Fan shaft system fault early warning method based on dynamic game optimization

Abstract

The invention discloses a fan shaft system fault early warning method based on dynamic game optimization. The method comprises the following steps: S1, collecting a monitoring data set of the running state of a fan shaft system; s2, generating a feature vector set of the operation state of the fan shaft system; s3, based on the feature vector set, combining a causal reasoning method to construct a fan shaft system fault risk assessment model; s4, constructing a dynamic game optimization model of fan shaft system fault early warning based on the fan shaft system fault risk assessment model; s5, updating the fan shaft system fault risk assessment model; s6, generating a real-time fan shaft system fault early warning signal based on the updated fan shaft system fault risk assessment model; and S7, transmitting the fault early warning signal to the intelligent operation and maintenance platform. According to the method, through an asymmetric strengthening mechanism and dynamic causal weight updating, the key driving factors of the shaft system fault are accurately identified, and the influence degree of each factor on the fault risk is quantified.

Description

Technical Field

[0001] The present invention relates to the technical field of fans, and in particular to a fan shaft fault early warning method based on dynamic game optimization. Background Art

[0002] With the rapid development of wind power generation technology, the operational safety and economy of wind turbines have received increasing attention. As an important component of wind turbines, the operating status of the wind turbine shaft system is directly related to the reliability and service life of the entire machine. However, the wind turbine shaft system is exposed to complex dynamic loads and changing environments for a long time and is easily affected by multiple factors such as vibration fatigue, mechanical wear and environmental changes, resulting in frequent failures of key components such as bearings, couplings and gears.

[0003] At present, the mainstream methods for fan shaft fault warning mostly rely on static data analysis or monitoring technology based on a single variable. Static data analysis methods usually evaluate the operating status of the fan shaft through vibration analysis, temperature monitoring or simple empirical models. Some vibration monitoring methods can use acceleration sensors to collect bearing vibration signals and extract fault characteristics through spectrum analysis. At the same time, temperature monitoring technology is also widely used to judge the operating status of bearings or gears. However, traditional technologies have significant limitations in practical applications:

[0004] On the one hand, static data analysis methods cannot fully reflect the complex interactive behaviors of the fan shaft system under dynamic operating conditions. For example, changes in wind speed, ambient humidity and temperature difference will have a significant impact on the operating state of the shaft system, but traditional methods find it difficult to include these dynamic factors in the evaluation scope, resulting in insufficient accuracy and adaptability of the early warning results.

[0005] On the other hand, most existing fault warning systems lack a closed-loop feedback mechanism and are unable to effectively use the warning results to guide wind farm operation and maintenance decisions. This isolated warning mode leads to unreasonable allocation of operation and maintenance resources, low maintenance timeliness, and may even cause additional downtime losses due to incorrect priority judgments.

[0006] In summary, the existing technology has significant deficiencies in dynamic environment adaptability, warning accuracy and operation and maintenance decision support. There is an urgent need for a new fault warning method that can combine dynamic game optimization and causal reasoning to improve the accuracy, intelligence and operation and maintenance efficiency of fan shaft fault prediction. Summary of the invention

[0007] One object of the present invention is to propose a fan shaft system fault early warning method based on dynamic game optimization. The present invention accurately identifies the key driving factors of shaft system failures and quantifies the influence of each factor on the failure risk through an asymmetric reinforcement mechanism and dynamic causal weight update.

[0008] A fan shaft fault early warning method based on dynamic game optimization according to an embodiment of the present invention comprises the following steps:

[0009] S1. Collect monitoring data set of fan shaft operation status;

[0010] S2. Preprocess the monitoring data set to generate a feature vector set of the fan shaft system operating status;

[0011] S3. Based on the feature vector set and combined with the causal reasoning method, a fan shaft fault risk assessment model is constructed;

[0012] S4. Construct a dynamic game optimization model for fan shaft failure warning based on the fan shaft failure risk assessment model;

[0013] S5. Calibrate the parameters of the dynamic game optimization model using historical operation data and real-time monitoring data, dynamically adjust the weights of each causal variable in the fan shaft failure risk assessment model based on the game optimization results, and update the fan shaft failure risk assessment model;

[0014] S6. Generate a real-time fan shaft failure warning signal based on the updated fan shaft failure risk assessment model;

[0015] S7. The fault warning signal is transmitted to the intelligent operation and maintenance platform, and the historical operation and maintenance data and real-time monitoring data in the intelligent operation and maintenance platform are combined to generate repair priority recommendations and resource allocation plans for the wind turbine shaft system.

[0016] Optionally, the S1 includes the following steps:

[0017] S11. Using a vibration sensor to collect a vibration signal data set V(t) of the fan shaft system, the vibration signal data set is obtained by recording the real-time vibration amplitude, frequency and its change trend under the operation state of the fan bearing;

[0018] S12. Using a temperature sensor to collect an operating temperature signal data set T(t) of the fan shaft system, wherein the operating temperature signal data set is obtained by recording temperature changes at key parts of the fan shaft system;

[0019] S13. Using environmental monitoring equipment to collect environmental load parameter data set L(t) of the fan shaft system, the environmental load parameters include wind speed, air density and humidity;

[0020] S14. Synchronize the vibration signal data set V(t), the operating temperature signal data set T(t) and the environmental load parameter data set L(t) to construct a monitoring data set D(t) of the fan shaft operating status:

[0021] D(t)={V(t),T(t),L(t)}.

