A fault monitoring system and method for fan status

Abstract

The present invention relates to the field of fault detection technology, and in particular to a fault monitoring system and method for wind turbine status. A fault monitoring system for wind turbine status includes: a data acquisition module, which collects the tower flange sensing data and the blade flange sensing data after adjusting the acquisition time interval according to the acquisition time change formula; an interpolation processing module, which obtains the flange gap sensor data at each flange in the wind turbine, and performs interpolation processing based on the flange gap sensor data to obtain the gap radar map at each flange; a status monitoring module, which monitors and judges the wind turbine status according to the gap radar map at each flange. The present invention generates a gap radar map based on the flange sensor data of the wind turbine tower and blades by adopting interpolation processing technology, and can accurately reflect the status of the wind turbine by visualizing the distribution change of the flange gap through the radar map, providing a reliable basis for status evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and in particular to a fault monitoring system and method for a fan state. Background Art

[0002] With the development of the wind power industry, the reliable operation and maintenance cost control of wind turbines are becoming increasingly important. Wind turbines are susceptible to wind loads, fatigue stress and environmental factors during long-term operation, causing gap changes at the connection between the tower and blade flange components, which in turn leads to potential faults. At present, most wind turbine status monitoring systems mainly rely on the single collection and statistical analysis of sensor data, and fail to provide multi-level fault identification methods, especially the distribution changes of the flange gap between the wind turbine tower and blades cannot be accurately monitored. At the same time, most of the existing data collection methods use periodic collection methods. When each collection time is in an abnormal environmental state, the accumulated abnormal data collected will increase significantly. For example, data collection is performed when ice is covered at night, but the melting process works normally during the day, and the collection frequency cannot be adjusted in time, which reduces the real-time and accuracy of data collection. Summary of the invention

[0003] In order to overcome the shortcomings of insufficient monitoring of flange gap changes and lack of multi-level fault identification means, the present invention provides a fault monitoring system and method oriented to the status of a fan.

[0004] Technical solution: A fault monitoring system for fan status, including:

[0005] The data acquisition module collects the tower flange sensing data and the blade flange sensing data after adjusting the acquisition time interval according to the acquisition time variation formula;

[0006] An interpolation processing module is used to obtain flange gap sensor data at each flange in the fan, and to perform interpolation processing based on the flange gap sensor data to obtain a gap radar map at each flange;

[0007] A status monitoring module monitors and determines the status of the fan according to the clearance radar diagram at each flange;

[0008] The tower anomaly detection module processes the tower flange sensing data and the tower flange interpolation data using the tower anomaly detection formula to obtain the tower anomaly index, and performs detection according to the tower anomaly index;

[0009] A cumulative graph acquisition module, used to collect the tower flange sensing data and the blade flange sensing data after adjusting the collection time interval according to the collection time variation formula, and obtain a flange clearance cumulative graph;

[0010] The blade abnormality data acquisition module is used to acquire the blade abnormal flange clearance data according to the flange clearance accumulation diagram;

[0011] The blade abnormality detection module obtains the abnormality index of the blade according to the blade abnormality detection formula, and performs detection according to the abnormality index of the blade;

[0012] The alarm reminder module issues an alarm based on the detection results of the abnormal index of the tower and the abnormal index of the blade.

[0013] Preferably, the data acquisition module collects tower flange sensing data and blade flange sensing data after adjusting the collection time interval according to the collection time change formula, including: evenly setting a first preset number of flange gap sensors at each flange connection of the tower to obtain tower flange sensing data; wherein, flange gap sensors are arranged between two adjacent flanges; and flange gap sensors at the same floor height are evenly distributed circumferentially; and a second preset number of flange gap sensors are evenly set at each blade flange connection to obtain blade flange sensing data.

