Composite material curved surface impact positioning method based on optical fiber sensor

Abstract

The invention discloses a composite material curved surface impact positioning method based on optical fiber sensors, and belongs to the field of composite material structure monitoring, and the method comprises the steps: embedding or attaching a plurality of optical fiber sensors in a composite material curved surface structure, selecting a fiber bragg grating sensor, and carrying out the design of a composite material. By means of finite element analysis software and in combination with engineering experience, key areas such as connection parts, load concentration areas and the like and possibly damaged areas are accurately determined, optimized layout is carried out according to area characteristics and mechanical requirements, two-dimensional or three-dimensional latticed distribution is formed, and full-coverage monitoring of the curved surface is achieved. According to the invention, the fiber Bragg grating sensors are optimally arranged in key and vulnerable areas of a composite material curved surface to form two-dimensional or three-dimensional latticed distribution, and special flexible packaging and an advanced manufacturing process are matched to adapt to a complex curved surface, realize full-coverage monitoring, avoid a monitoring blind area of a traditional sensor and accurately capture impact information.

Description

Technical Field

[0001] The present invention relates to the technical field of composite material structure monitoring, and in particular to a composite material curved surface impact positioning method based on an optical fiber sensor. Background Art

[0002] In modern industry, composite materials, with their high strength, low density, and excellent corrosion resistance, have played a vital role in structural design in key industries such as aerospace, automotive manufacturing, and shipbuilding. Composite curved structures, in particular, have become a core design direction in these industries due to their unique lightweight advantages, complex and diverse morphologies, and strong load-bearing capacity.

[0003] Traditional impact location methods mainly rely on conventional equipment such as accelerometers and pressure sensors. However, these traditional methods expose many difficult-to-overcome problems when applied to composite curved surface structures. First, it is extremely difficult to deploy sensors on complex curved surfaces. Due to the irregular shape and special mechanical properties of the curved surface, it is difficult to achieve comprehensive and reasonable coverage of sensors, which inevitably creates monitoring blind spots, resulting in some impact information not being effectively captured. Secondly, the response speed of traditional sensors is relatively slow, and they are unable to capture key information in a very short time at the moment of impact, resulting in serious lags in the initial judgment and processing of impact events. Finally, the monitoring accuracy of traditional methods is insufficient, making it difficult to accurately locate the impact position, and there are also large errors in the assessment of the degree of damage, which seriously affects the accurate judgment of the health status of composite structures and the formulation of subsequent maintenance decisions.

[0004] Therefore, a new, efficient and accurate impact positioning method for composite surface is needed. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for locating impacts on composite surfaces using fiber optic sensors. By leveraging the distributed sensing characteristics, extremely high response speed, relatively low cost, and superior high precision of fiber optic sensors, this method significantly improves the detection accuracy of composite surfaces under impact loads, enabling real-time response to impact events and accurately locating the impact location. This method provides strong technical support for the health monitoring and safety maintenance of composite structures.

[0006] Technical solution: To solve the above technical problems, according to one aspect of the present invention, more specifically, a composite material curved surface impact positioning method based on an optical fiber sensor comprises the following steps:

[0007] S1. Embed or attach multiple fiber optic sensors within the composite material's curved surface structure, using fiber Bragg grating sensors. During the composite material design phase, using finite element analysis software and engineering experience, accurately identify key areas such as connection points, load concentration areas, and potentially damaged areas. Optimize their layout based on regional characteristics and mechanical requirements, forming a two-dimensional or three-dimensional grid distribution to achieve full coverage monitoring of the curved surface. The fiber optic sensors utilize special flexible packaging materials and advanced manufacturing processes to adapt to various surfaces with complex curvatures.

[0008] S2. Through the distributed sensing capabilities of fiber optic sensors, the wavelength changes of fiber Bragg grating reflections are used to monitor strain, pressure, and temperature changes at multiple locations on the composite surface in real time. The sensors have high wavelength resolution, enabling timely detection of impact events at the microsecond level and simultaneous multi-point monitoring. The error in monitoring time between each sensor is kept to a very small range.