[0022] Optionally, S2 includes the following steps:

[0023] S21. Perform data denoising on the monitoring data set D(t), decompose the vibration signal data set and the operating temperature signal data set using a wavelet transform method, retain the characteristic frequency band and filter out high-frequency noise, and generate a denoised signal data set;

[0024] S22. Remove abnormal data from the signal data set after noise reduction, detect abnormal values ​​by setting a threshold range, remove data points that deviate from the statistical range, and generate a cleaned signal data set;

[0025] S23. Perform feature extraction on the cleaned signal data set, use fast Fourier transform to extract the characteristic frequency of the vibration signal from the vibration signal data set, extract the temperature change rate characteristics of key parts from the operating temperature signal data set, and extract the statistical characteristics of wind speed and humidity changes from the environmental load parameter data set to generate a feature set F(t):

[0026] F(t)={f v (t),f t (t),f l (t)};

[0027] Among them, f v (t) represents the vibration signal characteristics, f t (t) represents the temperature signal characteristics, f l (t) represents the environmental load characteristics;

[0028] S24. Perform multi-dimensional parameter normalization on the feature set F(t), use a standardization method to make the feature data dimensionless, and generate a feature vector set X(t) of the fan shaft system operating state after normalization:

[0029] X(t)={x1(t),x2(t),…,x n (t)};

[0030] Among them, x n (t) represents the nth normalized eigenvector at time t, and n is the total number of eigenvectors.

[0031] Optionally, S3 includes the following steps:

[0032] S31. Define the multi-layer nested causal reasoning variable set C based on the characteristic vector set X(t) of the fan shaft system operating state m (t):

[0033] C m (t) = {C1(t), C2(t), C3(t)};

[0034] Among them, C1(t) represents the first-layer state variables, including the bearing vibration signal and operating temperature signal of the fan shaft system, C2(t) represents the second-layer external environmental variables, including environmental load parameters, and C3(t represents the third-layer potential fault variables, including the signal response characteristics of each fault mode in the fan shaft system;

[0035] S32. Using multi-layer causal reasoning structure to construct dynamic nested causal graph G m (C m ,R m ), the nested structure of the dynamic nested causal graph dynamically describes the temporal interaction process between the wind turbine shaft system operating status, environmental conditions and potential failure modes:

[0036] G m (C m ,R m )=(C m ,R m );

[0037] Among them, G m represents a multi-layer causal variable set, R m Represents a multi-layer causal relationship set, where each layer of relationship R ij Describe the causal interaction from layer i to layer j;

[0038] S33. Dynamically update the relationship weights in the dynamic nested causal graph and introduce the time-sensitive causal weight function w through the change of wind turbine operation status. ij (t):

[0039] w ij (t)=f(ΔX(t),ΔE(t),T c );

[0040] Among them, ΔX(t) represents the change of the fan state, ΔE(t) represents the change of the environmental conditions, T c is a specific time window, and f represents a nonlinear function fitted by historical data;

[0041] S34. The causal relationship in the dynamic nested causal graph is asymmetrically strengthened by introducing the impulse response function φ ij (t) Describe the asymmetric impact of unidirectional variable changes on the overall system:

[0042]

[0043] Among them, β ij is the causal path influence coefficient, P(C j ∣C i ) is the conditional probability, describing the dependent variable C j With the independent variable C iProbability distribution of changes;

[0044] S35. Define a risk assessment model for fan shaft failure based on a dynamic nested causal graph

[0045]

[0046] in, It represents a multi-level fault risk function, and comprehensively evaluates the impact of the wind turbine shaft system operating status on potential faults through dynamic nested causal relationships.

[0047] Optionally, S4 includes the following steps:

[0048] S41. A dynamic game optimization model Γ(t) for early warning of shaft faults of a wind turbine is established. The dynamic game optimization model is composed of a shaft operating state of the wind turbine, an environmental load condition, a strategy space, and a multi-level fault risk function:

[0049]

[0050] Among them, P s Indicates the fan shaft running status, P e Indicates environmental load conditions, S s (t) represents the strategy set of the wind turbine shaft system operation status at time t, S e (t) represents the strategy set of the environmental load condition party at time t;

[0051] S42. According to the dynamic game optimization model Γ(t), the profit functions of the two parties are defined for the wind turbine shaft system operation state party and the environmental load condition party, and the dynamic goals of both parties are constructed:

[0052]

[0053] Among them, U s (t) represents the revenue function of the fan shaft system operating state, c s (t) represents the operation adjustment cost paid by the fan shaft system operating state at time t to reduce the failure risk, η s is the magnification factor of the strategy benefit of the other party, Ω s (S s (t)) represents the operation status of the fan shaft system in strategy S s (t) The additional benefit of shafting performance, U e (t) represents the benefit function of the environmental load condition, Indicates that in the time window [tT c ,t] is the integral of the influence of failure risk on the change of environmental load parameter E(τ), ζ(·) is the mapping function that weighs the risk sensitivity, represents the partial derivative of the risk function with respect to the change of environmental load;