[0014] Preferably, the interpolation processing module obtains the gap sensor data at each flange in the wind turbine, and performs interpolation processing based on the gap sensor data to obtain the gap radar map at each flange, including: using a linear interpolation formula to interpolate the tower flange sensing data and the blade flange sensing data to obtain tower flange interpolation data and blade flange interpolation data, and according to the tower flange sensing data, the tower flange interpolation data, the blade flange sensing data and the blade flange interpolation data, respectively obtain the tower flange gap radar map and the blade flange gap radar map, wherein the linear interpolation formula is:

[0015] ;

[0016] Where x1 and x2 are the positions of adjacent sensors, y1 and y2 are the gap values ​​measured by adjacent sensors, x is the target position to be interpolated, and y is the gap estimate of the interpolated position.

[0017] Preferably, the tower flange sensing data and the blade flange sensing data are interpolated using a linear interpolation formula to obtain tower flange interpolation data and blade flange interpolation data, including: taking the bolt position of each flange as the target position to be interpolated, and taking the number of bolts of each flange as the number to be interpolated.

[0018] Preferably, the state monitoring module monitors and determines the state of the wind turbine according to the clearance radar diagrams at the flanges, including: detecting and determining the state of the wind turbine according to the clearance radar diagrams of the tower flanges at different heights at the same time;

[0019] If the gap values ​​measured by all sensors on the tower flange at the same height are relatively consistent, continue monitoring;

[0020] If the sensor gap values ​​at the same height position on the tower flange show different gap distributions or inconsistencies, and the inconsistencies have different directional characteristics or size characteristics on the tower flanges at adjacent storey heights, the alarm reminder module issues an alarm message.

[0021] Preferably, the tower anomaly detection module uses a tower anomaly detection formula to process the tower flange sensing data and the tower flange interpolation data to obtain the tower anomaly index, including: performing data cleaning on the tower flange sensing data and the tower flange interpolation data to obtain the tower cleaning data, and inputting the data into the tower anomaly detection formula to obtain the tower anomaly index, wherein the tower anomaly detection formula is:

[0022] ;

[0023] Among them, W is the abnormal index of the tower, is the adjustment factor, P A Cleaning data for the tower, B A It is the preset standard tower flange clearance data.

[0024] Preferably, the data acquisition module, after adjusting the acquisition time interval according to the acquisition time variation formula, collects the tower flange sensing data and the blade flange sensing data, and obtains the flange clearance accumulation diagram, including: collecting the tower flange sensing data and the blade flange sensing data according to the dynamic acquisition time obtained by the acquisition time variation formula, and obtaining the flange clearance accumulation diagram, wherein the acquisition time variation formula is:

[0025] ;

[0026] Among them, T new is the dynamic acquisition time; T is the original acquisition time interval; R is a random variable with a value range of [-1,1]; ΔT is the variable amplitude of the acquisition time interval.

[0027] Preferably, the dynamic collection time obtained according to the collection time change formula is used to collect the tower flange sensing data and the blade flange sensing data, and obtain a flange clearance accumulation diagram, including: pre-processing the tower flange sensing data and the blade flange sensing data within a first preset time period to remove abnormal values ​​and fill in missing values, generating a flange clearance accumulation diagram, and taking the clearance data greater than the abnormal threshold as the blade abnormal flange clearance data.

[0028] Preferably, the blade abnormality detection module obtains the abnormality index of the blade according to the blade abnormality detection formula, including: obtaining the abnormality index of the blade according to the blade abnormal deformation degree data, the blade abnormal surface temperature data, the blade abnormal flange clearance data, the blade abnormal load change data, the blade material limit stress data and the blade material shear strength data according to the blade abnormality detection formula, wherein the blade abnormality detection formula is:

[0029] ;

[0030] Where S is the blade abnormality index, A is the blade deformation degree data, B is the blade surface temperature data, C is the blade flange clearance data, E is the blade load change data, D is the blade material limit stress data, and F is the blade material shear strength data, where ω, , μ, ρ, σ, and τ are preset adjustment factors.

