A fan blade icing fault detection system and method

Abstract

The application relates to the technical field of new energy equipment operation monitoring and fault diagnosis, and discloses a fan blade icing fault detection system and method, which comprises a data acquisition module, a sensor unit, a data processing module, an algorithm analysis module, an alarm module and a communication module. Through integration of a vibration sensor, an ultrasonic sensor, a temperature and humidity sensor and an infrared thermal imager, real-time data acquisition is realized. The data processing module carries out pretreatment and feature extraction on the collected data, and the algorithm analysis module dynamically calculates an icing risk index based on vibration frequency variation, icing mass, wind speed and other parameters. When the threshold value is exceeded, the alarm module is triggered to generate a warning signal. The application realizes multi-dimensional data fusion, real-time dynamic monitoring and intelligent risk assessment, can accurately judge the blade icing state, reduces false positives and false negatives, and provides scientific operation and maintenance suggestions.

Description

Technical Field

[0001] The present application relates to the technical field of operation monitoring and fault diagnosis of new energy equipment, and in particular to a system and method for detecting icing faults in wind turbine blades. Background Art

[0002] Wind power generation is a crucial component of the current global energy transition, and wind turbine blades are core components in wind power systems. Their operating status directly impacts power generation efficiency and equipment safety. Blade icing is a common problem in wind turbines operating in cold, humid, or high-altitude regions. Ice on the blade surface alters aerodynamic properties, reducing lift and increasing drag, significantly reducing power generation efficiency. It can also cause unbalanced blade operation, exacerbating vibrations and even leading to serious accidents such as blade breakage and bearing damage. Therefore, real-time detection and fault warning of wind turbine blade icing are of great engineering significance.

[0003] Although research and technical applications on wind turbine blade icing have gradually increased in recent years, existing detection technologies still have many deficiencies in practical applications:

[0004] Existing technologies often employ single detection methods, such as blade icing detection based on image recognition, fault diagnosis based on vibration signal analysis, and ultrasonic sensors monitoring ice thickness. These methods can only monitor a specific aspect of blade icing. For example, while image recognition technology can detect iced areas on the blade surface, it cannot directly assess ice thickness and quality. Vibration signal analysis can detect unbalanced vibrations in the blade, but it struggles to distinguish whether the vibration anomaly is caused by icing or other mechanical faults. Furthermore, these single methods often lack data fusion, making it difficult to comprehensively analyze the blade's operating status, resulting in incomplete detection results.

[0005] Many current detection technologies are primarily used for static testing, such as manual inspections and scheduled shutdowns. These methods rely on manual intervention and are unable to reflect the icing condition of blades during dynamic operation in real time. During dynamic operation, blades are affected by multiple factors, including wind speed, rotational speed, and icing load. The distribution and thickness of ice can change, further exacerbating vibration and imbalance. However, existing static detection technologies are unable to capture these dynamic changes, resulting in the discovery of icing failures often lagging behind their development, posing a significant safety hazard.

[0006] Some detection systems rely on a single parameter (such as vibration amplitude or ambient temperature) to determine faults. This single-parameter threshold alarm mechanism has significant shortcomings. For example, relying solely on temperature thresholds to determine icing risk fails to consider the impact of humidity and may overlook potential icing risks in low-humidity environments. Systems that rely solely on vibration signals for alarms may falsely trigger alarms due to non-icing factors such as mechanical noise and wind speed fluctuations. Such a simple mechanism is prone to missed alarms and results in a large number of false alarms, reducing system reliability.

[0007] Existing technologies primarily focus on determining whether blades are iced, but pay less attention to the subsequent effects of icing. For example, ice on blades can lead to localized uneven mass distribution, causing a shift in the blade's overall center of gravity and load variations, increasing the risk of fatigue damage. This structural risk is critical to the long-term safety of blades, yet current technologies haven't effectively assessed and monitored it.

[0008] Wind turbines often operate in complex and variable environments. Factors such as strong winds, extreme cold, and high humidity can affect sensor measurement accuracy and system stability. Existing detection technologies are often designed for a single environmental condition and lack dynamic adaptability. For example, in strong winds, blade vibration signals are easily disrupted by wind speed fluctuations, and ultrasonic sensors can introduce errors in ice thickness measurements due to variations in surface roughness. These issues make existing systems less reliable in actual operation.

