A surface-mount fiber optic grating structure health monitoring system for wind turbine blades

Abstract

This invention discloses a surface-mounted fiber Bragg grating (FBG) structural health monitoring system for wind turbine blades, belonging to the field of intelligent operation and maintenance technology for new energy equipment. It includes a signal acquisition module, a signal demodulation and conversion module, a data analysis and feature extraction module, a status assessment module, and a monitoring output module. The signal acquisition module is an embedded FBG demodulation system located within the wind turbine hub, receiving optical signals transmitted from sensors. This surface-mounted FBG structural health monitoring system for wind turbine blades uses surface-mounted FBG sensors as sensing units, combined with an embedded demodulation device built into the hub. Compared to traditional resistance strain sensors, it has stronger anti-electromagnetic interference capabilities, a simpler circuit layout, and excellent environmental adaptability. The sensors are arranged in a standard three-vertical, three-horizontal configuration, combined with independent temperature sensors to achieve cross-sensitivity compensation for temperature and stress, effectively eliminating monitoring errors caused by ambient temperature and significantly improving the accuracy of stress and strain data detection.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation and maintenance technology for new energy equipment, specifically a surface-mounted fiber optic grating structure health monitoring system for wind turbine blades. Background Technology

[0002] In recent years, with the acceleration of industrialization, human activities have caused varying degrees of damage to the natural environment. Traditional fossil energy, being non-renewable and heavily polluting, can no longer meet the strategic needs of current sustainable development. Developing clean and renewable energy is of great significance for reducing greenhouse gas emissions.

[0003] Wind energy has become one of the new energy sources that have been vigorously developed both domestically and internationally in recent years due to its advantages such as being green, renewable, and readily available. With the improvement of wind power generation layout and the continuous increase in the power of wind turbines, the size of wind turbine blades has also entered a "large" era. As the input end of wind energy, the blades are the key core components of the wind turbine, determining how much wind energy is captured.

[0004] Wind turbine blades have high material requirements, needing not only high strength, light weight, corrosion resistance, and fatigue resistance, but also widespread use of glass fiber, carbon fiber, and other high-performance composite materials. Blade manufacturing primarily employs techniques such as hand lay-up, molding, prepreg, pultrusion, fiber winding, resin transfer molding, and vacuum injection molding. During manufacturing, uneven resin content, poor resin wetting, and incomplete curing can lead to defects such as cracks, skin defects, and blade deformation. Furthermore, wind farms are typically located in harsh environments such as deserts, Gobi, and coastal areas, making them highly susceptible to corrosion from wind, sand, seawater, and snow. Over long-term operation, blades may develop pitting, gel coat peeling, surface cracking, and skin bulging, resulting in decreased strength and stiffness. Cracks commonly occur at the blade root and two-thirds of the blade length, and in severe cases, can cause the blade to break at the root, leading to the collapse of the entire wind turbine. This not only causes significant economic losses for the country and enterprises but also threatens the lives and property of workers. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a surface-mount fiber optic grating structure health monitoring system for wind turbine blades, which solves the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a surface-mount fiber optic grating structure health monitoring system for wind turbine blades, comprising a signal acquisition module, a signal demodulation and conversion module, a data parsing and feature extraction module, a status assessment module, and a monitoring output module; The signal acquisition module is an embedded fiber optic demodulation system located in the wind turbine hub. It receives the optical signals transmitted by the sensor, performs optical signal analysis, converts them into recognizable electrical signals, and completes signal form conversion and preliminary processing. The electrical signal data demodulated by the signal demodulation conversion module is uniformly uploaded to the ground-based host computer monitoring platform and data processing center via base station wireless transmission to ensure real-time and stable data transmission. The data parsing and feature extraction module relies on dedicated processing software and algorithms to perform calculations and analysis on the received data, eliminate interference data, extract core feature parameters that can reflect the structural state of the blade, and at the same time use temperature sensor data to eliminate monitoring errors caused by the cross-sensitivity of fiber optic grating temperature stress. The condition assessment module combines the original design parameters of the blade, long-term historical operating data, and methods such as statics, modal analysis, and fatigue analysis, supplemented by big data and artificial intelligence algorithms, to comprehensively determine the overall structural health status of the blade. The monitoring output module outputs blade health assessment results, enabling all-weather, all-round monitoring of the wind turbine blade's operating status, providing a basis for wind turbine operation and maintenance, safety management and performance optimization.