[0031] A fault monitoring method for a fan state comprises the following steps:

[0032] S1: After adjusting the collection time interval according to the collection time change formula, collect the tower flange sensing data and the blade flange sensing data;

[0033] S2: acquiring flange gap sensor data at each flange in the fan, and performing interpolation processing based on the flange gap sensor data to obtain a gap radar map at each flange;

[0034] S3: monitoring and judging the status of the fan according to the clearance radar diagrams at the flanges;

[0035] S4: using a tower anomaly detection formula to process tower flange sensing data and tower flange interpolation data to obtain a tower anomaly index;

[0036] S5: After adjusting the collection time interval according to the collection time variation formula, the tower flange sensing data and the blade flange sensing data are collected, and a flange clearance accumulation diagram is obtained;

[0037] S6: Obtain the abnormality index of the blade according to the blade abnormality detection formula;

[0038] S7: Issue an alarm based on the abnormal index of the tower or the abnormal index of the blade.

[0039] Beneficial effects: The present invention dynamically adjusts the data collection interval through the collection time change formula, so that the system can flexibly adapt to the changes in the operating state of the fan, ensuring the real-time and accuracy of the collected data; adopts interpolation processing technology to generate a gap radar map based on the flange sensor data of the fan tower and blades, and visualizes the distribution changes of the flange gap through the radar map, which can accurately reflect the state of the fan and provide a reliable basis for state evaluation; by combining the gap changes between the flanges of adjacent towers, it is detected whether the tower is rotated and twisted; by designing the tower and blade abnormality detection formula, the abnormality index is calculated, so that the system can judge the severity of the abnormal situation according to multiple parameters of the tower and blade gap, temperature, and load, so as to carry out more targeted fault warning; the present invention sets an alarm reminder module, when the abnormality index of the tower or blade exceeds the set threshold, an alarm is promptly issued to remind maintenance personnel to perform detection and maintenance, which can effectively reduce the occurrence rate of fan failures and extend the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flow chart of a fault monitoring system for a fan status according to the present invention;

[0041] Figure 2 It is a schematic diagram of the cumulative clearance of the blade flange of the present invention;

[0042] Figure 3 It is a schematic diagram of the tower flange of the present invention;

[0043] Figure 4 It is a schematic diagram of adjacent height connection flanges of the present invention;

[0044] Figure 5 4 is a block diagram of an anomaly detection module of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Embodiment 1: A fault monitoring system for a fan status, such as Figure 1-2 As shown, including:

[0047] The data acquisition module collects the tower flange sensing data and the blade flange sensing data after adjusting the acquisition time interval according to the acquisition time variation formula;

[0048] The tower flange sensing data and the blade flange sensing data are collected according to the dynamic acquisition time obtained by the acquisition time variation formula, and a flange clearance accumulation diagram is obtained, wherein the acquisition time variation formula is:

[0049] ;

[0050] Among them, T new is the dynamic acquisition time; T is the original acquisition time interval; R is a random variable with a value range of [-1,1]; ΔT is the variable amplitude of the acquisition time interval.

[0051] The tower flange sensing data and the blade flange sensing data within the first preset time period are preprocessed to remove abnormal values ​​and fill missing values, generate a flange gap accumulation graph, and use the gap data greater than the abnormal threshold as abnormal blade flange gap data.

[0052] It should be noted that the collection time interval is adjusted through the collection time change formula to obtain more accurate gap data and its cumulative graph, reflecting the gap change of the flange; this dynamic adjustment method helps to avoid missing key data under fixed frequency collection, thereby improving the flexibility and accuracy of monitoring.

[0053] Before generating the flange clearance accumulation graph, the data needs to be preprocessed, including removing outliers and filling missing values, to ensure data accuracy. The system will mark the clearance data exceeding the preset threshold as abnormal blade flange clearance data for subsequent analysis.

[0054] An interpolation processing module is used to obtain flange gap sensor data at each flange in the fan, and to perform interpolation processing based on the flange gap sensor data to obtain a gap radar map at each flange;

[0055] A first preset number of flange gap sensors are evenly arranged at each flange connection of the tower to obtain tower flange sensing data; wherein, flange gap sensors are arranged between two adjacent flanges; and flange gap sensors at the same layer height are evenly distributed circumferentially; a second preset number of flange gap sensors are evenly arranged at each blade flange connection to obtain blade flange sensing data.

