A pre-warning method and system for a wind turbine yaw caliper replacement device

Abstract

The application relates to the technical field of wind driven generators, and discloses a prewarning method and system for a wind turbine yaw caliper replacement device, which acquires data information of a vibration sensor; carries out feature extraction and standardization processing on the data information to obtain processing data; collects vibration responses of the vibration sensor, calculates modal participation factors of a vibration model according to the vibration responses; compares the numerical values of all the modal participation factors, determines a maximum modal participation factor based on the comparison result, determines a corresponding abnormal vibration judgment model according to the maximum modal participation factor; inputs the feature data into the abnormal vibration judgment model, carries out abnormal detection, and issues a prewarning reminder based on the abnormal detection result; the application can realize abnormal detection of a wind turbine yaw caliper according to vibration data of the wind turbine yaw caliper, can issue a prewarning reminder in real time, can avoid the influence of the wind turbine yaw caliper on the normal work of a wind driven generator, and can guarantee the performance exertion and stable operation of a wind turbine generator.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power generators, in particular to a pre-warning method and system for a wind turbine yaw caliper replacement device. BACKGROUND

[0002] A wind power generator is an electric power device that converts wind energy into mechanical work, and the mechanical work drives the rotation of the rotor, and finally outputs alternating current. A wind power generator generally comprises a wind wheel, a generator (including a device), a yawer (tail wing), a tower, a speed-limiting safety mechanism and an energy storage device. The working principle of a wind power generator is relatively simple. The wind wheel rotates under the action of wind power, which converts the kinetic energy of wind into mechanical energy of the wind wheel shaft. The generator rotates to generate electricity under the driving of the wind wheel shaft. In a broad sense, wind energy is also solar energy, so the wind power generator can also be regarded as a heat energy utilization generator with the sun as the heat source and the atmosphere as the working medium.

[0003] The number of yaw calipers in a wind power generator is generally large. The yaw caliper provides damping for the movement of the nacelle when the wind power generator is running normally to generate electricity and when the yaw cable is released. Therefore, the yaw caliper is directly related to the performance of the wind turbine and the stability of the operation. The existing wind turbine yaw caliper vibration monitoring usually requires the staff to detect the yaw caliper regularly, and to evaluate whether the yaw caliper is in an abnormal working state based on the sound at the position of the yaw caliper when yawing. Since the existing method relies too much on the operating experience of the staff, it lacks online automatic evaluation of the working state of the yaw caliper and alarm in the abnormal state, which leads to the discovery of the abnormal working state of the yaw caliper not being timely, and brings difficulties to the operation and maintenance of the unit.

[0004] Therefore, how to provide a pre-warning method and system for a wind turbine yaw caliper replacement device is a technical problem to be solved at present. SUMMARY

[0005] The embodiment of the present application provides a pre-warning method and system for a wind turbine yaw caliper replacement device, which solves the technical problems that the real-time state of the wind turbine yaw caliper cannot be monitored and real-time pre-warning cannot be given to the wind turbine yaw caliper in the prior art.

[0006] In order to achieve the above-mentioned purpose, the present application provides a pre-warning method for a wind turbine yaw caliper replacement device, which comprises:

[0007] Obtaining the working state of a vibration sensor pre-deployed at the wind turbine yaw caliper, and obtaining the data information of the vibration sensor when the vibration sensor is in an open state;

[0008] Performing feature extraction and standardization processing on the data information of the vibration sensor to obtain the processing data of the vibration sensor;

[0009] collecting the vibration response of the vibration sensor, and calculating modal participation factors of a pre-constructed vibration model according to the vibration response of the vibration sensor;

[0010] comparing the numerical sizes of all the modal participation factors, determining a maximum modal participation factor based on the comparison result, and determining a corresponding abnormal vibration judgment model according to the maximum modal participation factor;

[0011] inputting the feature data of the vibration sensor into the abnormal vibration judgment model, performing abnormal detection on the fan yaw clamp based on the abnormal vibration model, and issuing a warning reminder based on the abnormal detection result.