[0007] Furthermore, the method for implementing health monitoring includes the following steps: S1: In the key areas of the root, middle and tip of the wind turbine blade, the stress, strain and ambient temperature physical parameters of the blade under the operating state are collected synchronously in the partitioned areas by surface-mounted stress grating sensors and temperature grating sensors. The mechanical and temperature quantities are uniformly converted into optical signals and output in real time. S2: The embedded fiber optic grating demodulation device inside the hub receives multiple optical signals, completes channel-by-channel analysis and demodulation, converts the optical signals into standard digital electrical signals, and simultaneously completes preliminary noise reduction processing such as front-end filtering and baseline correction to suppress electromagnetic and mechanical vibration interference on site. S3: Package and encapsulate the pre-processed multi-channel monitoring data, establish a wireless transmission link through the base station, and upload it to the ground host computer monitoring platform and data processing center in batches according to the set time sequence. The breakpoint resume mechanism is used to ensure the integrity and continuity of data transmission. S4: In-depth data analysis, error correction, and comprehensive evaluation. First, the received data undergoes secondary noise reduction and outlier removal to extract core characteristic parameters that characterize blade deformation and stress state. Then, using measured data from temperature sensors, systematic errors caused by cross-sensitivity of fiber Bragg grating temperature and stress are accurately eliminated. Combining the blade's factory design parameters and historical operating data throughout its entire life cycle, static analysis, modal analysis, and fatigue analysis are carried out sequentially. Big data and artificial intelligence algorithms are introduced to perform multi-model fusion calculations to comprehensively determine the current structural health level and damage risk of the blade. S5: Displays blade health status, stress data, and risk level in real time in a visual format, and generates monitoring logs and health assessment reports simultaneously. The system enables 24 / 7 online monitoring of blades, triggers prompts immediately upon identifying abnormal conditions, and provides continuous data support for wind turbine operation and maintenance scheduling, on-site safety management, and unit performance optimization.

[0008] Furthermore, the signal acquisition module includes two types of fiber Bragg grating sensors: stress grating sensors and temperature grating sensors.

[0009] Furthermore, each wind turbine has three blades, and each blade is equipped with 6 stress grating sensors and 2 temperature grating sensors, for a total of 24 fiber optic grating sensors for the entire unit.

[0010] Furthermore, the stress grating sensor is arranged in a three-vertical and three-horizontal pattern, laid out sequentially at two-meter intervals along the blade root towards the blade tip.

[0011] Furthermore, the stress grating sensor is mainly arranged in the root region of the blade to detect the axial and lateral forces acting on the blade during operation.

[0012] Furthermore, the system is set to a monitoring sampling frequency of 100Hz, which can be flexibly adjusted according to the blade size, structural stiffness and natural frequency to fully capture dynamic monitoring information such as blade vibration and deformation.

[0013] Furthermore, in step S1, the temperature grating sensor is independently deployed and is not affected by blade stress, which is used to compensate for the cross-sensitivity problem of fiber optic grating temperature stress.

[0014] Furthermore, in step S3, the data transmission process employs data packaging, time-sharing transmission, and breakpoint resumption mechanisms to ensure the integrity and continuity of data transmission.