[0009] S3, using efficient intelligent algorithms, combined with machine learning and data fusion technology, to process and analyze sensor data in real time, using multi-point signal time difference analysis technology based on cross-correlation function and deep learning to accurately measure the time difference of each sensor receiving the impact signal: When using continuous time signal, the cross-correlation function R of the signal received by sensor i and sensor j is: ij (τ) is calculated using the following integral formula:

[0010]

[0011] In practical applications, since the signal is a discrete time series after sampling, the cross-correlation function R ij (k) is calculated by the following summation formula:

[0012]

[0013] The preliminary time difference Δ is obtained by finding the time delay corresponding to the maximum value of the cross-correlation function The cross-correlation function results of multiple sensor pairs are then used as the input of the convolutional neural network (CNN), processed by the convolution layer, pooling layer and fully connected layer, and the optimized time difference Δt is output through the regression layer. ij , to accurately calculate the impact source location; combined with machine learning models to identify impact intensity and damage distribution, it can distinguish different degrees of microcracks and interlayer delamination damage, and predict the possible damage process of the material based on material performance parameters and historical monitoring data;

[0014] S4. Integrate IoT technology to implement an adaptive feedback and early warning mechanism. The system pre-sets thresholds for parameters such as strain, pressure, and temperature based on the mechanical properties of composite materials and the operating environment through experiments and theoretical calculations. When an abnormal signal exceeding the threshold is detected, an early warning is automatically initiated, providing maintenance personnel with the impact source location coordinates and a detailed damage assessment report. Based on the damage assessment results, the system uses an expert system and intelligent decision-making algorithms to intelligently generate repair suggestions and transmit them to maintenance personnel.

[0015] Furthermore, in the fiber optic sensor deployment step, for two-dimensional grids, the sensors are evenly spaced along the longitude and latitude directions of the curved surface, and the spacing is determined based on the mechanical properties of the composite material and the expected monitoring accuracy; for three-dimensional grids, the sensors are layered in the thickness direction of the curved surface, and the layer spacing is determined based on actual needs.

[0016] Furthermore, the data acquisition system uses high-speed sampling technology, and the sampling frequency is set according to the actual monitoring needs and sensor response characteristics. Data transmission uses fiber optic communication lines or high-speed Ethernet, and data encryption and error correction technology are used to ensure data security and integrity.

[0017] Furthermore, in system integration and optimization operations, standardized data interfaces and communication protocols are used to achieve seamless connection with existing structural health monitoring systems (SHM) or digital twin technologies. The system can optimize sensor positions or flexibly increase the number of sensors through simulation technology according to monitoring needs. When the number of sensors is increased, the system automatically identifies and configures new sensors.

[0018] Furthermore, in the process of intelligent data processing, machine learning models use technologies such as neural networks and support vector machines, and use methods such as cross-validation and regularization to improve model accuracy and generalization capabilities.

[0019] Furthermore, the optical fiber sensor can maintain stable performance in harsh environments, and the wavelength drift and measurement accuracy changes are controlled within a reasonable range.

[0020] Furthermore, the multi-sensor data fusion platform has data pre-processing capabilities, which can perform denoising and filtering on different types of sensor data, and control data transmission delay within a reasonable range.

[0021] The beneficial effects of the composite material curved surface impact positioning method based on optical fiber sensor of the present invention are:

[0022] (1) The present invention optimizes the layout of fiber Bragg grating sensors in key and vulnerable areas of composite material surfaces to form a two-dimensional or three-dimensional grid distribution. Combined with special flexible packaging and advanced manufacturing processes, it adapts to complex surfaces and achieves full coverage monitoring, avoiding the monitoring blind spots of traditional sensors and accurately capturing impact information.

[0023] (2) The present invention utilizes the distributed sensing characteristics and high wavelength resolution of optical fiber sensors to detect impact events in a timely manner at the microsecond level. Through multi-point synchronous monitoring and high-precision time synchronization, it accurately identifies the shock wave propagation path and affected area. Combined with multi-point signal time difference or wave velocity analysis technology, it accurately calculates the location of the impact source, greatly improving positioning accuracy.

[0024] (3) The present invention uses machine learning and data fusion technology to deeply analyze sensor data, identify impact intensity and damage distribution, distinguish different degrees of microcracks and interlaminar delamination damage, and predict the damage process based on material performance parameters and historical monitoring data, providing strong support for structural health monitoring and helping to formulate reasonable maintenance strategies.