[0012] In one embodiment, when the feature extraction and standardization processing of the data information of the vibration sensor are performed to obtain the processed data of the vibration sensor, the method comprises:

[0013] extracting the time-frequency features of the data information of the vibration sensor in chronological order to determine the feature data of the vibration sensor;

[0014] standardizing the feature data of the vibration sensor to obtain the processed data of the vibration sensor; wherein

[0015] the feature data of the vibration sensor is standardized according to the following formula:

[0016]

[0017] wherein, S is the processed data of the vibration sensor, x is the feature data of the vibration sensor, x mean is the mean of the feature data of the vibration sensor, and x std is the variance of the feature data of the vibration sensor.

[0018] In one embodiment, when the vibration response of the vibration sensor is collected, and the modal participation factors of a pre-constructed vibration model are calculated according to the vibration response of the vibration sensor, the method comprises:

[0019] constructing a vibration response matrix according to the vibration response of the vibration sensor, and establishing an autocorrelation function matrix based on the vibration response matrix;

[0020] establishing a Hankel matrix according to the autocorrelation function matrix;

[0021] establishing a mode shape information matrix corresponding to the vibration model;

[0022] establishing a vector equation of the vibration model participation according to the Hankel matrix and the mode shape information matrix;

[0023] According to the vector equation, the autocorrelation function matrix and the scalar expression corresponding to the autocorrelation function matrix, a scalar of the vibration model participation is calculated and obtained;

[0024] According to the scalar of the vibration model participation and the participation factor calculation formula of the vibration model, a modal participation factor of the pre-constructed vibration model is calculated and obtained.

[0025] In one of the embodiments, when the feature data of the vibration sensor is input into the abnormal vibration judgment model, and the fan yaw clamp is abnormally detected based on the abnormal vibration model, it includes:

[0026] The feature data of the vibration sensor is taken as input, and the abnormal value of the fan yaw clamp is output based on the abnormal vibration model;

[0027] According to the relationship between the abnormal value of the fan yaw clamp and the preset abnormal value, the fan yaw clamp is abnormally detected,

[0028] When the abnormal value of the fan yaw clamp is less than the preset abnormal value, it is judged that the fan yaw clamp is in a non-abnormal state;

[0029] When the abnormal value of the fan yaw clamp is greater than or equal to the preset abnormal value, it is judged that the fan yaw clamp is in an abnormal state, and a pre-warning is issued.

[0030] In one of the embodiments, when it is judged that the fan yaw clamp is in an abnormal state and a pre-warning is issued, it includes:

[0031] The vibration speed of the vibration sensor and the acceleration of the vibration sensor are obtained;

[0032] The vibration amplitude of the vibration sensor is determined based on the vibration speed of the vibration sensor and the acceleration of the vibration sensor;

[0033] According to the relationship between the vibration amplitude of the vibration sensor and the preset vibration amplitude, different pre-warnings are issued;

[0034] When the vibration amplitude of the vibration sensor is less than the preset vibration amplitude, a maintenance pre-warning is issued;

[0035] When the vibration amplitude of the vibration sensor is greater than or equal to the preset vibration amplitude, a replacement pre-warning is issued.

[0036] In order to achieve the above purpose, the present application provides a pre-warning system for a fan yaw clamp replacement device, the system includes:

[0037] The acquisition module is configured to acquire a working state of a vibration sensor pre-deployed at a yaw clamp of a fan, and acquire data information of the vibration sensor when the vibration sensor is in an open state.

[0038] The processing module is configured to perform feature extraction and standardization processing on the data information of the vibration sensor, and acquire processing data of the vibration sensor.

[0039] The calculation module is configured to collect a vibration response of the vibration sensor, and calculate a modal participation factor of a pre-constructed vibration model according to the vibration response of the vibration sensor.

[0040] The determination module is configured to compare numerical values of all modal participation factors, determine a maximum modal participation factor based on a comparison result, and determine a corresponding abnormal vibration judgment model according to the maximum modal participation factor.

[0041] The early warning module is configured to input the feature data of the vibration sensor into the abnormal vibration judgment model, perform abnormal detection on the yaw clamp of the fan based on the abnormal vibration model, and issue a pre-warning reminder based on an abnormal detection result.

[0042] In one of the embodiments, the processing module is specifically configured to:

[0043] The processing module is configured to extract time-frequency features of the data information of the vibration sensor in a time sequence, and determine feature data of the vibration sensor.

