Structure monitoring optical fiber sensing optimization arrangement method

Abstract

The invention discloses a structure monitoring optical fiber sensing multi-target optimization arrangement method. The method comprises the following steps: S1, determining optimal sensor number selection by adopting a modular attitude measurement function; and S2, performing sensor multi-target optimization arrangement in combination with the sensor coverage model, the weight factor and the sensing overlapping cost function model. According to the technical scheme, the problems that monitoring coverage is redundant and overall deformation of the structure cannot be accurately reflected due to the fact that the number and layout distribution of the sensors are uneven are solved, meanwhile, a vision measurement system is combined, monitoring errors caused by a traditional marking positioning mode are reduced, and therefore the arrangement efficiency and positioning precision of the sensors are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of structural deformation monitoring, and in particular relates to an optimized layout method for fiber optic sensors for structural monitoring. Background Art

[0002] With the development of large composite material assembly fields such as aerospace, structural health monitoring throughout the product life cycle plays an important role in ensuring the overall performance of the structure. During the assembly process of large composite products, due to the influence of loads such as stress and impact, the structure deforms. As an important part of health monitoring, structural deformation monitoring installs sensors on the surface or inside, and based on the online data collected by the sensors, structural health identification is carried out to achieve the evaluation of the structural safety performance. As the basis for obtaining structural health monitoring information, the location layout of sensors will have a profound impact on the effective acquisition of structural information. Ideally, increasing the number of sensors will improve the ability to perceive structural detail information. However, during the assembly process of large composite structures such as aerospace, due to factors such as a large number of components, complex assembly environments, and data maintenance, the number of sensors is usually limited. Therefore, based on a limited number of sensors, through the optimization of their positions, it is of great significance for the development of structural health monitoring to collect structural state data with maximum efficiency.

[0003] Currently, with the development of intelligent optimization algorithms, many studies optimize the sensor positions based on intelligent algorithms. When using a single objective function for optimization, the number and angular distribution of sensors are often ignored, resulting in excessive coverage overlap during the sensor optimization process; due to the complex and multi-objective characteristics of practical problems, the applicability of the single-objective optimization method for sensor optimization research is reduced, and multi-objective optimization gradually plays a dominant role in sensor layout. Although the multi-objective optimization algorithms currently used have improved the problem of the number of sensors, there is still a large gap between the optimized positions and the constraints in the actual application process, and problems such as insufficient modal coverage are likely to occur. At the same time, there are problems such as a decrease in the sensing sensitivity in key areas leading to missing sensing monitoring data. Therefore, it is necessary to combine the sensing range of the sensors, the overall change state of the structure, and the sensor angles to ensure the overall identifiability of structural monitoring on the premise of meeting the lightweight of the sensors. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an optimized layout method for fiber optic sensors for structural monitoring, which solves the problems that uneven distribution of the number and layout of sensors leads to redundant monitoring coverage and inability to accurately reflect the overall deformation of the structure. On the premise of meeting the lightweight of the sensors, combined with the overall change trend of the structure, weight distribution is carried out according to different regions, and the sensor layout angles are adjusted in combination with the sensing range to improve the overall utilization rate of the sensors, ensure the monitoring of key areas of the structure, and achieve global effective monitoring of the structure to be measured.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] An optimized layout method for structural monitoring fiber optic sensors, comprising:

[0007] Step S1, determining the selection of the optimal number of sensors by using a modal metric function;

[0008] Step S2, performing multi-objective optimization layout of sensors by combining a sensor coverage model, a weight factor, and a perception overlap cost function model.

[0009] Preferably, in step S1, seven modal metric functions are selected, namely effective independence, Fisher information matrix, information entropy, kinetic energy, average driving point residual, eigenvalue vector product, and modal confidence; taking one of the seven modal metric functions and the number of sensors as the optimization objectives, analyzing the Pareto solutions obtained between the modal function and the number of sensors, and determining the selection of the optimal number of sensors.

[0010] Preferably, the optimal modal function is selected based on the Hypervolume index.

[0011] Preferably, in step S2, according to the sensor coverage model, the weight factor, and the perception overlap cost function model, the NSGA-II multi-objective optimization algorithm is used to find the global optimal solution based on the Pareto front by comprehensively considering multiple conflicting objective functions; according to the selected number of sensors, taking the optimal modal metric function and the sensor coverage rate as the objective functions, and optimizing the positions and angles of the sensors based on the structural weight factor and the sensor overlap cost function, and outputting the optimal positions, angles, overlap costs, and coverage rates of the sensors under the optimal number.