[0025] (4) The present invention combines the Internet of Things technology to establish an adaptive feedback and early warning mechanism, sets parameter thresholds according to the mechanical properties of composite materials and the use environment, automatically issues early warnings when an abnormality occurs, provides reports on the location of the impact source and the degree of damage, and uses expert systems and intelligent decision-making algorithms to generate repair suggestions and transmit them in real time, facilitating rapid processing of damaged areas and reducing the impact of structural damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0027] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0029] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] Reference Figure 1 A composite material curved surface impact positioning method based on an optical fiber sensor comprises the following steps:

[0031] S1. Embed or attach multiple fiber optic sensors within the composite material's curved surface structure, with fiber Bragg grating (FBG) sensors selected as the core sensing elements. This sensor can keenly sense the dynamic changes in strain, pressure, and temperature within the composite material through subtle changes in its reflected wavelength. In the early stages of composite material design, advanced finite element analysis software and extensive engineering experience are used to accurately determine key areas, such as connection points, load concentration areas, and areas that may be damaged. Then, based on the specific characteristics and mechanical requirements of these areas, careful optimization and layout are performed to form a scientifically reasonable two-dimensional or three-dimensional grid distribution of sensors on the composite material's curved surface, thereby ensuring that the entire curved surface area can be fully and comprehensively monitored. Fiber optic sensors use special flexible packaging materials and advanced manufacturing processes, making them extremely flexible and able to easily adapt to complex curved surfaces of various curvatures, fundamentally avoiding the problem of monitoring blind spots caused by improper layout of traditional sensors.

[0032] S2. Through the distributed sensing capabilities of fiber optic sensors, the wavelength changes of fiber Bragg grating reflections are used to monitor strain, pressure, and temperature changes at multiple locations on the composite surface in real time. The sensors have high wavelength resolution, enabling timely detection of impact events at the microsecond level and simultaneous multi-point monitoring. The error in monitoring time between each sensor is kept to a very small range.

[0033] When an impact load acts on a composite material, the fiber Bragg gratings in the fiber optic sensor can quickly sense the changes in strain, pressure, and temperature caused by the impact, which in turn cause a corresponding shift in its reflected wavelength. The sensor accurately captures minute deformations at each monitoring point and transmits these changes in real time to the data acquisition system via a high-speed, stable data transmission link. Its extremely fast response speed allows it to keenly capture instantaneous changes in impact force within microseconds, enabling timely and accurate detection of impact events. Furthermore, through carefully designed multi-point synchronous monitoring technology and the use of high-precision time synchronization devices, the monitoring time error between each sensor is kept to a very small range. This allows the shock wave's propagation path in the composite material and its affected area to be accurately identified, providing rich and accurate raw information for subsequent data analysis and processing.

[0034] S3. Use efficient intelligent algorithms, combined with machine learning and data fusion technology, to process and analyze sensor data in real time, achieving accurate calculation of impact source location and damage assessment:

[0035] In impact monitoring of composite curved surfaces, effective processing and analysis of sensor data is key to accurately locating the impact source and assessing damage. This step utilizes a combination of advanced technologies to deeply mine sensor data to provide reliable structural health information.

[0036] Accurate calculation of impact source location

[0037] To precisely locate the impact source within a composite curved surface, this method employs two complementary techniques: multi-point signal time-of-day analysis and wave velocity analysis. These two techniques each have their own advantages, and their combined use significantly improves location accuracy.

[0038] Multi-point signal time difference analysis technology

[0039] The technology calculates the location of the impact source based on the time difference between the impact signals received by sensors at different locations. When a composite material is impacted, the elastic waves generated will propagate in all directions, and sensors at different locations will detect this signal at different times.

[0040] Signal preprocessing: The raw signals collected by the sensor may contain noise and interference, so they must first be preprocessed. Wavelet transforms are used to denoise the signal. Wavelet transforms offer multi-resolution analysis capabilities, allowing them to decompose signals at different scales, effectively removing high-frequency noise while preserving the signal's key features. For example, for a noisy strain signal, the appropriate wavelet basis and number of decomposition levels can be selected to decompose the signal into sub-signals of varying frequencies. The high-frequency sub-signals are then thresholded to remove noise components, and the signal is reconstructed to achieve the denoised result.

