, which can handle complex nonlinear equations.

[0112] More specifically, an automated system can be set up to monitor the operating parameters of the gearbox in real time and automatically input these parameters into the temperature prediction model, thereby achieving real-time temperature prediction.

[0113] Further, as an alternative, machine learning techniques can also be used to predict the temperature change of the gearbox by training the model, which can use historical data to improve the accuracy and response speed of the prediction. Through these detailed operation steps and alternatives, the temperature warning system of the gearbox can be more efficient and reliable.

[0114] In some embodiments, the temperature warning threshold calculation equation is:

[0115] T warn = T nom + k3σ

[0116] where T warn is the warning temperature threshold, T nom is the mean of normal working temperature, and k3 is the safety factor, and σ is the standard deviation of normal working temperature.

[0117] The first paragraph: This embodiment relates to how to determine the temperature warning threshold of the fan gearbox. The temperature warning threshold refers to a temperature value set in the warning system, when the actual temperature of the gearbox exceeds this value, the system will issue a warning signal. The warning temperature threshold here is a key parameter to ensure the safe operation of the gearbox, and the mean of normal working temperature refers to the average temperature of the gearbox under normal operating conditions, and the safety factor is a multiplier factor to increase the warning threshold to ensure safety.

[0118] Specifically, the warning temperature threshold can be calculated by the formula

[0119] T 预警 = T 均值 + σ·k3

[0120] where T 预警 is the warning temperature threshold, T 均值 is the mean of normal working temperature, σ is the standard deviation of normal working temperature, and k3 is a safety factor.

[0121] Further, this safety factor can be set according to the design standards and historical failure data of the gearbox to ensure timely warning when the temperature of the gearbox abnormally rises. The mean and standard deviation of normal working temperature can be obtained by long-term monitoring of the operating data of the gearbox.

[0122] Preferably, to set the early warning temperature threshold more accurately, statistical analysis methods can be used to determine the mean and standard deviation of the normal operating temperature.

[0123] More specifically, the gearbox temperature data under different seasons and load conditions can be analyzed to determine more accurate mean and standard deviation.

[0124] Further, as an alternative, machine learning techniques can also be used to predict the early warning temperature threshold, which can dynamically adjust the early warning threshold according to historical failure data and real-time operation data to adapt to different operating conditions and environmental changes. Through these detailed operation steps and alternatives, the accuracy and response speed of the temperature early warning system can be improved.

[0125] In some embodiments, the failure risk assessment equation is:

[0126]

[0127] where R is the failure risk degree between 0 and 1, k4 is the correction coefficient, T max is the maximum predicted temperature.

[0128] It should be noted that the present embodiment describes how to evaluate the failure risk degree of the fan gearbox. Failure risk assessment is a key step for determining the probability of gearbox failure under certain conditions. Here, the failure risk degree is a value between 0 and 1, representing the probability of gearbox failure, while the correction coefficient is a parameter used to adjust the failure risk assessment results to reflect the complexity of actual operation.

[0129] Specifically, the failure risk degree can be calculated by the formula

[0130]

[0131] where R represents the failure risk degree, a is the correction coefficient, T 预测 is the maximum predicted temperature, T 预警 is the early warning temperature threshold, and T 正常 is the normal operating temperature. The correction coefficient a can be set according to historical failure data and expert experience to ensure the accuracy of the assessment results. The values of T 预测 , T 预警 and T 正常 need to be obtained through actual monitoring and calculation.

[0132] Preferably, to improve the accuracy of the fault risk assessment, a historical fault database can be used to assist in determining the correction coefficient a. More specifically, by analyzing the temperature data and actual fault conditions in historical fault cases, the value of a can be adjusted to be closer to the actual situation.

[0133] Further, as an alternative, a machine learning algorithm can also be used to dynamically adjust the fault risk assessment model, which can predict the fault risk based on real-time data and historical trends, providing more timely warnings. Through these detailed operation steps and alternatives, the reliability and practicality of the fault risk assessment can be enhanced.

[0134] The above-mentioned various embodiments of the present application have the following beneficial effects: The fan gearbox temperature warning method described in the present application can construct a fan gearbox physical model, install multiple sensors at key positions to obtain operating parameters, establish a solving equation of the relationship between temperature and operating parameters based on heat conduction differential equations and energy balance equations, and thus accurately obtain temperature prediction values under different operating conditions. Finally, combined with a warning threshold calculation equation and a fault risk assessment equation, the warning state of the gearbox can be accurately judged and the degree of fault risk can be assessed, effectively preventing faults caused by abnormal temperature, ensuring the safe and stable operation of the fan gearbox, and also providing strong protection for the reliable operation of the entire fan.