[0044] The processing module is configured to perform standardization processing on the feature data of the vibration sensor, and acquire processing data of the vibration sensor; wherein,

[0045] The processing module is configured to perform standardization processing on the feature data of the vibration sensor according to the following formula:

[0046]

[0047] wherein, the S is the processing data of the vibration sensor, x is the feature data of the vibration sensor, x mean is a mean value of the feature data of the vibration sensor, and x std is a variance of the feature data of the vibration sensor.

[0048] In one of the embodiments, the calculation module is specifically configured to:

[0049] The calculation module is configured to construct a vibration response matrix according to the vibration response of the vibration sensor, and establish an autocorrelation function matrix based on the vibration response matrix.

[0050] The computing module is configured to establish a Hankel matrix according to the autocorrelation function matrix;

[0051] The computing module is configured to establish a mode shape information matrix corresponding to the vibration model;

[0052] The computing module is configured to establish a vector equation participated by the vibration model according to the Hankel matrix and the mode shape information matrix;

[0053] The computing module is configured to calculate a scalar participated by the vibration model according to the vector equation, the autocorrelation function matrix, and a scalar expression corresponding to the autocorrelation function matrix;

[0054] The computing module is configured to calculate a modal participation factor of a pre-constructed vibration model according to the scalar participated by the vibration model and a participation factor calculation formula of the vibration model.

[0055] In one of the embodiments, the anomaly detection module is specifically configured to:

[0056] The anomaly detection module is configured to take the feature data of the vibration sensor as input and output an anomaly value of the fan yaw clamp based on the anomaly vibration model;

[0057] The anomaly detection module is configured to perform anomaly detection on the fan yaw clamp according to a relationship between the anomaly value of the fan yaw clamp and a preset anomaly value,

[0058] The anomaly detection module is configured to determine that the fan yaw clamp is in a non-anomalous state when the anomaly value of the fan yaw clamp is less than the preset anomaly value.

[0059] The anomaly detection module is configured to determine that the fan yaw clamp is in an anomalous state and issue a pre-warning reminder when the anomaly value of the fan yaw clamp is greater than or equal to the preset anomaly value.

[0060] In one of the embodiments, the anomaly detection module is specifically configured to:

[0061] The anomaly detection module is configured to obtain a vibration speed of the vibration sensor and an acceleration of the vibration sensor;

[0062] The anomaly detection module is configured to determine a vibration amplitude of the vibration sensor based on the vibration speed of the vibration sensor and the acceleration of the vibration sensor;

[0063] The anomaly detection module is configured to issue different pre-warning reminders according to a relationship between the vibration amplitude of the vibration sensor and a preset vibration amplitude;

[0064] The abnormality detection module is used for issuing a maintenance early warning prompt when the vibration amplitude of the vibration sensor is less than the preset vibration amplitude.

[0065] The abnormality detection module is used for issuing a replacement early warning prompt when the vibration amplitude of the vibration sensor is greater than or equal to the preset vibration amplitude.

[0066] The application provides an early warning method and system for a wind turbine yaw caliper replacement device, which has the following beneficial effects compared with the prior art.

[0067] The application discloses an early warning method and system for a wind turbine yaw caliper replacement device, obtains data information of a vibration sensor, performs feature extraction and standardization processing on the data information to obtain processed data, collects vibration response of the vibration sensor, calculates modal participation factors of a vibration model according to the vibration response, compares numerical values of all the modal participation factors, determines a maximum modal participation factor based on a comparison result, determines a corresponding abnormal vibration judgment model according to the maximum modal participation factor, inputs feature data into the abnormal vibration judgment model, performs abnormality detection, and issues an early warning prompt based on an abnormality detection result. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 A flowchart of an early warning method for a wind turbine yaw caliper replacement device in an embodiment of the application is shown.

[0069] Figure 2 A structural diagram of an early warning system for a wind turbine yaw caliper replacement device in an embodiment of the application is shown. DETAILED DESCRIPTION

[0070] The specific embodiments of the application are described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are used to illustrate the application, but are not used to limit the scope of the application.

[0071] In the description of the present application, it should be understood that the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and therefore cannot be understood as indicating or implying that the device or element 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.