[0009] In summary, existing wind turbine blade icing fault detection technologies suffer from problems such as limited detection methods, incomplete monitoring, lack of dynamic monitoring capabilities, simple alarm mechanisms, lack of structural risk assessment, and poor adaptability. These shortcomings limit the efficient and safe operation of wind turbines in icing environments. Therefore, this application proposes a wind turbine blade icing fault detection system and method to address these issues. Summary of the Invention

[0010] The purpose of this application is to provide a fan blade icing fault detection system and method to solve the defects in the existing technology.

[0011] The above-mentioned purpose of the present invention is achieved like this:

[0012] In one aspect, the present invention provides a system for detecting icing faults in fan blades, the system comprising:

[0013] Data acquisition module: includes sensor units, which are used to collect key data of wind turbine operation, including blade vibration data , ambient temperature data and ambient humidity data , wind speed data , Blade ice thickness and blade surface temperature distribution ;Sensor unit: including: temperature and humidity sensor: used to collect environmental temperature and humidity data; ultrasonic sensor: used to monitor the local ice thickness on the blade surface ; Infrared thermal imager: used to obtain the temperature distribution on the blade surface ;Vibration sensor, used to collect blade vibration data;

[0014] Data processing module: including data pre-processing unit, used to process vibration signals , ambient temperature data and ambient humidity data Denoising and filtering, ambient humidity data Used to adjust the blade vibration abnormality threshold ;

[0015] Feature extraction unit: used to extract blade vibration frequency changes , Blade surface temperature distribution , ambient temperature data and ambient humidity data ;

[0016] Algorithm analysis module: used to calculate the blade icing risk index according to the following formula Right now:

[0017] ;

[0018] in, is the blade icing risk index; is the blade vibration frequency change; is the ice mass on the blade surface; is the wind speed data; , , is the risk weight factor, According to the ambient temperature data Make adjustments;

[0019] The ice mass on the blade surface Calculated according to the following formula:

[0020] ;

[0021] in, is the total ice mass of the blade, in kilograms (kg); is the ice area on the blade surface, in square meters ; is the ice thickness at a certain position on the blade surface, in meters (m); is the ice density at a certain position on the blade surface, in kilograms per cubic meter , calculated using the following formula:

[0022] ;

[0023] in, is the standard ice density; is the temperature at a certain position on the blade surface; is the freezing temperature;

[0024] Alarm module: used for Trigger alarm signal And output maintenance suggestions, is the icing risk threshold;

[0025] Communication module: used to transmit the test results to the remote monitoring center.

[0026] Furthermore, the blade vibration frequency changes Calculated according to the following formula:

[0027] ;

[0028] in, For the A vibration amplitude; For the The sampling interval of vibration data; is the number of data sampling points; Indicates changes in blade vibration frequency and is used to identify vibration anomalies caused by blade icing.

[0029] Furthermore, the local ice thickness on the blade surface is The area of ​​the entire blade ice is calculated by real-time measurement using an ultrasonic sensor and combined with the geometric model of the wind turbine blade. .

[0030] Furthermore, the risk weight factor 、 and The value of is dynamically adjusted according to the following conditions: ambient temperature data The lower, The higher the weight, the higher the wind speed. The bigger, The higher the weight, the more the blade vibration frequency changes. The greater the amplitude, The higher the weight.

[0031] Furthermore, the data processing module further includes a dynamic anomaly detection unit for detecting abnormal vibration modes of the blade during operation. The specific steps include:

[0032] The collected blade vibration frequency changes Compare with historical normal operating data;

[0033] By calculating the similarity index ,Right now:

[0034] ;

[0035] like , determine that the blade is operating abnormally;

[0036] in, is the actual sampled vibration frequency change; is the reference vibration frequency data; is the blade vibration abnormality threshold.