[0056] It should be noted that the module first collects the gap data of each key position of the wind turbine through the flange gap sensors set at the flanges of the tower and the blades. The flange structure of the tower is usually distributed at the connection points of different heights, while the flange structure of the blade is located at the connection point between the blade and the hub. By setting sensors to monitor the gap changes of the flanges, the corresponding gap radar diagram is obtained to reflect the mechanical state of each key part of the wind turbine during operation; the sensors of the upper flange are evenly distributed on the circumference of the flange to ensure that the data collection of the entire flange joint surface is not missed; the sensors of the lower flange are located in the middle of the adjacent sensors of the upper flange, that is, the center point between the adjacent sensors of the upper layer. This layout method helps to improve the accuracy of data collection and facilitates the accuracy of subsequent interpolation calculations; the structure of the blade flange is relatively small, but the force situation is complex, so it is necessary to evenly arrange the second preset number of sensors at the blade flange connection to accurately capture the gap situation at the connection between the blade and the hub. For example: divide the two connecting flanges into eight equal parts, install a DC split displacement sensor every 45°, install 8 gap sensors on each blade, and install 24 flange gap sensors on three blades. The change in the gap between the flange opening and closing is used to monitor the loosening and breakage of the bolts.

[0057] The tower flange sensing data and the blade flange sensing data are interpolated using a linear interpolation formula to obtain tower flange interpolation data and blade flange interpolation data, and a tower flange clearance radar map and a blade flange clearance radar map are obtained respectively according to the tower flange sensing data, the tower flange interpolation data, the blade flange sensing data and the blade flange interpolation data, wherein the linear interpolation formula is:

[0058] ;

[0059] Where x1 and x2 are the positions of adjacent sensors, y1 and y2 are the gap values ​​measured by adjacent sensors, x is the target position to be interpolated, and y is the gap estimate of the interpolated position.

[0060] The bolt position of each flange is used as the target position to be interpolated, and the number of bolts of each flange is used as the number to be interpolated.

[0061] It should be noted that the main function of the interpolation processing module is to obtain the gap sensor data at each flange in the fan, and process it through the linear interpolation formula to generate the tower flange gap radar map and the blade flange gap radar map. The purpose of this module is to solve the data gap caused by the limited distribution of sensors, fill the data of the uncollected points through linear interpolation, and improve the complete monitoring of the flange status.

[0062] The interpolation processing module uses the bolt position of each flange as the target point for interpolation, estimates the gap for each bolt position, clarifies the stress condition and gap change near each bolt, and helps detect the overall stability of the flange connection. For example, there are 8 bolts around the tower flange, and the sensor data only covers 4 main positions. The gap data near the remaining 4 bolts is estimated through the interpolation formula to determine the balance of the entire flange.

[0063] A status monitoring module monitors and determines the status of the fan according to the clearance radar diagram at each flange;

[0064] The status of the wind turbine can be detected and judged based on the radar diagram of the tower flange clearance at different heights at the same time;

[0065] If the gap values ​​measured by all sensors on the tower flange at the same height are relatively consistent, continue monitoring;

[0066] If the sensor gap values ​​at the tower flange at the same height position show different gap distributions or inconsistencies, and the inconsistencies have different directional characteristics or size characteristics at adjacent heights, the alarm reminder module issues an alarm message.

[0067] It should be noted that the module compares the gap values ​​of all sensors on the tower flange at the same height to determine whether the gap values ​​measured by these sensors are relatively consistent. For example, if the gap values ​​measured by each sensor at the flange at a certain height are all around 0.5mm, it means that the gap at this height is relatively stable and the system can continue to monitor normally; the status monitoring module will also detect the distribution of flange gaps at different heights to determine whether there are structural abnormalities. When differences in gap values ​​are detected at adjacent heights, and these differences show inconsistent distribution trends (such as an increasing trend in the gap value at height A, but a decreasing trend in the gap value at height B), this phenomenon reflects that the tower is tilted, twisted, or rotated. At this time, the status monitoring module will trigger an alarm reminder to notify the operation and maintenance personnel to conduct a detailed inspection.