[0054] S43. Based on the profit function of both parties, a dynamic game optimization problem is established. In a given strategy space S s (t)×S e (t), the fan shaft system operating state and the environmental load condition are allowed to minimize the failure risk through game interaction. The dynamic optimization problem is characterized as follows:

[0055]

[0056] At the same time, the following Nash equilibrium conditions are met:

[0057]

[0058] in, It represents the optimal strategy of the wind turbine shaft system operation state and the environmental load condition when the game equilibrium is reached at time t;

[0059] S44. By monitoring the failure risk function Real-time changes, adjust the strategy space S s (t),S e (t), and based on the benefit function U of both parties s (t),U e (t) is iterated to make strategy corrections, so that the dynamic game optimization model can dynamically migrate to a new strategy equilibrium point when the external environment changes or the operating state of the wind turbine shaft system changes abnormally:

[0060]

[0061] Among them, s (·) and Ψ e (·) represent the mapping functions for dynamically updating the wind turbine shaft system operation status and environmental load condition strategies, and Represents the gradient information for the profit function;

[0062] S45. Iterate and solve the dynamic game optimization problem to obtain the optimal strategy combination of the wind turbine shaft system operating state and environmental load conditions. The optimal strategy combination corresponds to the failure risk level As a result of the assessment to minimize the risk of shaft failure in wind turbines:

[0063]

[0064] in, Represents the failure risk value under the optimal strategy combination.

[0065] Optionally, S5 includes the following steps:

[0066] S51. Arrange the historical operation data and real-time monitoring data D(t) of the dynamic game optimization model Γ(t) into a time series form;

[0067] S52. Based on historical operation data and real-time monitoring data, the profit function parameter γ in the dynamic game optimization model is s ,α e And the causal weight function w ij (t) Perform parameter calibration and predict risk value by minimizing historical data The actual risk value Error calibration parameters;

[0068] S53. According to the dynamic game optimization results and the calibrated parameters, the weights of the causal variables in the fan shaft failure risk assessment model are adjusted. The adjustment rules are as follows:

[0069]

[0070] Among them, λ1 is the adjustment step coefficient, and They represent the effect of the profit function on the causal variable C j The partial derivative of

[0071] S54. Based on the calibrated parameters and dynamically adjusted causal variable weights, the fan shaft system fault risk assessment model is updated, and the calibration and update process of S51-S54 is repeated to iteratively optimize the dynamic game optimization model and the fan shaft system fault risk assessment model until the error between the predicted fault risk value and the actual observed risk value converges to the preset threshold ε.

[0072] Optionally, the S6 includes the following steps:

[0073] S61. Based on the updated fan shaft fault risk assessment model, define real-time fault risk classification rules, divide the fault risk into normal operation state, early warning state and abnormal alarm state, and fit the classification rules in combination with historical operation data and actual alarm conditions;

[0074] S62. Using the real-time monitoring data of the fan shaft system, the current fault risk level is evaluated based on the fan shaft system fault risk assessment model, and the potential fault characteristics in the monitoring data are identified and quantified through dynamically updated risk assessment parameters;

[0075] S63. Generate a fault status signal of the fan shaft system according to the real-time assessed fault risk value and classification rules, the fault status signal including a normal operating status signal, an early warning signal and an abnormal alarm signal;

[0076] S64. Optimize the generated fault status signal in combination with the game strategy results of the dynamic game optimization model, and adjust the sensitivity of the warning signal trigger and the response priority of the abnormal alarm signal according to the operating conditions;

[0077] S65. Output the optimized fault warning signal to the wind turbine operation and maintenance system in real time, and the output content includes the fault status, current risk level and recommended response strategy.

[0078] The beneficial effects of the present invention are:

[0079] (1) The present invention introduces a dynamic game optimization model to establish a game relationship between the wind turbine shaft system operating state and the environmental load condition, thereby realizing dynamic optimization of multi-party interaction strategies. It can adjust model parameters in real time and dynamically respond to changes in the external environment such as wind speed and humidity and fluctuations in the shaft system operating state, thereby significantly improving the adaptability and accuracy of fault warning. By introducing the profit function and Nash equilibrium conditions, the model can still reach the optimal state under complex operating conditions.

[0080] (2) The present invention uses causal reasoning methods to construct a multi-layer nested causal graph to deeply explore the causal relationship between the shaft system operating status, environmental conditions and potential fault signals. Compared with the traditional linear model that can only capture the correlation between variables, the present invention accurately identifies the key driving factors of shaft system failures through an asymmetric reinforcement mechanism and dynamic causal weight update, and quantifies the influence of each factor on the failure risk.