[0037] Another aspect of the present invention further provides a detection method based on the wind turbine blade icing fault detection system, comprising the following steps:

[0038] Collect the operating data of the wind turbine blades through vibration sensors, temperature and humidity sensors, ultrasonic sensors and infrared thermal imagers;

[0039] Extract blade vibration frequency changes through data preprocessing module denoising and filtering , ambient temperature data , environmental humidity data , local ice thickness on blade surface and blade surface temperature distribution ;

[0040] Calculate risk index through algorithm analysis module ;

[0041] like , triggering an alarm signal And output maintenance recommendations.

[0042] Furthermore, the method uses time series forecasting to analyze historical data and real-time data to calculate the trend of vibration frequency changes:

[0043] ;

[0044] in, is the predicted vibration frequency change; is the weight coefficient; is the sliding window size, real-time vibration frequency changes and predicted value Compare; if the two deviate , If it is the vibration frequency threshold, the system determines that the vibration is abnormal.

[0045] Furthermore, the method further includes calculating the load change caused by blade icing, and calculating the center of gravity offset using the following formula:

[0046] ;

[0047] in, is the center of gravity offset; is the center of gravity position in the non-icing state; is the local icing mass, when When the system generates an early warning signal, it will make maintenance suggestions: slow down, stop the machine for inspection or perform blade de-icing. is the center of gravity offset threshold.

[0048] Furthermore, the alarm signal The output includes:

[0049] Fan shutdown signal;

[0050] Recommendations for heating and deicing fan blades;

[0051] Recommendations for adjusting fan operating parameters, including reducing speed or changing blade angle.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The system of this application integrates multiple sensors such as vibration sensors, ultrasonic sensors, temperature and humidity sensors, and infrared thermal imagers to achieve multi-dimensional data collection of blade operating status. Combined with the algorithm analysis module, it can comprehensively detect the icing characteristics of the blades.

[0054] Specifically: the system of the present invention can change the vibration frequency of the blade Dynamic monitoring to detect operational anomalies (such as unbalanced vibration) caused by icing; using ultrasonic sensors and infrared thermal imagers to accurately measure the local ice thickness of the blades and ice quality , and calculates the load distribution in combination with the blade geometry model; the system dynamically calculates the risk index through a weight adjustment algorithm based on real-time environmental data (such as temperature, humidity and wind speed) , to achieve quantitative assessment of blade icing risk; through time series prediction and center of gravity offset The system can predict structural risks caused by icing and provide more accurate operation and maintenance recommendations.

[0055] 2. The system of this application is a multi-source data fusion and dynamic monitoring: Unlike traditional single detection methods, this system combines vibration, temperature, humidity, ice thickness and other multi-dimensional data to comprehensively analyze the blade operation status, significantly improving the accuracy and reliability of detection. Real-time assessment and early warning: Dynamically calculate the risk index through the algorithm analysis module The system can assess icing risks in real time and provide intelligent warnings based on the risk level to avoid safety hazards caused by icing delays. Structural impact analysis: By calculating the center of gravity offset and load changes, the system can quantify the impact of icing on the blade structure, providing a scientific basis for operation and maintenance decisions;

[0056] To sum up, the technical solution of the present invention overcomes the shortcomings of the existing technology in detection accuracy, real-time and comprehensiveness through multi-sensor fusion detection, dynamic algorithm analysis and structural impact assessment; the system of the present application can not only detect blade icing failures in real time, but also dynamically assess the icing risk and its impact on the structure, thereby effectively reducing the operation risk of the wind turbine, extending the service life of the blades, and improving the operation efficiency of the wind turbine; the present invention is suitable for wind turbine operating environments under various complex climatic conditions, and has wide engineering application value and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 This is a system block diagram of a fan blade icing fault detection system provided by the present invention;

[0059] Figure 2 This is a flow chart of a method for detecting icing faults of fan blades provided by the present invention. DETAILED DESCRIPTION

[0060] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0061] The following combination Figure 1 、 Figure 2 The specific embodiments of the present invention are described in detail with reference to the accompanying drawings and multiple embodiments.

[0062] Example 1: Collection and analysis of fan blade vibration data; This example focuses on how to use vibration sensors to detect the operating status of fan blades and calculate the blade vibration frequency changes , to make a preliminary assessment of the icing risk.