[0068] The tower anomaly detection module processes the tower flange sensing data and the tower flange interpolation data using the tower anomaly detection formula to obtain the tower anomaly index, and performs detection according to the tower anomaly index;

[0069] After cleaning the tower flange sensing data and the tower flange interpolation data, the tower cleaning data is obtained and input into the tower anomaly detection formula to obtain the tower anomaly index, where the tower anomaly detection formula is:

[0070] ;

[0071] Among them, W is the abnormal index of the tower, is the adjustment factor, P A Cleaning data for the tower, B A It is the preset standard tower flange clearance data.

[0072] It should be noted that this module uses the tower anomaly detection formula to calculate based on the sensing data and interpolation data of the tower flange after cleaning to obtain the tower anomaly index, thereby determining whether there is an anomaly.

[0073] A cumulative graph acquisition module, used to collect the tower flange sensing data and the blade flange sensing data after adjusting the collection time interval according to the collection time variation formula, and obtain a flange clearance cumulative graph;

[0074] It should be noted that the main function of the cumulative graph acquisition module is to dynamically adjust the collection time interval according to the collection time change formula, flexibly collect the gap sensing data of the tower flange and the blade flange, and generate a flange gap cumulative graph. This graph can reflect the gap data accumulation at different time points, thereby helping to monitor the change trend of the flange gap over time and facilitate the detection of potential abnormalities or faults.

[0075] The blade abnormality data acquisition module is used to acquire the blade abnormal flange clearance data according to the flange clearance accumulation diagram;

[0076] It should be noted that the main function of the blade abnormal data acquisition module is to extract abnormal flange clearance data from the cumulative graph to identify and locate possible abnormal conditions of the blade flange. It mainly determines whether there is clearance data beyond the normal range by analyzing the data changes in the cumulative graph, and further uses these data as abnormal data for system analysis; the module extracts the blade flange clearance data from the flange clearance cumulative graph and sets an abnormal threshold. Any data exceeding this threshold will be marked as abnormal data.

[0077] The blade abnormality detection module obtains the abnormality index of the blade according to the blade abnormality detection formula, and performs detection according to the abnormality index of the blade;

[0078] The alarm reminder module issues an alarm based on the detection results of the abnormal index of the tower and the abnormal index of the blade.

[0079] According to the blade abnormal deformation degree data, blade abnormal surface temperature data, blade abnormal flange clearance data, blade abnormal load change data, blade material limit stress data and blade material shear strength data, according to the blade abnormality detection formula, the blade abnormality index is obtained, where the blade abnormality detection formula is:

[0080] ;

[0081] Where S is the blade abnormality index, A is the blade deformation degree data, B is the blade surface temperature data, C is the blade flange clearance data, E is the blade load change data, D is the blade material limit stress data, and F is the blade material shear strength data, where ω, , μ, ρ, σ, and τ are preset adjustment factors.

[0082] It should be noted that by attaching strain gauges to key parts of the blade (such as the root and the middle area with the greatest force), the strain data of the blade under different loads is measured, the deformation degree is deduced, and the blade deformation degree data is obtained; a contact temperature sensor is installed at a key position of the blade (such as the tip or the center of the windward surface) to record the surface temperature in real time and obtain the blade surface temperature data; a gap sensor is installed at the flange connection to obtain the blade flange gap data; by combining the wind speed and direction data of the fan operating environment, the load change trend is obtained through simulation calculation to obtain the blade load change data; in the fan production stage, the ultimate stress of the blade material is determined through laboratory test methods (such as tensile test and bending test) to obtain the ultimate stress data of the blade material; after the fan is installed, the reliability of the shear strength data is verified through a small range load test to obtain the shear strength data of the blade material. The blade anomaly detection module obtains the abnormality index of the blade through multi-dimensional data analysis, and mainly uses the blade anomaly detection formula to quantify the abnormal situation. The indicators include blade deformation, temperature, flange gap, load change, material ultimate stress and shear strength, providing a comprehensive evaluation of the blade status.