[0081] (3) The present invention transforms the dynamic game optimization results and real-time risk assessment results into targeted operation and maintenance suggestions by constructing an intelligent closed-loop operation and maintenance system, including fault priority sorting, maintenance resource allocation and dynamic early warning strategy adjustment. Compared with the isolated early warning process in the existing method, the present invention realizes closed-loop management of the entire life cycle based on the early warning of the fan shaft system fault, which greatly improves the maintenance efficiency and the utilization rate of operation and maintenance resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0083] Figure 1 The present invention provides a flow chart of a fan shaft fault early warning method based on dynamic game optimization. DETAILED DESCRIPTION

[0084] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0085] refer to Figure 1 , a fan shaft fault early warning method based on dynamic game optimization, comprising the following steps:

[0086] S1. Collect monitoring data set of fan shaft operation status;

[0087] S2. Preprocess the monitoring data set to generate a feature vector set of the fan shaft system operating status;

[0088] S3. Based on the feature vector set and combined with the causal reasoning method, a fan shaft fault risk assessment model is constructed;

[0089] S4. Construct a dynamic game optimization model for fan shaft failure warning based on the fan shaft failure risk assessment model;

[0090] S5. Calibrate the parameters of the dynamic game optimization model using historical operation data and real-time monitoring data, dynamically adjust the weights of each causal variable in the fan shaft system failure risk assessment model based on the game optimization results, and update the fan shaft system failure risk assessment model;

[0091] S6. Generate a real-time fan shaft failure warning signal based on the updated fan shaft failure risk assessment model;

[0092] S7. The fault warning signal is transmitted to the intelligent operation and maintenance platform, and the historical operation and maintenance data and real-time monitoring data in the intelligent operation and maintenance platform are combined to generate repair priority recommendations and resource allocation plans for the wind turbine shaft system.

[0093] In this implementation, S1 includes the following steps:

[0094] S11. Use a vibration sensor to collect a vibration signal data set V(t) of the fan shaft system, where the vibration signal data set is obtained by recording the real-time vibration amplitude, frequency and its change trend under the operating state of the fan bearing;

[0095] S12. Using a temperature sensor to collect an operating temperature signal data set T(t) of the fan shaft system, the operating temperature signal data set is obtained by recording temperature changes at key parts of the fan shaft system;

[0096] S13. Use environmental monitoring equipment to collect the environmental load parameter data set L(t) of the fan shaft system, where the environmental load parameters include wind speed, air density and humidity;

[0097] S14. Synchronize the vibration signal data set V(t), the operating temperature signal data set T(t) and the environmental load parameter data set L(t) to construct a monitoring data set D(t) of the fan shaft operating status:

[0098] D(t)={V(t),T(t),L(t)}.

[0099] In this implementation, S2 includes the following steps:

[0100] S21. Perform data denoising on the monitoring data set D(t), decompose the vibration signal data set and the operating temperature signal data set using a wavelet transform method, retain the characteristic frequency band and filter out high-frequency noise, and generate a denoised signal data set;

[0101] S22. Remove abnormal data from the signal data set after noise reduction, detect abnormal values ​​by setting a threshold range, remove data points that deviate from the statistical range, and generate a cleaned signal data set;

[0102] S23. Perform feature extraction on the cleaned signal data set, use fast Fourier transform to extract the characteristic frequency of the vibration signal from the vibration signal data set, extract the temperature change rate characteristics of key parts from the operating temperature signal data set, and extract the statistical characteristics of wind speed and humidity changes from the environmental load parameter data set to generate a feature set F(t):

[0103] F(t)={f v (t),f t (t),f l (t)};

[0104] Among them, f v (t) represents the vibration signal characteristics, f t (t) represents the temperature signal characteristics, f l (t) represents the environmental load characteristics;

[0105] S24. Perform multi-dimensional parameter normalization on the feature set F(t), use a standardization method to make the feature data dimensionless, and generate a feature vector set X(t) of the fan shaft system operating state after normalization:

[0106] X(t)={x1(t),x2(t),…,x n (t)};

[0107] Among them, x n (t) represents the nth normalized eigenvector at time t, and n is the total number of eigenvectors.

[0108] In this implementation, S3 includes the following steps:

[0109] S31. Define the multi-layer nested causal reasoning variable set C based on the characteristic vector set X(t) of the fan shaft system operating state m (t):

[0110] C m (t) = {C1(t), C2(t), C3(t)};

[0111] Among them, C1(t) represents the first-layer state variables, including the bearing vibration signal and operating temperature signal of the fan shaft system, C2(t) represents the second-layer external environmental variables, including environmental load parameters, and C3(t represents the third-layer potential fault variables, including the signal response characteristics of each fault mode in the fan shaft system;

[0112] S32. Using multi-layer causal reasoning structure to construct dynamic nested causal graph G m (C m ,R m ), the nested structure of the dynamic nested causal graph dynamically describes the temporal interaction process between the wind turbine shaft system operating status, environmental conditions and potential failure modes:

[0113] G m (C m ,R m )=(C m ,R m );

[0114] Among them, G m represents a multi-layer causal variable set, R m Represents a multi-layer causal relationship set, where each layer of relationship R ij Describe the causal interaction from layer i to layer j;

[0115] S33. Dynamically update the relationship weights in the dynamic nested causal graph and introduce the time-sensitive causal weight function w through the change of wind turbine operation status. ij (t):

[0116] w ij (t)=f(ΔX(t),ΔE(t),T c );

[0117] Among them, ΔX(t) represents the change of the fan state, ΔE(t) represents the change of the environmental conditions, T c is a specific time window, and f represents a nonlinear function fitted by historical data;

[0118] S34. The causal relationship in the dynamic nested causal graph is asymmetrically strengthened by introducing the impulse response function φ ij (t) Describe the asymmetric impact of unidirectional variable changes on the overall system:

[0119]

[0120] Among them, β ij is the causal path influence coefficient, P(C j ∣C i ) is the conditional probability, describing the dependent variable C j With the independent variable Ci Probability distribution of changes;

[0121] S35. Define a risk assessment model for fan shaft failure based on a dynamic nested causal graph

[0122]

[0123] in, It represents a multi-level fault risk function, and comprehensively evaluates the impact of the wind turbine shaft system operating status on potential faults through dynamic nested causal relationships.