[0015] Furthermore, both types of fiber Bragg grating sensors are equipped with dedicated flexible adhesive substrates and protective coatings, and both types of fiber Bragg grating sensors are internally treated with anti-electromagnetic shielding. Combined with the front-end demodulation equipment, they have dual anti-interference capabilities. All stress grating sensors and temperature grating sensors are wired in a series network configuration.

[0016] This invention provides a surface-mount fiber optic grating structure health monitoring system for wind turbine blades, which has the following beneficial effects: 1. This wind turbine blade surface-mount fiber optic grating structural health monitoring system uses surface-mount fiber optic grating sensors as sensing units, paired with hub-embedded demodulation equipment. Compared to traditional resistance strain sensors, it offers stronger anti-electromagnetic interference capabilities, simpler circuit layout, and superior environmental adaptability. The sensors are arranged in a standard three-vertical, three-horizontal configuration, combined with independent temperature sensors to achieve cross-sensitivity compensation for temperature and stress, effectively eliminating monitoring errors caused by ambient temperature and significantly improving the accuracy of stress and strain data detection. Simultaneously, the sensors and demodulation equipment are small in size, lightweight, corrosion-resistant, and fatigue-resistant, allowing for long-term adaptation to harsh outdoor environments such as sandstorms, salt spray, and high / low temperatures, reducing equipment maintenance and replacement frequency and lowering hardware operation and maintenance costs.

[0017] 2. This wind turbine blade surface-mounted fiber optic grating structure health monitoring system adopts a wireless base station transmission mode and is equipped with data packaging, time-sharing transmission, and breakpoint resume mechanisms to ensure long-distance, uninterrupted, complete, and stable transmission of monitoring data. With a standard sampling frequency of 100Hz, it can accurately capture dynamic signals such as blade vibration and instantaneous deformation, balancing real-time monitoring with data integrity. The entire system achieves synchronous monitoring of key areas at all points—the root, middle, and tip of the blade—overcoming the limitations of traditional equipment's large size and inconvenient on-site deployment. It truly achieves 24 / 7 online monitoring of wind turbine blades, providing a comprehensive understanding of the blade's real-time operating status.

[0018] 3. This wind turbine blade surface-mounted fiber optic grating structural health monitoring system integrates static, modal, and fatigue multi-dimensional mechanical analysis methods, and combines big data and artificial intelligence algorithms for comprehensive judgment. Based on blade design parameters and historical data throughout the entire life cycle, it can not only identify existing damage in real time, but also predict the development trend of fatigue damage. The system has functions such as visualization, anomaly classification alarm, automatic generation of monitoring logs and evaluation reports, etc. It can quickly locate the fault location and risk level, provide reliable data support for wind turbine operation and maintenance scheduling, safety management, and unit performance optimization, and avoid major safety accidents such as blade breakage and unit collapse in advance, effectively reducing the safety risks and economic losses of wind power operation. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the steps of a surface-mounted fiber optic grating structure health monitoring system for wind turbine blades according to the present invention; Figure 2 This is a schematic diagram of the wind turbine blade measuring point layout for a surface-mounted fiber optic grating structure health monitoring system for wind turbine blades according to the present invention. Detailed Implementation