[0063] Sensor arrangement: The sensor is connected to the electric slip ring installed on the wind turbine hub through a wire, and the electric slip ring is connected to the control unit installed in the nacelle. Multiple vibration sensors (such as accelerometers) are installed at the root and middle part of the wind turbine blade to collect the amplitude of blade vibration. The data sampling frequency is set to 100Hz and the number of sampling points is .

[0064] Data acquisition vibration sensor records the vibration amplitude of each sample and time interval The sampling data is transmitted to the data processing module through the communication module.

[0065] Data preprocessing: The preprocessing module filters and denoises the vibration data: a bandpass filter is used to remove high-frequency noise;

[0066] Smooth the data:

[0067] ;

[0068] in, is the smoothing coefficient, which is used to balance real-time performance and stability.

[0069] Vibration frequency calculation: Calculate the vibration frequency change using the following formula :

[0070] ;valve

[0071] Calculation results As the characteristic quantity of blade vibration state, Exceeding the vibration frequency threshold When the system determines that the blade has abnormal vibration.

[0072] Output and transfer: The results are stored in the database for use by subsequent risk assessment modules; if abnormal vibration is found, the system generates an early warning signal, prompting you to check whether the blades may be frozen.

[0073] Example 2: Calculation of Blade Icing Mass: This example describes how to calculate the icing mass on a blade surface using an ultrasonic sensor and an infrared thermal imager. .

[0074] Data collection: Ultrasonic sensors are installed at multiple locations on the blade (for example, one sensor is placed every 2 meters) to monitor the local ice thickness on the blade surface in real time. ; Infrared thermal imager records the temperature distribution on the blade surface .

[0075] Ice density calculation: Calculate the local ice density according to the following formula :

[0076] ;

[0077] in, , is the standard ice density; is the freezing temperature; is the local temperature.

[0078] Icing area calculation: Combine the geometric model of the wind turbine blade and the thickness distribution measured by the ultrasonic sensor to calculate the icing area on the blade surface .

[0079] Ice mass calculation: ice area on blade surface Integrate and calculate the total ice mass, which is:

[0080] ;

[0081] Output and result analysis: The calculation results are input into the risk index calculation module; if the ice mass exceeds the set threshold, the system triggers an ice alarm.

[0082] Communication module: used to transmit the test results to the remote monitoring center.

[0083] Example 3: Calculation and Dynamic Adjustment of Blade Icing Risk Index; This example describes how to calculate the icing risk index by integrating data from multiple sensors. and improve the evaluation accuracy through dynamic adjustment.

[0084] Risk Index Calculation: Risk Index The calculation formula is as follows:

[0085] ;

[0086] in, is the blade vibration frequency change; is the ice mass; is the wind speed, which is obtained in real time by the wind speed sensor; , , is the dynamically adjusted weight factor.

[0087] Dynamic weight adjustment: Adjust weight factors according to environmental conditions:

[0088] When the temperature Below -10°C, increase The value of

[0089] When wind speed When it is higher than 15 m / s, increase The value of

[0090] The weight ratio is dynamically adjusted through real-time input of environmental parameters.

[0091] Alarm threshold: When Exceeding the icing risk threshold When the alarm signal is triggered , prompting the operation and maintenance personnel to conduct inspection or maintenance.

[0092] Example 4: Evaluation of the impact of blade icing on structural loads; This example calculates the center of gravity offset Evaluate the stress changes on the blade structure.

[0093] Local ice mass distribution: Calculate the local ice mass based on the local thickness and density:

[0094] ;

[0095] Calculation of center of gravity offset According to the blade geometry model, calculate the center of gravity offset:

[0096] ;

[0097] in, is the center of gravity offset; It is the center of gravity position of the blade when there is no ice.

[0098] Structural stress analysis: If If the set threshold is exceeded, the system will generate a warning signal of structural damage and provide operational adjustment suggestions, such as slowing down or stopping the machine.

[0099] Example 5: Dynamic anomaly detection unit for blade vibration anomaly detection; This example describes in detail the specific implementation of the dynamic anomaly detection unit, which detects blade vibration anomalies by comparing historical data with real-time data.