[0124] In this implementation, S4 includes the following steps:

[0125] S41. A dynamic game optimization model Γ(t) for wind turbine shaft fault warning is established. The dynamic game optimization model is composed of the wind turbine shaft operation state, environmental load condition, strategy space and multi-level fault risk function:

[0126]

[0127] Among them, P s Indicates the fan shaft running status, P e Indicates environmental load conditions, S s (t) represents the strategy set of the wind turbine shaft system operation status at time t, S e (t) represents the strategy set of the environmental load condition party at time t;

[0128] S42. According to the dynamic game optimization model Γ(t), the profit functions of the two parties are defined for the wind turbine shaft system operation state party and the environmental load condition party, and the dynamic goals of both parties are constructed:

[0129]

[0130] Among them, U s (t) represents the revenue function of the fan shaft system operating state, c s (t) represents the operation adjustment cost paid by the fan shaft system operating state at time t to reduce the failure risk, η s is the magnification factor of the strategy benefit of the other party, Ω s (S s (t)) represents the operation status of the fan shaft system in strategy S s (t) The additional benefit of shafting performance, U e (t) represents the benefit function of the environmental load condition, Indicates that in the time window [tT c,t] is the integral of the influence of failure risk on the change of environmental load parameter E(τ), ζ(·) is the mapping function that weighs the risk sensitivity, represents the partial derivative of the risk function with respect to the change of environmental load;

[0131] S43. Based on the profit function of both parties, a dynamic game optimization problem is established. In a given strategy space S s (t)×S e (t), the fan shaft system operating state and the environmental load condition are allowed to minimize the failure risk through game interaction. The dynamic optimization problem is characterized as follows:

[0132]

[0133] At the same time, the following Nash equilibrium conditions are met:

[0134]

[0135] in, It represents the optimal strategy of the wind turbine shaft system operation state and the environmental load condition when the game equilibrium is reached at time t;

[0136] S44. By monitoring the failure risk function Real-time changes, adjust the strategy space S s (t),S e (t), and based on the benefit function U of both parties s (t),U e (t) is iterated to make strategy corrections, so that the dynamic game optimization model can dynamically migrate to a new strategy equilibrium point when the external environment changes or the operating state of the wind turbine shaft system changes abnormally:

[0137]

[0138] Among them, s (·) and Ψ e (·) represent the mapping functions for dynamically updating the wind turbine shaft system operation status and environmental load condition strategies, and Represents the gradient information for the profit function;

[0139] S45. Iterate and solve the dynamic game optimization problem to obtain the optimal strategy combination of the wind turbine shaft system operating state and environmental load conditions. The optimal strategy combination corresponds to the failure risk level As a result of the assessment to minimize the risk of shaft failure in wind turbines:

[0140]

[0141] in, Represents the failure risk value under the optimal strategy combination.

[0142] In this implementation, S5 includes the following steps:

[0143] S51. Arrange the historical operation data and real-time monitoring data D(t) of the dynamic game optimization model Γ(t) into a time series form;

[0144] S52. Based on historical operation data and real-time monitoring data, the profit function parameter γ in the dynamic game optimization model is s ,α e And the causal weight function w ij (t) Perform parameter calibration and predict risk value by minimizing historical data The actual risk value Error calibration parameters;

[0145] S53. According to the dynamic game optimization results and the calibrated parameters, the weights of the causal variables in the fan shaft failure risk assessment model are adjusted. The adjustment rules are as follows:

[0146]

[0147] Among them, λ1 is the adjustment step coefficient, and They represent the effect of the profit function on the causal variable C j The partial derivative of

[0148] S54. Based on the calibrated parameters and dynamically adjusted causal variable weights, the fan shaft system fault risk assessment model is updated, and the calibration and update process of S51-S54 is repeated to iteratively optimize the dynamic game optimization model and the fan shaft system fault risk assessment model until the error between the predicted fault risk value and the actual observed risk value converges to the preset threshold ε.

[0149] In this implementation, S6 includes the following steps:

[0150] S61. Based on the updated fan shaft fault risk assessment model, define real-time fault risk classification rules, divide the fault risk into normal operation state, early warning state and abnormal alarm state, and fit the classification rules in combination with historical operation data and actual alarm conditions;

[0151] S62. Using the real-time monitoring data of the fan shaft system, the current fault risk level is evaluated based on the fan shaft system fault risk assessment model, and the potential fault characteristics in the monitoring data are identified and quantified through dynamically updated risk assessment parameters;

[0152] S63. Generate a fault status signal of the fan shaft system according to the real-time assessed fault risk value and classification rules, the fault status signal including a normal operating status signal, an early warning signal and an abnormal alarm signal;

[0153] S64. Optimize the generated fault status signal in combination with the game strategy results of the dynamic game optimization model, and adjust the sensitivity of the warning signal trigger and the response priority of the abnormal alarm signal according to the operating conditions;

[0154] S65. Output the optimized fault warning signal to the wind turbine operation and maintenance system in real time, and the output content includes the fault status, current risk level and recommended response strategy.