[0020] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0021] like Figure 1 and Figure 2 As shown, the present invention provides a technical solution: a health monitoring system for surface-mounted fiber optic grating structures of wind turbine blades, comprising a signal acquisition module, a signal demodulation and conversion module, a data parsing and feature extraction module, a status assessment module, and a monitoring output module; The signal acquisition module is an embedded fiber Bragg grating demodulation system located in the wind turbine hub. It receives optical signals transmitted from sensors, performs optical signal analysis, converts them into recognizable electrical signals, and completes signal form conversion and preliminary processing. The signal acquisition module includes two types of fiber Bragg grating sensors: stress grating sensors and temperature grating sensors. The stress grating sensors are arranged in a three-vertical and three-horizontal configuration, laid out sequentially at two-meter intervals from the blade root to the blade tip. The stress grating sensors are mainly arranged in the blade root area to detect the axial and lateral forces experienced by the blade during operation. Both types of fiber Bragg grating sensors are equipped with dedicated flexible adhesive substrates and protective coatings. Both types of fiber Bragg grating sensors are internally treated with electromagnetic shielding, and the front-end demodulation equipment provides double anti-interference. All stress grating sensors and temperature grating sensors are wired in a series network configuration. The electrical signal data demodulated by the signal demodulation conversion module is uniformly uploaded to the ground-based host computer monitoring platform and data processing center via base station wireless transmission to ensure real-time and stable data transmission. The data parsing and feature extraction module relies on dedicated processing software and algorithms to perform calculations and analysis on the received data, eliminate interference data, extract core feature parameters that can reflect the structural state of the blade, and at the same time use temperature sensor data to eliminate monitoring errors caused by the cross-sensitivity of fiber optic grating temperature stress. The status assessment module combines the original design parameters of the blades and long-term historical operating data with static, modal, and fatigue analysis methods, supplemented by big data and artificial intelligence algorithms, to comprehensively determine the overall structural health status of the blades. A single wind turbine contains three blades, and each blade is equipped with 6 stress grating sensors and 2 temperature grating sensors. The entire unit is equipped with a total of 24 fiber optic grating sensors. The system is set to a monitoring sampling frequency of 100Hz, which can be flexibly adjusted according to the blade size, structural stiffness, and natural frequency to fully capture dynamic monitoring information such as blade vibration and deformation. The monitoring and output module outputs blade health assessment results, enabling all-weather, all-round monitoring of the wind turbine blade's operating status, providing a basis for wind turbine operation and maintenance, safety management and performance optimization.

[0022] The method for implementing health monitoring includes the following steps: S1: In the key areas of the root, middle and tip of the wind turbine blade, stress, strain and ambient temperature physical parameters of the blade under the operating state are collected synchronously in the partitioned areas by surface-mounted stress grating sensors and temperature grating sensors. The mechanical and temperature quantities are converted into optical signals and output in real time. In step S1, the temperature grating sensor is independently deployed and is not affected by the blade stress, which is used to compensate for the cross-sensitivity problem of fiber optic grating temperature and stress. S2: The embedded fiber optic grating demodulation device inside the hub receives multiple optical signals, completes channel-by-channel analysis and demodulation, converts the optical signals into standard digital electrical signals, and simultaneously completes preliminary noise reduction processing such as front-end filtering and baseline correction to suppress electromagnetic and mechanical vibration interference on site. S3: Pack and encapsulate the pre-processed multi-channel monitoring data, establish a wireless transmission link through the base station, and upload it to the ground host computer monitoring platform and data processing center in batches according to the set time sequence. The interruption resume mechanism is adopted to ensure the integrity and continuity of data transmission. In step S3, the data transmission process adopts data packaging, time-sharing transmission and interruption resume mechanism to ensure the integrity and continuity of data transmission. S4: In-depth data analysis, error correction, and comprehensive evaluation. First, the received data undergoes secondary noise reduction and outlier removal to extract core characteristic parameters that characterize blade deformation and stress state. Then, using measured data from temperature sensors, systematic errors caused by cross-sensitivity of fiber Bragg grating temperature and stress are accurately eliminated. Combining the blade's factory design parameters and historical operating data throughout its entire life cycle, static analysis, modal analysis, and fatigue analysis are carried out sequentially. Big data and artificial intelligence algorithms are introduced to perform multi-model fusion calculations to comprehensively determine the current structural health level and damage risk of the blade. S5: Displays blade health status, stress data, and risk level in real time in a visual format, and generates monitoring logs and health assessment reports simultaneously. The system enables 24 / 7 online monitoring of blades, triggers prompts immediately upon identifying abnormal conditions, and provides continuous data support for wind turbine operation and maintenance scheduling, on-site safety management, and unit performance optimization.