[0100] Data collection and storage: historical data : During the normal operation of the blade, record the vibration frequency changes over a period of time , which is stored in the database as reference vibration data.

[0101] Real-time data : Vibration frequency changes are collected in real time through vibration sensors.

[0102] Similarity Calculation The dynamic anomaly detection unit calculates the similarity between real-time data and reference data:

[0103] ;

[0104] in, The vibration frequency changes sampled in real time; For historical reference data; is the number of sampling points.

[0105] Abnormal judgment: If , is the blade vibration abnormality threshold, which is the preset dynamic threshold, and the system determines that the blade vibration is abnormal;

[0106] Blade vibration abnormality threshold According to the ambient temperature data and ambient humidity data Make adjustments, then:

[0107] ;

[0108] in, is the basic threshold, is the humidity sensitivity coefficient, The value range is 0.01~0.1.

[0109] Result output: Triggering an early warning signal, indicating that the vibration anomaly may be related to blade icing; storing the anomaly detection results for further analysis.

[0110] Example 6: A method for detecting an icing fault of a fan blade; This example describes in detail the complete process of the method for detecting an icing fault of a fan blade.

[0111] Data collection: Sensors (vibration, temperature and humidity, ultrasonic, and infrared thermal imagers) are used to collect blade operation vibration, environment, ice thickness, and temperature distribution data; the data is stored in the form of a time series.

[0112] Data preprocessing: Filter, smooth and remove noise from the collected data. , and wind speed Perform averaging to eliminate instantaneous fluctuations.

[0113] Feature extraction extracts the following features from the preprocessed data: vibration frequency changes (Reference Example 1); Ice thickness on the blade surface and ice quality (Reference Example 2); Wind speed and ambient temperature data and ambient humidity data .

[0114] Risk index calculation: Combined formula:

[0115] ;

[0116] Calculating blade icing risk index , and set according to The result triggers an alarm signal , is the icing risk threshold.

[0117] Alarm and maintenance suggestions: When the risk index exceeds the threshold, the alarm module outputs Output maintenance recommendations: including shutdown inspection, blade heating or adjustment of operating parameters (such as reducing speed).

[0118] Example 7: Time Series Prediction and Trend Analysis; This example describes the application of a time series prediction model in the analysis of vibration frequency change trends.

[0119] Time series modeling: Collect historical vibration frequency changes of blades Data, forming a time series ;

[0120] Build a sliding window prediction model:

[0121] ;

[0122] in, is the predicted vibration frequency change; is the weight coefficient of the sliding window; is the sliding window size (e.g. 10 time steps).

[0123] Real-time vibration frequency changes and predicted value Compare; if the two deviate , the system determines that the vibration is abnormal, is the vibration frequency threshold.

[0124] Forecast result analysis: Dynamically adjust the and ambient temperature data ) Dynamically adjust the sliding window size of the prediction model and weights , improving prediction accuracy. Output results Output vibration trend prediction results and deviation assessment, indicating whether there is an icing risk.

[0125] Example 8: Evaluation of center of gravity offset caused by blade icing; This example calculates the center of gravity offset Evaluate the effects of icing on blade structures.

[0126] Local ice mass calculation: Use ultrasonic sensors and infrared thermal imagers to calculate the local ice mass distribution on the blade:

[0127] ;

[0128] Calculation of center of gravity offset: Combined with the geometric model of the blade, calculate the center of gravity offset caused by icing:

[0129] ;

[0130] in, is the center of gravity of the blade in the non-icing state; is the ice area of ​​the blade.