[0155] Embodiment 1:

[0156] In the actual operation of a coastal wind farm, the shaft system of a wind turbine numbered W-35 was monitored from July 2024 to September 2024 to verify the actual effect of the present invention. The W-35 wind turbine is located in the center of the wind farm and is affected by wind speed changes and humid sea breeze on a daily basis. Its operating state is complex and has a high risk of failure.

[0157] On August 15, 2024, the system collected real-time monitoring data of the W-35 fan, in which the average value of the main shaft vibration signal increased from the previous 0.15m / s 2 Significantly increased to 0.26m / s 2 The vibration spectrum characteristics showed an obvious enhancement of low-frequency components. At the same time, the gearbox temperature slowly increased from the conventional 65°C to 78°C. In addition, environmental load monitoring showed that the wind speed in the area increased from 12m / s to 16m / s within 24 hours from August 14 to August 15, accompanied by an increase in humidity from 70% to 88%.

[0158] The dynamic game optimization model evaluated the above data in real time, and combined with the causal reasoning model to generate an assessment result of a fault risk value of 0.82 (higher than the warning threshold of 0.8). Further analysis by the model showed that the change in the low-frequency component of the main shaft vibration signal was determined to be a potential feature of the main bearing abnormality, and there was a significant causal relationship between the increase in gearbox temperature and the increase in humidity, which may lead to an increased risk of poor shaft lubrication.

[0159] At 10:00 am on August 16, 2024, the system automatically generated an early warning signal and recorded the following information:

[0160] Fan No.: W-35

[0161] Trigger time: 10:00 on August 16, 2024

[0162] Warning level: Early warning

[0163] Risk Assessment Value: 0.82

[0164] Abnormal characteristics: The low frequency of the spindle vibration signal is enhanced, the gearbox temperature is increased, and the ambient humidity is high.

[0165] The warning signal was simultaneously transmitted to the wind farm operation and maintenance management platform, which automatically recommended the corresponding maintenance strategy: check the main bearing status of the W-35 wind turbine, add lubricating oil, and monitor the temperature and humidity trends of the gearbox. According to the suggestions, the operation and maintenance team carried out maintenance inspections on the W-35 wind turbine at 16:00 that afternoon. The inspection results showed that the inner ring of the main bearing was worn and cracks appeared, and the quality of the lubricating oil was affected by humidity, but the overall operating status had not deteriorated.

[0166] The maintenance personnel repaired the main bearing online and replaced the lubricating oil. The total maintenance time was about 4 hours. The fan downtime was only from 16:00 to 20:00 on the same day. The maintenance cost was RMB 75,000.

[0167] In contrast, another wind turbine W-42 that did not adopt the method of the present invention suffered a serious main bearing failure on September 10, 2024 under similar operating conditions. After the failure occurred, the traditional early warning method only issued an abnormal alarm signal when the main bearing temperature rose to 95°C. The maintenance results showed that the main bearing of the W-42 wind turbine was completely damaged and had to be replaced. The maintenance time was as long as 3 days and the cost was as high as RMB 420,000.

[0168] It can be seen from this embodiment that the method of the present invention successfully identified the potential failure risk of the W-35 fan and achieved early warning, avoiding the high maintenance costs and long downtime losses caused by serious damage to the main bearing. The traditional method failed to provide effective warning under similar circumstances, resulting in serious consequences of the failure. The method of the present invention significantly improves the accuracy and timeliness of the fan shaft system fault warning, and has a high application value in actual scenarios.

[0169] The present invention introduces a dynamic game optimization model, establishes a game relationship between the wind turbine shaft system operating state and the environmental load condition, realizes the dynamic optimization of multi-party interaction strategies, can adjust model parameters in real time, dynamically respond to changes in the external environment of wind speed and humidity and fluctuations in the shaft system operating state, significantly improves the adaptability and accuracy of fault warning, and introduces the profit function and Nash equilibrium conditions to enable the model to reach the optimal state under complex operating conditions.

[0170] The present invention uses causal reasoning methods to construct a multi-layer nested causal graph to deeply explore the causal relationship between the shaft system operating status, environmental conditions and potential fault signals. Compared with the traditional linear model that can only capture the correlation between variables, it accurately identifies the key driving factors of shaft system failures through asymmetric reinforcement mechanism and dynamic causal weight update, and quantifies the influence of each factor on the failure risk.

[0171] The present invention transforms dynamic game optimization results and real-time risk assessment results into targeted operation and maintenance suggestions by constructing an intelligent closed-loop operation and maintenance system, including fault priority sorting, maintenance resource allocation and dynamic early warning strategy adjustment. Compared with the isolated early warning process in the existing method, the present invention realizes closed-loop management of the entire life cycle based on the early warning of the fan shaft system fault, which greatly improves the maintenance efficiency and the utilization rate of operation and maintenance resources.