[0135] The method can comprehensively consider various factors such as rotational speed, load, lubrication state, and external factors such as environmental temperature, and the established model is more in line with actual operating conditions, making temperature prediction more accurate and enabling timely warnings, facilitating maintenance personnel to take measures in advance such as adjusting operating parameters, scheduling maintenance and repair, reducing downtime caused by faults, improving fan operating efficiency, reducing maintenance costs, prolonging the service life of the fan gearbox, and improving the economy and stability of the entire wind power generation system.

[0136] Further, the storage medium of the embodiments of the present application stores program instructions capable of implementing all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes, or a computer, a server, a mobile phone, a tablet, and other terminal devices.

[0137] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not limited to them; under the idea of the present application, the technical features of the above examples or different examples can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the present application as above. In order to be simple, they are not provided in details; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A model-based temperature warning method for a fan gearbox, characterized in that, It comprises the following steps: Select a fan gearbox with a typical structure, install temperature sensors and working condition sensors at key parts of the gearbox, and build a physical model of the fan gearbox accordingly; Obtain the operating parameters of the gearbox under different working conditions, including speed, load, and lubrication state parameters; Based on the heat conduction differential equation and energy balance equation of the fan gearbox, obtain the solving equation of the relationship between the gearbox temperature and operating parameters; Based on the operating parameters of the gearbox and the solving equation of the relationship between the temperature and operating parameters, obtain the temperature prediction values of the gearbox under different working conditions and select the highest prediction temperature as the maximum prediction temperature; Based on the maximum prediction temperature, the temperature warning threshold calculation equation, and the fault risk assessment equation, obtain the warning state and fault risk degree of the gearbox; The working condition sensors include speed sensors, torque sensors, and oil pressure sensors, including the following three installation positions: Install the speed sensor and torque sensor at the input shaft of the gearbox; Install the oil pressure sensor at the main oil pipe of the gearbox lubrication system; Install the temperature sensor at an appropriate position of the gearbox body; Obtain the operating parameters of the gearbox under different working conditions by the following formula: wherein P is the power, N is the rotational speed, T is the torque, Q is the lubrication flow, , C is a coefficient related to the gearbox structure; The heat conduction differential equation of the fan gearbox is: wherein is the density of the gearbox material, is the specific heat capacity, is the temperature, is the time, is the thermal conductivity, is the internal heat source term; The energy balance equation is: wherein, Qin is the input heat, Qout is the outgoing heat, Qgen is the internally generated heat, Qstorage is the heat storage change; The solving equation of the relationship between the gearbox temperature and operating parameters is: wherein, is the gearbox temperature, is the rotational speed, is the ambient temperature, is the power, is the lubrication state parameter; wherein is a coefficient determined by experiment or theoretical analysis, , , , is an order determined according to model complexity.

2. The model-based temperature warning method for a fan gearbox according to claim 1, wherein, Based on the operating parameters of the gearbox and the solving equation of the relationship between the temperature and operating parameters, obtain the temperature prediction values of the gearbox under different working conditions and select the maximum temperature prediction value, which comprises: Substitute the operating parameters of the gearbox under different working conditions obtained by formula (3) into the solving equation of the relationship between the temperature and operating parameters, solve the equation, obtain the temperature prediction values of the gearbox under different working conditions, and select the highest prediction temperature as the maximum prediction temperature.

3. A model-based temperature warning method for a fan gearbox according to claim 2, characterized in that, The temperature warning threshold calculation equation is: wherein, is a pre-alarm temperature threshold, is a normal operating temperature mean, is a safety factor, is a normal operating temperature standard deviation.

4. The model-based temperature warning method for a fan gearbox according to claim 3, wherein, The fault risk assessment equation is: wherein is the degree of risk of failure, between 0 and 1, is the correction factor, is the maximum predicted temperature.

5. A readable storage medium, characterized by, The storage medium is used to store a computer program, and when the processor executes the computer program, the steps of the model-based fan gearbox temperature warning method according to any one of claims 1-4 are realized.

6. A model-based fan gearbox temperature early warning device, characterized by, The device comprises: A memory for storing a computer program; A processor for executing the computer program to realize the steps of the model-based fan gearbox temperature warning method according to any one of claims 1-4.

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