[0072] The terms "first", "second", etc. are used only for the purpose of description, and should not be understood as indicating or implying relative importance or implying the number of the technical features indicated. Therefore, the features defined as "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0073] In the description of the present application, it should be noted that, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through intermediate medium, or internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0074] The following is a description of the preferred embodiments of the present application in conjunction with the accompanying drawings.

[0075] As shown in Figure 1 The embodiments of the present application disclose a warning method for a fan yaw caliper replacement device, which comprises the following steps:

[0076] S110: acquiring the working state of a vibration sensor pre-deployed at the fan yaw caliper, and acquiring the data information of the vibration sensor when the vibration sensor is in an open state;

[0077] In the present embodiment, the working state of the vibration sensor is divided into an open state and a closed state.

[0078] S120: performing feature extraction and standardization processing on the data information of the vibration sensor to acquire the processing data of the vibration sensor;

[0079] In some embodiments of the present application, when performing feature extraction and standardization processing on the data information of the vibration sensor to acquire the processing data of the vibration sensor, it comprises:

[0080] extracting the time-frequency features of the data information of the vibration sensor in time sequence to determine the feature data of the vibration sensor;

[0081] performing standardization processing on the feature data of the vibration sensor to acquire the processing data of the vibration sensor; wherein,

[0082] The feature data of the vibration sensor is standardized according to the following formula:

[0083]

[0084] Wherein, the S is the processing data of the vibration sensor, x is the characteristic data of the vibration sensor, x mean is the mean value of the characteristic data of the vibration sensor, x std is the variance of the characteristic data of the vibration sensor.

[0085] The beneficial effects of the above technical solutions are: the present application obtains the processing data of the vibration sensor by standardizing the characteristic data of the vibration sensor, which can lay a foundation for the subsequent warning of the fan yaw clamp.

[0086] S130: collecting the vibration response of the vibration sensor, and calculating the modal participation factor of the pre-constructed vibration model according to the vibration response of the vibration sensor;

[0087] In some embodiments of the present application, when the vibration response of the vibration sensor is collected, and the modal participation factor of the pre-constructed vibration model is calculated according to the vibration response of the vibration sensor, it includes:

[0088] According to the vibration response of the vibration sensor, a vibration response matrix is constructed, and an autocorrelation function matrix is established based on the vibration response matrix;

[0089] According to the autocorrelation function matrix, a Hankel matrix is established;

[0090] The mode shape information matrix corresponding to the vibration model is established;

[0091] According to the Hankel matrix and the mode shape information matrix, a vector equation participated by the vibration model is established;

[0092] According to the vector equation, the autocorrelation function matrix and the scalar expression corresponding to the autocorrelation function matrix, the scalar participated by the vibration model is calculated and obtained;

[0093] According to the scalar participated by the vibration model and the participation factor calculation formula of the vibration model, the modal participation factor of the pre-constructed vibration model is calculated and obtained.

[0094] The beneficial effects of the above technical solutions are: the present application calculates and obtains the modal participation factor of the pre-constructed vibration model according to the scalar participated by the vibration model and the participation factor calculation formula of the vibration model. The present application can ensure the accurate selection of the model by calculating and obtaining the modal participation factor of the pre-constructed vibration model, further ensuring the judgment accuracy of the fan yaw clamp and improving the abnormal detection accuracy.

[0095] S140: comparing the numerical values of all modal participation factors, determining a largest modal participation factor based on the comparison result, and determining the corresponding abnormal vibration judgment model according to the largest modal participation factor;

[0096] S150: input the feature data of the vibration sensor into the abnormal vibration judgment model, perform abnormal detection on the fan yaw clamp based on the abnormal vibration model, and issue a warning prompt based on the abnormal detection result.

[0097] In some embodiments of the present application, when the feature data of the vibration sensor is input into the abnormal vibration judgment model and the abnormal vibration model is used to detect the abnormality of the fan yaw clamp, the following steps are included:

[0098] The feature data of the vibration sensor is input, and the abnormal value of the fan yaw clamp is output based on the abnormal vibration model;

[0099] The fan yaw clamp is detected based on the relationship between the abnormal value of the fan yaw clamp and the preset abnormal value,

[0100] When the abnormal value of the fan yaw clamp is less than the preset abnormal value, it is determined that the fan yaw clamp is in a non-abnormal state;

[0101] When the abnormal value of the fan yaw clamp is greater than or equal to the preset abnormal value, it is determined that the fan yaw clamp is in an abnormal state, and a warning prompt is issued.