[0172] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A fan shaft fault early warning method based on dynamic game optimization, characterized in that: The steps include: S1. Collect monitoring data set of fan shaft operation status; S2. Preprocess the monitoring data set to generate a feature vector set of the fan shaft system operating status; S3. Based on the feature vector set and combined with the causal reasoning method, a fan shaft fault risk assessment model is constructed; S4. Construct a dynamic game optimization model for fan shaft failure warning based on the fan shaft failure risk assessment model; S5. Calibrate the parameters of the dynamic game optimization model using historical operation data and real-time monitoring data, dynamically adjust the weights of each causal variable in the fan shaft failure risk assessment model based on the game optimization results, and update the fan shaft failure risk assessment model; S6. Generate a real-time fan shaft failure warning signal based on the updated fan shaft failure risk assessment model; S7. The fault warning signal is transmitted to the intelligent operation and maintenance platform, and the historical operation and maintenance data and real-time monitoring data in the intelligent operation and maintenance platform are combined to generate repair priority recommendations and resource allocation plans for the wind turbine shaft system.

2. The method for early warning of fan shaft fault based on dynamic game optimization according to claim 1 is characterized in that: The S1 comprises the following steps: S11. Using a vibration sensor to collect a vibration signal data set V(t) of the fan shaft system, the vibration signal data set is obtained by recording the real-time vibration amplitude, frequency and its change trend under the operation state of the fan bearing; S12. Using a temperature sensor to collect an operating temperature signal data set T(t) of the fan shaft system, wherein the operating temperature signal data set is obtained by recording temperature changes at key parts of the fan shaft system; S13. Using environmental monitoring equipment to collect environmental load parameter data set L(t) of the fan shaft system, the environmental load parameters include wind speed, air density and humidity; S14. Synchronize the vibration signal data set V(t), the operating temperature signal data set T(t) and the environmental load parameter data set L(t) to construct a monitoring data set D(t) of the fan shaft operating status: D(t)={V(t),T(t),L(t)}.

3. The method for early warning of fan shaft fault based on dynamic game optimization according to claim 1 is characterized in that: The S2 comprises the following steps: S21. Perform data denoising on the monitoring data set D(t), decompose the vibration signal data set and the operating temperature signal data set using a wavelet transform method, retain the characteristic frequency band and filter out high-frequency noise, and generate a denoised signal data set; S22. Remove abnormal data from the signal data set after noise reduction, detect abnormal values ​​by setting a threshold range, remove data points that deviate from the statistical range, and generate a cleaned signal data set; S23. Perform feature extraction on the cleaned signal data set, use fast Fourier transform to extract the characteristic frequency of the vibration signal from the vibration signal data set, extract the temperature change rate characteristics of key parts from the operating temperature signal data set, and extract the statistical characteristics of wind speed and humidity changes from the environmental load parameter data set to generate a feature set F(t): F(t)={f v (t),f t (t),f l (t)}; Among them, f v (t) represents the vibration signal characteristics, f t (t) represents the temperature signal characteristics, f l (t) represents the environmental load characteristics; S24. Perform multi-dimensional parameter normalization on the feature set F(t), use a standardization method to make the feature data dimensionless, and generate a feature vector set X(t) of the fan shaft system operating state after normalization: X(t)={x1(t),x2(t),…,x n (t)}; Among them, x n (t) represents the nth normalized eigenvector at time t, and n is the total number of eigenvectors.

4. The method for early warning of fan shaft fault based on dynamic game optimization according to claim 1 is characterized in that: The S3 comprises the following steps: S31. Define the multi-layer nested causal reasoning variable set C based on the characteristic vector set X(t) of the fan shaft system operating state m (t): C m (t)={C1(t),C2(t),C3(t)}; Among them, C1(t) represents the first-layer state variables, including the bearing vibration signal and operating temperature signal of the fan shaft system, C2(t) represents the second-layer external environmental variables, including environmental load parameters, and C3(t represents the third-layer potential fault variables, including the signal response characteristics of each fault mode in the fan shaft system; S32. Using multi-layer causal reasoning structure to construct dynamic nested causal graph G m (C m ,R m ), the nested structure of the dynamic nested causal graph dynamically describes the temporal interaction process between the wind turbine shaft system operating status, environmental conditions and potential failure modes: G m (C m ,R m )=(C m ,R m ); Among them, G m represents a multi-layer causal variable set, R m Represents a multi-layer causal relationship set, where each layer of relationship R ij Describe the causal interaction from layer i to layer j; S33. Dynamically update the relationship weights in the dynamic nested causal graph and introduce the time-sensitive causal weight function w through the change of wind turbine operation status. ij (t): w ij (t)=f(ΔX(t),ΔE(t),T c ); Among them, ΔX(t) represents the change of the fan state, ΔE(t) represents the change of the environmental conditions, T c is a specific time window, and f represents a nonlinear function fitted by historical data; S34. The causal relationship in the dynamic nested causal graph is asymmetrically strengthened by introducing the impulse response function φ ij (t) Describe the asymmetric impact of unidirectional variable changes on the overall system: Among them, β ij is the causal path influence coefficient, P(C j ∣C i ) is the conditional probability, describing the dependent variable C j With the independent variable C i Probability distribution of changes; S35. Define a risk assessment model for fan shaft failure based on a dynamic nested causal graph in, It represents a multi-level fault risk function, and comprehensively evaluates the impact of the wind turbine shaft system operating status on potential faults through dynamic nested causal relationships.