[0041] Cross-correlation function calculation: For continuous time signals, the cross-correlation function R of the signals received by sensor i and sensor j is ij (τ) is calculated using the following integral formula:

[0042]

[0043] In practical applications, since the signal is a discrete time series after sampling, the cross-correlation function R ij (k) is calculated by the following summation formula:

[0044]

[0045] The preliminary time difference Δ is obtained by finding the time delay corresponding to the maximum value of the cross-correlation function The cross-correlation function results of multiple sensor pairs are then used as the input of the convolutional neural network (CNN), processed by the convolution layer, pooling layer and fully connected layer, and the optimized time difference Δt is output through the regression layer. ij , to accurately calculate the location of the impact source; combined with the machine learning model to identify the impact intensity and damage distribution, it can distinguish between different degrees of microcracks and interlayer delamination damage, and predict the possible damage process of the material based on material performance parameters and historical monitoring data.

[0046] S4. Integrate IoT technology to implement an adaptive feedback and early warning mechanism. The system pre-sets thresholds for parameters such as strain, pressure, and temperature based on the mechanical properties of composite materials and the operating environment through experiments and theoretical calculations. When an abnormal signal exceeding the threshold is detected, an early warning is automatically initiated, providing maintenance personnel with the impact source location coordinates and a detailed damage assessment report. Based on the damage assessment results, the system uses an expert system and intelligent decision-making algorithms to intelligently generate repair suggestions and transmit them to maintenance personnel.

[0047] (Preliminary setting of reasonable thresholds for parameters such as strain, pressure, and temperature based on factors such as the mechanical properties of composite materials, the use environment, and safety standards, through a large amount of experimental data and theoretical calculations;

[0048] When abnormal strain changes or damage signals exceeding the predetermined threshold are found during monitoring, the system will automatically start the early warning program immediately;

[0049] Using IoT technology, maintenance personnel are promptly alerted, providing precise impact source location coordinates and a detailed damage assessment report, including key information such as damage type, damage range, and damage depth. Furthermore, based on the damage assessment results, the system leverages expert systems and intelligent decision-making algorithms to intelligently generate targeted repair recommendations, including recommended repair methods, a list of required materials and tools, and detailed steps. This data is then transmitted to maintenance personnel in real time, helping them quickly and accurately locate and address damaged areas, minimizing the impact of structural damage.

[0050] Preferably, in the fiber optic sensor layout step, for a two-dimensional grid, the sensors are evenly spaced along the longitude and latitude directions of the curved surface, and the spacing is determined based on the mechanical properties of the composite material and the expected monitoring accuracy; for a three-dimensional grid, the sensors are layered in the thickness direction of the curved surface, and the layer spacing is determined based on actual needs.

[0051] Preferably, the data acquisition system adopts high-speed sampling technology, the sampling frequency is set according to the actual monitoring needs and sensor response characteristics, data transmission adopts optical fiber communication lines or high-speed Ethernet, and uses data encryption and error correction technology to ensure data security and integrity.

[0052] Preferably, during system integration and optimization, the fiber optic sensor hardware system is deeply integrated with the intelligent data processing system and actively combined with the existing structural health monitoring system (SHM) or advanced digital twin technology to build a powerful multi-sensor data fusion platform. In this platform, through advanced data fusion algorithms, the data collected by the fiber optic sensor is comprehensively analyzed with the data collected by other types of sensors, such as accelerometers, temperature sensors, strain gauges, etc., giving full play to the advantages of different types of sensors, complementing and verifying each other, thereby improving the overall accuracy and robustness of the system. In addition, the system is designed with full consideration of scalability and modularity, and adopts standardized data interfaces and communication protocols, so that the system can conveniently and flexibly increase the number of sensors or optimize the sensor positions according to actual monitoring needs. When increasing the number of sensors, the system can automatically identify and configure the newly connected sensors to ensure their seamless integration with the original system; when optimizing the sensor position, the sensor layout schemes at different positions are evaluated and compared through simulation technology to determine the optimal sensor position to meet the monitoring needs of composite materials of different types and structures, providing a strong guarantee for long-term and reliable monitoring of composite materials.

[0053] Preferably, in the process of intelligent data processing, the machine learning model adopts technologies such as neural networks and support vector machines, and uses methods such as cross-validation and regularization to improve the accuracy and generalization ability of the model.

[0054] Preferably, the optical fiber sensor can maintain stable performance in harsh environments (such as high temperature, high humidity, and strong electromagnetic interference), and the wavelength drift and measurement accuracy changes are controlled within a reasonable range.

[0055] Preferably, the multi-sensor data fusion platform has a data preprocessing function, which can perform denoising, filtering and other processing on different types of sensor data, and control the data transmission delay within a reasonable range.