[0083] Embodiment 2: Based on Embodiment 1, a fault monitoring method for a fan state is provided, comprising the following steps:

[0084] S1: After adjusting the collection time interval according to the collection time change formula, collect the tower flange sensing data and the blade flange sensing data;

[0085] S2: acquiring flange gap sensor data at each flange in the fan, and performing interpolation processing based on the flange gap sensor data to obtain a gap radar map at each flange;

[0086] S3: monitoring and judging the status of the fan according to the clearance radar diagrams at the flanges;

[0087] S4: using a tower anomaly detection formula to process tower flange sensing data and tower flange interpolation data to obtain a tower anomaly index;

[0088] S5: After adjusting the collection time interval according to the collection time variation formula, the tower flange sensing data and the blade flange sensing data are collected, and a flange clearance accumulation diagram is obtained;

[0089] S6: Obtain the abnormality index of the blade according to the blade abnormality detection formula;

[0090] S7: Issue an alarm based on the abnormal index of the tower or the abnormal index of the blade.

[0091] The above embodiments are provided for persons familiar with the art to implement or use the present invention. Personnel familiar with the art can make various modifications or changes to the above embodiments without departing from the inventive concept of the present invention. Therefore, the protection scope of the present invention is not limited to the above embodiments, but should be the maximum scope of the innovative features mentioned in the claims.

Claims

1. A fault monitoring system for fan status, characterized in that: include: The data acquisition module collects the tower flange sensing data and the blade flange sensing data after adjusting the acquisition time interval according to the acquisition time variation formula; The interpolation processing module obtains the flange gap sensor data at each flange in the fan, and performs interpolation processing based on the flange gap sensor data to obtain the gap radar map at each flange, including: using a linear interpolation formula to interpolate the tower flange sensing data and the blade flange sensing data to obtain tower flange interpolation data and blade flange interpolation data, and according to the tower flange sensing data, the tower flange interpolation data, the blade flange sensing data and the blade flange interpolation data, respectively obtain the tower flange gap radar map and the blade flange gap radar map, wherein the linear interpolation formula is: ； in, and is the position of the adjacent sensor, and is the gap value measured by adjacent sensors, is the target position to be interpolated, is the gap estimate for the interpolated position; A status monitoring module monitors and determines the status of the fan according to the clearance radar diagram at each flange; The tower anomaly detection module processes the tower flange sensing data and the tower flange interpolation data using the tower anomaly detection formula to obtain the tower anomaly index, and performs detection according to the tower anomaly index; A cumulative graph acquisition module, used to collect the tower flange sensing data and the blade flange sensing data after adjusting the collection time interval according to the collection time variation formula, and obtain a flange clearance cumulative graph; The blade abnormality data acquisition module is used to acquire the blade abnormal flange clearance data according to the flange clearance accumulation diagram; The blade abnormality detection module obtains the abnormality index of the blade according to the blade abnormality detection formula, and performs detection according to the abnormality index of the blade; The alarm reminder module issues an alarm based on the detection results of the abnormal index of the tower and the abnormal index of the blades; The data acquisition module, after adjusting the acquisition time interval according to the acquisition time variation formula, collects the tower flange sensing data and the blade flange sensing data, and obtains the flange clearance accumulation diagram, including: collecting the tower flange sensing data and the blade flange sensing data according to the dynamic acquisition time obtained by the acquisition time variation formula, and obtaining the flange clearance accumulation diagram, wherein the acquisition time variation formula is: ； in, is the dynamic acquisition time; is the original collection time interval; is a random variable with a value range between [-1,1]; is the variable amplitude of the acquisition time interval.