[0102] The beneficial effects of the above technical solutions are: the present application detects the abnormality of the fan yaw clamp according to the relationship between the abnormal value of the fan yaw clamp and the preset abnormal value. The present application can detect the abnormality of the fan yaw clamp according to the vibration data of the fan yaw clamp, and issue a real-time warning prompt to avoid the influence of the fan yaw clamp on the normal operation of the wind turbine, and ensure the performance and stable operation of the wind turbine.

[0103] In some embodiments of the present application, when it is determined that the fan yaw clamp is in an abnormal state and a warning prompt is issued, the following steps are included:

[0104] Obtain the vibration speed of the vibration sensor and the acceleration of the vibration sensor;

[0105] Determine the vibration amplitude of the vibration sensor based on the vibration speed of the vibration sensor and the acceleration of the vibration sensor;

[0106] Issue different warning prompts according to the relationship between the vibration amplitude of the vibration sensor and the preset vibration amplitude;

[0107] When the vibration amplitude of the vibration sensor is less than the preset vibration amplitude, a maintenance warning prompt is issued;

[0108] When the vibration amplitude of the vibration sensor is greater than or equal to the preset vibration amplitude, a replacement early warning is issued.

[0109] The beneficial effects of the above technical solution are: the maintenance early warning and the replacement early warning are respectively issued according to the relationship between the vibration amplitude of the vibration sensor and the preset vibration amplitude, different early warnings can be realized, the worker can be reminded to maintain or replace the fan yaw clamp, and the work efficiency is improved.

[0110] In order to further illustrate the technical idea of the application, the technical solution of the application will be described in combination with a specific application scenario.

[0111] Correspondingly, as shown in Figure 2 The application also provides an early warning system for a fan yaw clamp replacement device, the system comprising:

[0112] An acquisition module is configured to acquire the working state of a vibration sensor pre-deployed at a fan yaw clamp, and when the vibration sensor is in an open state, acquire data information of the vibration sensor;

[0113] A processing module is configured to perform feature extraction and standardization processing on the data information of the vibration sensor, and acquire processing data of the vibration sensor;

[0114] A calculation module is configured to collect the vibration response of the vibration sensor, and calculate the modal participation factor of a pre-constructed vibration model according to the vibration response of the vibration sensor;

[0115] A determination module is configured to compare the numerical values of all modal participation factors, determine a maximum modal participation factor based on the comparison result, and determine a corresponding abnormal vibration judgment model according to the maximum modal participation factor;

[0116] An early warning module is configured to input the feature data of the vibration sensor into the abnormal vibration judgment model, perform abnormal detection on the fan yaw clamp based on the abnormal vibration model, and issue an early warning based on the abnormal detection result.

[0117] In some embodiments of the application, the processing module is specifically configured to:

[0118] The processing module is configured to extract the time-frequency features of the data information of the vibration sensor in time sequence, and determine the feature data of the vibration sensor;

[0119] The processing module is configured to perform standardization processing on the feature data of the vibration sensor, and acquire the processing data of the vibration sensor; wherein

[0120] The processing module is configured to normalize the feature data of the vibration sensor according to the following formula:

[0121]

[0122] wherein, S is the processed data of the vibration sensor, x is the feature data of the vibration sensor, x mean is the mean value of the feature data of the vibration sensor, x std is the variance of the feature data of the vibration sensor.

[0123] In some embodiments of the present application, the computing module is specifically configured to:

[0124] The computing module is configured to construct a vibration response matrix according to the vibration response of the vibration sensor, and establish an autocorrelation function matrix based on the vibration response matrix;

[0125] The computing module is configured to establish a Hankel matrix according to the autocorrelation function matrix;

[0126] The computing module is configured to establish a mode shape information matrix corresponding to the vibration model;

[0127] The computing module is configured to establish a vector equation in which the vibration model participates according to the Hankel matrix and the mode shape information matrix;

[0128] The computing module is configured to calculate a scalar in which the vibration model participates according to the vector equation, the autocorrelation function matrix, and a scalar expression corresponding to the autocorrelation function matrix;

[0129] The computing module is configured to calculate a modal participation factor of a pre-constructed vibration model according to the scalar in which the vibration model participates and a participation factor calculation formula of the vibration model.