[0023] In summary, the system uses surface-mounted fiber Bragg grating sensors as the sensing front end, deployed in key stress areas of the wind turbine blades, to convert physical quantities such as blade mechanical deformation and ambient temperature into optical signals. Then, the embedded demodulation equipment in the hub completes photoelectric conversion and front-end preprocessing, and transmits the data back to the ground platform via a wireless link. The platform combines algorithms to complete noise reduction, error compensation, and feature extraction, and with static, modal, fatigue analysis, and artificial intelligence models, comprehensively evaluates the health status of the blade structure. Finally, the results are displayed in a visual way and anomaly alarms are triggered, forming a closed-loop monitoring system of sensing, demodulation, transmission, analysis, evaluation, and early warning. Each wind turbine has three blades as the monitoring target. Six stress grating sensors are deployed on each blade, arranged in a three-vertical, three-horizontal pattern, every 2 meters from the blade root to the tip, with a focus on the blade root (a high-risk area for failure). These sensors capture the deformation and strain caused by axial and lateral forces during blade operation. Each blade is also equipped with two independently deployed temperature grating sensors. These sensors are unaffected by the mechanical stress on the blade and only collect the temperature of the blade surface and surrounding environment. The entire turbine unit uses a total of 24 sensors, achieving full coverage monitoring of key points across the entire blade area. When the blade undergoes tensile or compressive deformation under wind force, [the following should be noted]. When the grating period of a fiber optic grating changes, the center wavelength of its reflected light shifts accordingly. The wavelength shift is linearly related to the blade strain and stress magnitude. Changes in ambient temperature also cause wavelength drift in the fiber optic grating. A temperature sensor separately collects temperature data for subsequent error correction. The sensor converts the mechanical deformation of the blade and the ambient temperature into a unified optical signal with characteristic information and outputs it continuously. The sensor, combined with a protective coating and a flexible adhesive substrate, can resist the erosion of Gobi desert winds and sand, coastal salt spray, and high-altitude ice and snow. It also has an integrated anti-electromagnetic shielding structure to avoid electromagnetic interference from wind farm frequency converters and power transmission equipment. To mitigate strong electromagnetic interference and ensure stable optical signal transmission, this module, an embedded fiber Bragg grating demodulation system integrated inside the wind turbine hub, performs optical-to-electrical signal conversion and front-end preprocessing. Replacing traditional bulky acquisition equipment, it features small size, portability, and fast response. The demodulation device emits broadband incident light to each branch fiber Bragg grating sensor and receives the characteristic reflected light. Through beam splitting, filtering, and spectral analysis, it identifies the wavelength offset of the reflected light from different sensors, completing the multi-channel optical signal splitting and analysis. This accurately converts the optical signal carrying stress, strain, and temperature information into standard digital signals. The system simultaneously performs front-end filtering and baseline correction to filter out interference signals caused by wind turbine mechanical vibration and equipment noise, completing the initial data purification. The system is uniformly set to a 100Hz sampling frequency, and combined with blade size, structural stiffness and natural frequency, it fully captures high-frequency information such as dynamic vibration and instantaneous deformation of the blades. The sampling frequency can be flexibly adjusted according to actual working conditions. The pre-processed digital electrical signal data is packaged and encapsulated in a fixed format, and a wireless transmission link is built based on the wind farm base station. It works by using time-division transmission and breakpoint resume mechanism: the system uploads data packets in batches according to a predetermined time sequence to avoid data congestion.If signal interruption or network fluctuations occur during transmission, the device will automatically record the transmission node and resume transmission after the network is restored, ensuring the integrity, continuity, and real-time nature of the monitoring data. All data is ultimately aggregated to the ground-based host computer monitoring platform and data processing center. The ground platform is equipped with dedicated processing software and algorithms to perform secondary fine processing on the transmitted data, eliminating system errors and extracting valid state parameters. Secondary noise reduction and outlier removal are performed on the original electrical signal data, further eliminating invalid data caused by transmission interference and transient environmental disturbances to ensure data authenticity. Since fiber optic gratings themselves have a temperature-stress cross-sensitivity defect, temperature changes can interfere with stress monitoring results. The module retrieves synchronously acquired temperature sensor data and calculates the temperature-induced wavelength shift using a calibration algorithm, removing it from the stress grating data. In addition to eliminating temperature interference components, the system accurately corrects monitoring errors, significantly improving the detection accuracy of stress and strain data. Based on computational analysis, it extracts core characteristic parameters from the corrected massive data that can directly characterize the degree of blade deformation, stress distribution, and vibration amplitude, providing data support for subsequent health assessments. Combining the blade's original design parameters, material properties, and manufacturing process, static analysis, modal analysis, and fatigue analysis are carried out sequentially: static analysis determines whether the blade's static stress exceeds the design threshold; modal analysis identifies the blade's inherent vibration characteristics and identifies resonance risks; fatigue analysis, combined with long-term operating data, assesses the cumulative fatigue damage of the blade under repeated loading. Big data and artificial intelligence algorithms are introduced to train models using historical operating data and past fault data throughout the blade's entire life cycle, achieving multi-model fusion calculation. The system comprehensively assesses the current location, extent, and health level of blade damage, while predicting the development trends of defects such as fatigue cracks, skin bulging, and root cracking. The monitoring output module visualizes real-time stress, strain, temperature, and vibration data, along with health level and damage risk assessments, in charts and configuration windows, enabling 24 / 7 online monitoring. The system presets multiple fault thresholds to differentiate between different risk levels, including minor damage, moderate defects, and major hazards. When monitoring data exceeds the threshold or an abnormal state is detected, it automatically triggers audible and visual alarms and pop-up alerts, marking the fault location and type. The system automatically stores all monitoring data and alarm records in real time, generating monitoring logs and periodic health assessment reports. All data provides continuous and reliable support for daily wind turbine operation and maintenance scheduling, on-site safety management, unit performance optimization, and overhaul work.