2. A fault monitoring system for fan status according to claim 1, characterized in that: The data acquisition module collects tower flange sensing data and blade flange sensing data after adjusting the collection time interval according to the collection time change formula, including: evenly setting a first preset number of flange gap sensors at each flange connection of the tower to obtain tower flange sensing data; wherein the flange gap sensors are arranged between two adjacent flanges; and the flange gap sensors at the same layer height are evenly distributed circumferentially; and evenly setting a second preset number of flange gap sensors at each blade flange connection to obtain blade flange sensing data.

3. A fault monitoring system for wind turbine status according to claim 1, characterized in that: The tower flange sensing data and the blade flange sensing data are interpolated using a linear interpolation formula to obtain tower flange interpolation data and blade flange interpolation data, including: taking the bolt position of each flange as the target position to be interpolated, and taking the number of bolts of each flange as the number to be interpolated.

4. A fault monitoring system for wind turbine status according to claim 1, characterized in that: The state monitoring module monitors and determines the state of the wind turbine according to the clearance radar diagrams at each flange, including: detecting and determining the state of the wind turbine according to the clearance radar diagrams of the tower flanges at different heights at the same time; If the gap values ​​measured by all sensors on the tower flange at the same height are relatively consistent, continue monitoring; If the sensor gap values ​​at the same height position on the tower flange show different gap distributions or inconsistencies, and the inconsistencies have different directional characteristics or size characteristics on the tower flanges at adjacent storey heights, the alarm reminder module issues an alarm message.

5. A fault monitoring system for wind turbine status according to claim 1, characterized in that: The tower anomaly detection module uses a tower anomaly detection formula to process the tower flange sensing data and the tower flange interpolation data to obtain the tower anomaly index, including: performing data cleaning on the tower flange sensing data and the tower flange interpolation data to obtain the tower cleaning data, and inputting the data into the tower anomaly detection formula to obtain the tower anomaly index, wherein the tower anomaly detection formula is: ； in, is the abnormal index of the tower, is the regulating factor, Cleaning data for the tower, It is the preset standard tower flange clearance data.

6. A fault monitoring system for wind turbine status according to claim 1, characterized in that: The dynamic collection time obtained according to the collection time change formula is used to collect the tower flange sensing data and the blade flange sensing data, and obtain a flange clearance accumulation diagram, including: pre-processing the tower flange sensing data and the blade flange sensing data within a first preset time period to remove abnormal values ​​and fill in missing values, generating a flange clearance accumulation diagram, and using the clearance data greater than the abnormal threshold as the blade abnormal flange clearance data.

7. A fault monitoring system for wind turbine status according to claim 1, characterized in that: The blade abnormality detection module obtains the abnormality index of the blade according to the blade abnormality detection formula, including: obtaining the abnormality index of the blade according to the blade abnormal deformation degree data, the blade abnormal surface temperature data, the blade abnormal flange clearance data, the blade abnormal load change data, the blade material limit stress data and the blade material shear strength data according to the blade abnormality detection formula, wherein the blade abnormality detection formula is: ； in Leaf abnormality index, is the blade deformation degree data, B is the blade surface temperature data, C is the blade flange clearance data, E is the blade load change data, D is the blade material limit stress data, and F is the blade material shear strength data, where is the preset adjustment factor.

8. A method for fault monitoring of a wind turbine state, according to the fault monitoring system for a wind turbine state according to any one of claims 1 to 7, characterized in that: The steps include: S1: After adjusting the collection time interval according to the collection time change formula, collect the tower flange sensing data and the blade flange sensing data; S2: acquiring flange gap sensor data at each flange in the fan, and performing interpolation processing based on the flange gap sensor data to obtain a gap radar map at each flange; S3: monitoring and judging the status of the fan according to the clearance radar diagrams at the flanges; S4: using a tower anomaly detection formula to process tower flange sensing data and tower flange interpolation data to obtain a tower anomaly index; S5: After adjusting the collection time interval according to the collection time variation formula, the tower flange sensing data and the blade flange sensing data are collected, and a flange clearance accumulation diagram is obtained; S6: Obtain the abnormality index of the blade according to the blade abnormality detection formula; S7: Issue an alarm based on the abnormal index of the tower or the abnormal index of the blade.

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