[0130] In some embodiments of the present application, the anomaly detection module is specifically configured to:

[0131] The anomaly detection module is configured to take the feature data of the vibration sensor as input, and output an abnormal value of the fan yaw clamp based on the abnormal vibration model;

[0132] The anomaly detection module is configured to perform anomaly detection on the fan yaw clamp according to a relationship between the abnormal value of the fan yaw clamp and a preset abnormal value,

[0133] The anomaly detection module is configured to determine that the fan yaw clamp is in a non-anomalous state when the abnormal value of the fan yaw clamp is less than the preset abnormal value;

[0134] The abnormality detection module is configured to determine that the fan yaw clamp is in an abnormal state and issue a pre-warning prompt when the abnormal value of the fan yaw clamp is greater than or equal to the preset abnormal value.

[0135] In some embodiments of the present application, the abnormality detection module is specifically configured to:

[0136] The abnormality detection module is configured to obtain the vibration speed of the vibration sensor and the acceleration of the vibration sensor.

[0137] The abnormality detection module is configured to determine the vibration amplitude of the vibration sensor based on the vibration speed of the vibration sensor and the acceleration of the vibration sensor.

[0138] The abnormality detection module is configured to issue different pre-warning prompts according to the relationship between the vibration amplitude of the vibration sensor and a preset vibration amplitude.

[0139] The abnormality detection module is configured to issue a maintenance pre-warning prompt when the vibration amplitude of the vibration sensor is less than the preset vibration amplitude.

[0140] The abnormality detection module is configured to issue a replacement pre-warning prompt when the vibration amplitude of the vibration sensor is greater than or equal to the preset vibration amplitude.

[0141] In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0142] Although the present application has been described with reference to the embodiments above, various modifications can be made to it and components thereof can be replaced with equivalents without departing from the scope of the present application. In particular, each feature in the embodiments disclosed by the present application can be combined with any other features in any manner, unless there is a structural conflict. The combinations of these features are not all described in the present specification only for the purpose of saving space and resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

[0143] Those skilled in the art can understand that the above are only preferred embodiments of the present application and are not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent replacements of some technical features. Any modifications, equivalent replacements or improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A pre-warning method for a wind turbine yaw caliper replacement device, characterized in that, The method includes: The operating status of the vibration sensor pre-deployed at the yaw caliper of the wind turbine is obtained, and when the vibration sensor is in the on state, the data information of the vibration sensor is obtained. Feature extraction and standardization are performed on the data information from the vibration sensor to obtain the processed data of the vibration sensor; The vibration response of the vibration sensor is collected, and the modal participation factor of the pre-constructed vibration model is calculated based on the vibration response of the vibration sensor. The numerical values ​​of all modal participation factors are compared. Based on the comparison results, the largest modal participation factor is determined, and the corresponding abnormal vibration judgment model is determined according to the largest modal participation factor. The characteristic data of the vibration sensor is input into the abnormal vibration judgment model, and the abnormal vibration judgment model is used to detect the abnormality of the wind turbine yaw caliper. Based on the abnormality detection result, an early warning is issued. When acquiring the vibration response of the vibration sensor and calculating the modal participation factor of the pre-built vibration model based on the vibration response of the vibration sensor, the process includes: A vibration response matrix is ​​constructed based on the vibration response of the vibration sensor, and an autocorrelation function matrix is ​​established based on the vibration response matrix. Establish the Hankel matrix based on the autocorrelation function matrix; Establish the mode shape information matrix corresponding to the vibration model; Based on the Hankel matrix and the mode shape information matrix, establish the vector equations involved in the vibration model; Based on the vector equation, the autocorrelation function matrix, and the scalar expression corresponding to the autocorrelation function matrix, the scalars involved in the vibration model are calculated. Based on the scalars involved in the vibration model and the calculation formula of the participation factor of the vibration model, the modal participation factor of the pre-constructed vibration model is calculated. When performing feature extraction and standardization on the data information of the vibration sensor to obtain the processed data of the vibration sensor, the process includes: Extract the time-frequency characteristics of the vibration sensor data information according to the time series, and determine the characteristic data of the vibration sensor; The characteristic data of the vibration sensor are standardized to obtain the processed data of the vibration sensor; wherein, The characteristic data of the vibration sensor are standardized according to the following formula: ； Wherein, S represents the processed data of the vibration sensor, and x represents the characteristic data of the vibration sensor. mean x is the mean of the characteristic data of the vibration sensor. std Let V be the variance of the characteristic data of the vibration sensor.