[0131] Result evaluation and early warning: (center of gravity deviation threshold), the system generates a warning signal and makes maintenance suggestions: slow down, stop for inspection or perform blade de-icing.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A fan blade icing fault detection system, characterized in that: The system comprises: Data acquisition module: includes sensor units, which are used to collect key data of wind turbine operation, including blade vibration data , ambient temperature data and ambient humidity data , wind speed data , Blade ice thickness and blade surface temperature distribution ;Sensor unit: including: temperature and humidity sensor: used to collect environmental temperature and humidity data; ultrasonic sensor: used to monitor the local ice thickness on the blade surface ; Infrared thermal imager: used to obtain the temperature distribution on the blade surface ;Vibration sensor, used to collect blade vibration data; Data processing module: including data pre-processing unit, used to process vibration signals , ambient temperature data and ambient humidity data Denoising and filtering, ambient humidity data Used to adjust the blade vibration abnormality threshold ; Feature extraction unit: used to extract blade vibration frequency changes , Blade surface temperature distribution , ambient temperature data and ambient humidity data ; Algorithm analysis module: used to calculate the blade icing risk index according to the following formula Right now: ； in, is the blade icing risk index; is the blade vibration frequency change; is the ice mass on the blade surface; is the wind speed data; , , is the risk weight factor, According to the ambient temperature data Make adjustments; The ice mass on the blade surface Calculated according to the following formula: ； in, is the summarized ice mass of the blade; is the ice area on the blade surface; is the ice thickness at a certain position on the blade surface; is the ice density at a certain position on the blade surface, which is calculated using the following formula: ； in, is the standard ice density; is the temperature at a certain position on the blade surface; is the freezing temperature; Alarm module: used for Trigger alarm signal And output maintenance suggestions, is the icing risk threshold; Communication module: used to transmit the test results to the remote monitoring center.

2. A fan blade icing fault detection system according to claim 1, characterized in that: The blade vibration frequency changes Calculated according to the following formula: ； in, For the A vibration amplitude; For the The sampling interval of vibration data; is the number of data sampling points; Indicates changes in blade vibration frequency and is used to identify vibration anomalies caused by blade icing.

3. A fan blade icing fault detection system according to claim 1, characterized in that: Local ice thickness on the blade surface The area of ​​the entire blade ice is calculated by real-time measurement using an ultrasonic sensor and combined with the geometric model of the wind turbine blade. .

4. A fan blade icing fault detection system according to claim 1, characterized in that: The risk weight factor 、 and The value of is dynamically adjusted according to the following conditions: ambient temperature data The lower, The higher the weight, the higher the wind speed. The bigger, The higher the weight, the more the blade vibration frequency changes. The greater the amplitude, The higher the weight.

5. The wind turbine blade icing fault detection system according to claim 1, characterized in that: The data processing module further includes a dynamic anomaly detection unit for detecting abnormal vibration patterns of the blade during operation, and the specific steps include: The collected blade vibration frequency changes Compare with historical normal operating data; By calculating the similarity index ,Right now: ； like , determine that the blade is operating abnormally; in, is the actual sampled vibration frequency change; is the reference vibration frequency data; is the blade vibration abnormality threshold.

6. A detection method based on the wind turbine blade icing fault detection system according to any one of claims 1 to 5, characterized in that: The following steps are involved: Collect the operating data of the wind turbine blades through vibration sensors, temperature and humidity sensors, ultrasonic sensors and infrared thermal imagers; Extract blade vibration frequency changes through data preprocessing module denoising and filtering , ambient temperature data , environmental humidity data , local ice thickness on blade surface and blade surface temperature distribution ; Calculate risk index through algorithm analysis module ; like , triggering an alarm signal And output maintenance recommendations.

7. The detection method according to claim 6, characterized in that The method uses time series forecasting to analyze historical data and real-time data and calculate the trend of vibration frequency changes: ； in, is the predicted vibration frequency change; is the weight coefficient; is the sliding window size, real-time vibration frequency changes and predicted value Compare; if the two deviate , If it is the vibration frequency threshold, the system determines that the vibration is abnormal.

8. The detection method according to claim 6, characterized in that The method further includes calculating the load change caused by blade icing and calculating the center of gravity offset using the following formula: ； in, is the center of gravity offset; is the center of gravity position in the non-icing state; is the local icing mass, when When the system generates an early warning signal, it will make maintenance suggestions: slow down, stop the machine for inspection or perform blade de-icing. is the center of gravity offset threshold.

9. The detection method according to claim 6, characterized in that The alarm signal The output includes: Fan shutdown signal; Recommendations for heating and deicing fan blades; Recommendations for adjusting fan operating parameters, including reducing speed or changing blade angle.

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