[0024] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A surface-mount fiber optic grating structure health monitoring system for wind turbine blades, characterized in that: It includes a signal acquisition module, a signal demodulation and conversion module, a data parsing and feature extraction module, a status assessment module, and a monitoring output module; The signal acquisition module is an embedded fiber optic demodulation system located in the wind turbine hub. It receives the optical signals transmitted by the sensor, performs optical signal analysis, converts them into recognizable electrical signals, and completes signal form conversion and preliminary processing. The electrical signal data demodulated by the signal demodulation conversion module is uniformly uploaded to the ground-based host computer monitoring platform and data processing center via base station wireless transmission to ensure real-time and stable data transmission. The data parsing and feature extraction module relies on dedicated processing software and algorithms to perform calculations and analysis on the received data, eliminate interference data, extract core feature parameters that can reflect the structural state of the blade, and at the same time use temperature sensor data to eliminate monitoring errors caused by the cross-sensitivity of fiber optic grating temperature stress. The condition assessment module combines the original design parameters of the blade, long-term historical operating data, and methods such as statics, modal analysis, and fatigue analysis, supplemented by big data and artificial intelligence algorithms, to comprehensively determine the overall structural health status of the blade. The monitoring output module outputs blade health assessment results, enabling all-weather, all-round monitoring of the wind turbine blade's operating status, providing a basis for wind turbine operation and maintenance, safety management and performance optimization.