2. The early warning method for the wind turbine yaw caliper replacement device according to claim 1, characterized in that, When inputting the characteristic data of the vibration sensor into the abnormal vibration judgment model, and performing abnormal detection on the wind turbine yaw caliper based on the abnormal vibration judgment model, the process includes: The characteristic data of the vibration sensor is used as input, and the abnormal value of the wind turbine yaw caliper is output based on the abnormal vibration judgment model. Anomaly detection of the wind turbine yaw caliper is performed based on the relationship between the abnormal value of the yaw caliper and the preset abnormal value. When the abnormal value of the wind turbine yaw caliper is less than the preset abnormal value, it is determined that the wind turbine yaw caliper is in a non-abnormal state. When the abnormal value of the wind turbine yaw caliper is greater than or equal to the preset abnormal value, the wind turbine yaw caliper is determined to be in an abnormal state, and a warning reminder is issued.

3. The early warning method for the wind turbine yaw caliper replacement device according to claim 2, characterized in that, When determining that the wind turbine yaw caliper is in an abnormal state and issuing a warning, the following steps are included: The vibration velocity and acceleration of the vibration sensor are obtained. The vibration amplitude of the vibration sensor is determined based on the vibration velocity and acceleration of the vibration sensor. Different warning alerts are issued based on the relationship between the vibration amplitude of the vibration sensor and the preset vibration amplitude; When the vibration amplitude of the vibration sensor is less than the preset vibration amplitude, a maintenance warning reminder is issued; When the vibration amplitude of the vibration sensor is greater than or equal to the preset vibration amplitude, a replacement warning is issued.

4. A warning system for a wind turbine yaw caliper replacement device, characterized in that, The system includes: The acquisition module is used to acquire the working status of the vibration sensor pre-deployed at the yaw caliper of the wind turbine, and to acquire the data information of the vibration sensor when the vibration sensor is in the on state; The processing module is used to perform feature extraction and standardization on the data information of the vibration sensor to obtain the processed data of the vibration sensor. The calculation module is used to acquire the vibration response of the vibration sensor and calculate the modal participation factor of the pre-built vibration model based on the vibration response of the vibration sensor. The determination module is used to compare the numerical values ​​of all modal participation factors, determine the largest modal participation factor based on the comparison results, and determine the corresponding abnormal vibration judgment model based on the largest modal participation factor. The early warning module is used to input the characteristic data of the vibration sensor into the abnormal vibration judgment model, perform abnormal detection on the wind turbine yaw caliper based on the abnormal vibration judgment model, and issue an early warning reminder based on the abnormal detection result; The calculation module is used to construct a vibration response matrix based on the vibration response of the vibration sensor, and to establish an autocorrelation function matrix based on the vibration response matrix; The calculation module is used to establish the Hankel matrix based on the autocorrelation function matrix; The calculation module is used to establish the mode shape information matrix corresponding to the vibration model; The calculation module is used to establish the vector equations in which the vibration model participates based on the Hankel matrix and the mode shape information matrix; The calculation module is used to calculate the scalars involved in the vibration model based on the vector equation, the autocorrelation function matrix, and the scalar expression corresponding to the autocorrelation function matrix. The calculation module is used to calculate the modal participation factor of the pre-constructed vibration model based on the scalars involved in the vibration model and the participation factor calculation formula of the vibration model. The processing module is used to extract the time-frequency characteristics of the data information of the vibration sensor according to the time series, and to determine the characteristic data of the vibration sensor. The processing module is used to standardize the feature data of the vibration sensor to obtain the processed data of the vibration sensor. The processing module is used to standardize the feature data of the vibration sensor according to the following formula: ； Wherein, S represents the processed data of the vibration sensor, and x represents the characteristic data of the vibration sensor. mean x is the mean of the characteristic data of the vibration sensor. std Let V be the variance of the characteristic data of the vibration sensor.

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