[0056] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A composite material curved surface impact positioning method based on optical fiber sensor, characterized in that: The following steps are involved: S1. Embed or attach multiple fiber optic sensors within the composite material's curved surface structure, using fiber Bragg grating sensors. During the composite material design phase, using finite element analysis software and engineering experience, accurately determine connection locations, critical load concentration areas, and potentially damaged areas. Optimize their layout based on regional characteristics and mechanical requirements, forming a two-dimensional or three-dimensional grid distribution to achieve full coverage monitoring of the curved surface. The fiber optic sensors utilize special flexible packaging materials and advanced manufacturing processes to adapt to various surfaces with complex curvatures. S2. Through the distributed sensing capabilities of fiber optic sensors, the wavelength changes of fiber Bragg grating reflections are used to monitor strain, pressure, and temperature changes at multiple locations on the composite surface in real time. The sensors have high wavelength resolution, enabling timely detection of impact events at the microsecond level and simultaneous multi-point monitoring. The error in monitoring time between each sensor is kept to a very small range. S3, using efficient intelligent algorithms, combined with machine learning and data fusion technology, to process and analyze sensor data in real time, using multi-point signal time difference analysis technology based on cross-correlation function and deep learning to accurately measure the time difference of each sensor receiving the impact signal: When using continuous time signal, the cross-correlation function R of the signal received by sensor i and sensor j is: ij (τ) is calculated using the following integral formula: In practical applications, since the signal is a discrete time series after sampling, the cross-correlation function R ij (k) is calculated by the following summation formula: The preliminary time difference Δ is obtained by finding the time delay corresponding to the maximum value of the cross-correlation function The cross-correlation function results of multiple sensor pairs are then used as the input of the convolutional neural network (CNN), processed by the convolution layer, pooling layer and fully connected layer, and the optimized time difference Δt is output through the regression layer. ij , to accurately calculate the impact source location; combined with machine learning models to identify impact intensity and damage distribution, it can distinguish different degrees of microcracks and interlayer delamination damage, and predict the possible damage process of the material based on material performance parameters and historical monitoring data; S4. Combining Internet of Things technology to realize adaptive feedback and early warning mechanism, the system pre-sets strain, pressure, and temperature parameter thresholds based on the mechanical properties of composite materials and the use environment through experiments and theoretical calculations. When an abnormal signal exceeding the threshold is detected, the system automatically starts the early warning and provides the maintenance personnel with the impact source location coordinates and a detailed damage assessment report. Based on the damage assessment results, the system uses expert systems and intelligent decision-making algorithms to intelligently generate repair suggestions and transmit them to the maintenance personnel.

2. The composite material curved surface impact positioning method based on optical fiber sensor according to claim 1, characterized in that: In the fiber optic sensor deployment step, for a two-dimensional grid, the sensors are evenly spaced along the longitude and latitude directions of the curved surface, and the spacing is determined based on the mechanical properties of the composite material and the expected monitoring accuracy; for a three-dimensional grid, the sensors are layered in the thickness direction of the curved surface.

3. The composite material curved surface impact positioning method based on optical fiber sensor according to claim 1, characterized in that: The data acquisition system adopts high-speed sampling technology. The sampling frequency is set according to the actual monitoring needs and sensor response characteristics. Data transmission adopts optical fiber communication lines or high-speed Ethernet, and uses data encryption and error correction technology to ensure data security and integrity.

4. The composite material curved surface impact positioning method based on optical fiber sensor according to claim 1, characterized in that: In system integration and optimization operations, standardized data interfaces and communication protocols are used to achieve seamless connection with existing structural health monitoring systems or digital twin technologies. According to monitoring requirements, simulation technology is used to optimize sensor positions or flexibly increase the number of sensors. When the number of sensors is increased, new sensors are automatically identified and configured.

5. The composite material curved surface impact positioning method based on optical fiber sensor according to claim 1, characterized in that: During the intelligent data processing process, the machine learning model adopts neural network and support vector machine technology, and uses cross-validation and regularization methods to improve model accuracy and generalization ability.

6. The composite material curved surface impact positioning method based on optical fiber sensor according to claim 1, characterized in that: The multi-sensor data fusion platform has the function of data preprocessing, which can denoise and filter the data of different types of sensors, and control the data transmission delay within a reasonable range.

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