2. The surface-mount fiber optic grating structure health monitoring system for wind turbine blades according to claim 1, characterized in that: The method for implementing health monitoring includes the following steps: S1: In the key areas of the root, middle and tip of the wind turbine blade, the stress, strain and ambient temperature physical parameters of the blade under the operating state are collected synchronously in the partitioned areas by surface-mounted stress grating sensors and temperature grating sensors. The mechanical and temperature quantities are uniformly converted into optical signals and output in real time. S2: The embedded fiber optic grating demodulation device inside the hub receives multiple optical signals, completes channel-by-channel analysis and demodulation, converts the optical signals into standard digital electrical signals, and simultaneously completes preliminary noise reduction processing such as front-end filtering and baseline correction to suppress electromagnetic and mechanical vibration interference on site. S3: Package and encapsulate the pre-processed multi-channel monitoring data, establish a wireless transmission link through the base station, and upload it to the ground host computer monitoring platform and data processing center in batches according to the set time sequence. The breakpoint resume mechanism is used to ensure the integrity and continuity of data transmission. S4: In-depth data analysis, error correction, and comprehensive evaluation. First, the received data undergoes secondary noise reduction and outlier removal to extract core characteristic parameters that characterize blade deformation and stress state. Then, using measured data from temperature sensors, systematic errors caused by cross-sensitivity of fiber Bragg grating temperature and stress are accurately eliminated. Combining the blade's factory design parameters and historical operating data throughout its entire life cycle, static analysis, modal analysis, and fatigue analysis are carried out sequentially. Big data and artificial intelligence algorithms are introduced to perform multi-model fusion calculations to comprehensively determine the current structural health level and damage risk of the blade. S5: Displays blade health status, stress data, and risk level in real time in a visual format, and generates monitoring logs and health assessment reports simultaneously. The system enables 24 / 7 online monitoring of blades, triggers prompts immediately upon identifying abnormal conditions, and provides continuous data support for wind turbine operation and maintenance scheduling, on-site safety management, and unit performance optimization.

3. The surface-mount fiber optic grating structure health monitoring system for wind turbine blades according to claim 1, characterized in that: The signal acquisition module includes two types of fiber Bragg grating sensors: stress grating sensors and temperature grating sensors.

4. The surface-mount fiber optic grating structure health monitoring system for wind turbine blades according to claim 1, characterized in that: Each wind turbine has three blades, and each blade is equipped with 6 stress grating sensors and 2 temperature grating sensors. The entire unit is equipped with a total of 24 fiber optic grating sensors.

5. The surface-mount fiber optic grating structure health monitoring system for wind turbine blades according to claim 1, characterized in that: The stress grating sensor is arranged in a three-vertical and three-horizontal pattern, laid out sequentially at two-meter intervals along the blade root towards the blade tip.

6. The surface-mount fiber optic grating structure health monitoring system for wind turbine blades according to claim 1, characterized in that: The stress grating sensor is mainly arranged in the root region of the blade to detect the axial and lateral forces acting on the blade during operation.

7. The surface-mount fiber optic grating structure health monitoring system for wind turbine blades according to claim 1, characterized in that: The system is set to a monitoring sampling frequency of 100Hz, which can be flexibly adjusted according to the blade size, structural stiffness and natural frequency to fully capture dynamic monitoring information such as blade vibration and deformation.

8. The surface-mount fiber optic grating structure health monitoring system for wind turbine blades according to claim 2, characterized in that: In step S1, the temperature grating sensor is independently deployed and is not affected by blade stress, which is used to compensate for the cross-sensitivity problem of fiber optic grating temperature stress.

9. A surface-mount fiber optic grating structure health monitoring system for wind turbine blades according to claim 2, characterized in that: In step S3, the data transmission process employs data packaging, time-sharing transmission, and breakpoint resumption mechanisms to ensure the integrity and continuity of data transmission.

10. A surface-mount fiber optic grating structure health monitoring system for wind turbine blades according to claim 3, characterized in that: Both types of fiber Bragg grating sensors are equipped with a dedicated flexible adhesive substrate and protective coating. Both types of fiber Bragg grating sensors are internally treated with electromagnetic shielding and have dual anti-interference capabilities in conjunction with the front-end demodulation equipment. All stress grating sensors and temperature grating sensors are wired in a series network configuration.

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