5. The method for early warning of fan shaft fault based on dynamic game optimization according to claim 1 is characterized in that: The S4 comprises the following steps: S41. Establish a dynamic game optimization model Γ(t) for wind turbine shaft fault warning, wherein the dynamic game optimization model is composed of the wind turbine shaft operation state, environmental load condition, strategy space, and multi-level fault risk function: Among them, P s Indicates the fan shaft running status, P e Indicates environmental load conditions, S s (t) represents the strategy set of the wind turbine shaft system operation status at time t, S e (t) represents the strategy set of the environmental load condition party at time t; S42. According to the dynamic game optimization model Γ(t), the profit functions of the two parties are defined for the wind turbine shaft system operation state party and the environmental load condition party, and the dynamic goals of both parties are constructed: Among them, U s (t) represents the revenue function of the fan shaft system operating state, c s (t) represents the operation adjustment cost paid by the fan shaft system operating state at time t to reduce the failure risk, η s is the magnification factor of the strategy benefit of the other party, Ω s (S s (t)) represents the operation status of the fan shaft system in strategy S s (t) The additional benefit of shafting performance, U e (t) represents the benefit function of the environmental load condition, Indicates that in the time window [tT c ,t] is the integral of the influence of failure risk on the change of environmental load parameter E(τ), ζ(·) is the mapping function that weighs the risk sensitivity, represents the partial derivative of the risk function with respect to the change of environmental load; S43. Based on the profit function of both parties, a dynamic game optimization problem is established. In a given strategy space S s (t)×S e (t), the fan shaft system operating state and the environmental load condition are allowed to minimize the failure risk through game interaction. The dynamic optimization problem is characterized as follows: At the same time, the following Nash equilibrium conditions are met: in, It represents the optimal strategy of the wind turbine shaft system operation state and the environmental load condition when the game equilibrium is reached at time t; S44. By monitoring the failure risk function Real-time changes, adjust the strategy space S s (t),S e (t), and based on the benefit function U of both parties s (t),U e (t) is iterated to make strategy corrections, so that the dynamic game optimization model can dynamically migrate to a new strategy equilibrium point when the external environment changes or the operating state of the wind turbine shaft system changes abnormally: Among them, s (·) and Ψ e (·) represent the mapping functions for dynamically updating the wind turbine shaft system operation status and environmental load condition strategies, and Represents the gradient information for the profit function; S45. Iterate and solve the dynamic game optimization problem to obtain the optimal strategy combination of the wind turbine shaft system operating state and environmental load conditions. The optimal strategy combination corresponds to the failure risk level As a result of the assessment to minimize the risk of shaft failure in wind turbines: in, Represents the failure risk value under the optimal strategy combination.

6. The method for early warning of fan shaft fault based on dynamic game optimization according to claim 1 is characterized in that: The S5 comprises the following steps: S51. Arrange the historical operation data and real-time monitoring data D(t) of the dynamic game optimization model Γ(t) into a time series form; S52. Based on historical operation data and real-time monitoring data, the profit function parameter γ in the dynamic game optimization model is s ,α e And the causal weight function w ij (t) Perform parameter calibration and predict risk value by minimizing historical data The actual risk value Error calibration parameters; S53. According to the dynamic game optimization results and the calibrated parameters, the weights of the causal variables in the fan shaft failure risk assessment model are adjusted. The adjustment rules are as follows: Among them, λ1 is the adjustment step coefficient, and They represent the effect of the profit function on the causal variable C j The partial derivative of S54. Based on the calibrated parameters and dynamically adjusted causal variable weights, the fan shaft system fault risk assessment model is updated, and the calibration and update process of S51-S54 is repeated to iteratively optimize the dynamic game optimization model and the fan shaft system fault risk assessment model until the error between the predicted fault risk value and the actual observed risk value converges to the preset threshold ε.

7. The method for early warning of fan shaft fault based on dynamic game optimization according to claim 1 is characterized in that: The S6 comprises the following steps: S61. Based on the updated fan shaft fault risk assessment model, define real-time fault risk classification rules, divide the fault risk into normal operation state, early warning state and abnormal alarm state, and fit the classification rules in combination with historical operation data and actual alarm conditions; S62. Using the real-time monitoring data of the fan shaft system, the current fault risk level is evaluated based on the fan shaft system fault risk assessment model, and the potential fault characteristics in the monitoring data are identified and quantified through dynamically updated risk assessment parameters; S63. Generate a fault status signal of the fan shaft system according to the real-time assessed fault risk value and classification rules, the fault status signal including a normal operating status signal, an early warning signal and an abnormal alarm signal; S64. Optimize the generated fault status signal in combination with the game strategy results of the dynamic game optimization model, and adjust the sensitivity of the warning signal trigger and the response priority of the abnormal alarm signal according to the operating conditions; S65. Output the optimized fault warning signal to the wind turbine operation and maintenance system in real time, and the output content includes the fault status, current risk level and recommended response strategy.

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