[0012] For the monitoring of large composite structures, according to the optimized optimal number of sensors, combining the optimal modal function and the sensor perception model, and optimizing the positions and angles of the sensors according to the weight factors and overlap cost functions in different regions. Under the optimal number of sensors, not only the monitoring coverage overlap and monitoring blind areas of the sensors are reduced, ensuring the monitoring of key areas while taking into account the overall coverage of the structure. The visual tracking projection positioning system is used to determine the positions and angles of the fiber optic sensors, reducing the monitoring errors introduced by the traditional scribing positioning method, thereby improving the sensor layout efficiency and positioning accuracy. Combining random point layout and binocular vision scanning to form a comparative verification of the feasibility of this optimized layout method, improving the accuracy and reliability of structural deformation monitoring in the actual application process. Description of the Drawings

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0014] Figure 1 This is the flowchart of the optimized layout method for structural monitoring fiber optic sensors in the embodiments of the present invention. Specific embodiments

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0016] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0017] Embodiment 1:

[0018] In the current health monitoring of large composite structures, for problems such as uneven distribution of sensors resulting in excessive cross - overlaps, monitoring blind spots caused by insufficient modal coverage of sensors, and missing sensing monitoring data due to decreased sensor sensitivity, as Figure 1 shown, the embodiments of the present invention provide an optimized layout method for structural monitoring fiber optic sensors, including:

[0019] Step S1: Selection of the number of sensors based on the modal metric function

[0020] To achieve the lightweight design of sensors and the purpose of optimal selection of the number of sensors, seven modal metric functions are selected, namely effective independence, Fisher information matrix, information entropy, kinetic energy, average driving point residual, eigenvalue vector product, and modal confidence. Taking one of the seven modal metric functions and the number of sensors as the optimization objective, analyze the Pareto solutions obtained between the modal function and the number of sensors to determine the optimal selection of the number of sensors.

[0021] Step S2: Selection of the optimal modal function based on the Hypervolume index

[0022] According to the Hypervolume (HV) indicator, the hypervolume evaluation is carried out on the Pareto solutions obtained from the above seven modal metric functions and the number of sensors, which is used to measure the diversity and balance of the obtained solution set. According to the trend of the obtained solution set, it is divided into linear solutions and convex solutions, and the optimal modal metric function is selected from the two solution set trends by the HV indicator. The expression of the HV indicator is as follows:

[0023]

[0024] In the formula, v(x, P r ) represents the spatial hypervolume formed by the solutions in the non-dominated solution set D and the reference point P r , and the optimal modal metric is determined according to the hypervolume value.

[0025] Step S3: Establishment of the FBG fiber optic sensing coverage model

[0026] To achieve the optimal number of sensors while ensuring the maximum coverage of the sensors in the measured structure area, it is necessary to establish an FBG sensing coverage model. When solving with the goal of the best sensor coverage, a probability perception model is often introduced to analyze the sensor coverage rate problem. Since the monitoring environment of the sensors is complex during the actual application process, the perception probability is converted into the form of an attenuation function according to different conditions. Let represent the combined perception coverage rate of N sensors, then the average coverage rate of N sensors for M nodes is:[[]]

[0027]

[0028] Step S4: Weight factor and angular overlap cost function model

[0029] In the case of a limited number of sensors, to improve the coverage of the monitoring area and achieve the rational utilization of sensor resources, a weight factor is introduced to optimize the distribution of sensor positions. Before determining the weight factor, to avoid the increase in data division complexity caused by the uneven distribution of the deformation data set, according to the overall trend of the structural deformation, combined with the definition of the nominal value and the critical value in the structural analysis, the partition is carried out. The minimum and maximum nominal values, the minimum and maximum critical values are selected in turn to complete the deformation data partition. According to prior knowledge, the amount of deformation data between the minimum and maximum nominal values needs to account for about 50% of the total amount of deformation data, and the data in this interval shows the characteristic of being closer to the sample mean; the deformation data between the minimum and maximum critical values needs to account for about 95% of the total amount of deformation data. When the value of the deformation data gradually approaches the critical value, the deformation data shows a trend of moving away from the mean. After the partition is completed, the weight is defined according to the mean values of the data in the low, medium, and high deformation amount regions. This method is not easily affected by extreme values and has a certain stability. At the same time, the mean values of different deformation regions can intuitively reflect the deformation degree of the region, which conforms to the overall trend of the data characteristics. Let the mean values of the data in the low, medium, and high regions be μl , μ m , μ h , and its expression is:

[0030]

[0031] In the formula, n l , n m , n h respectively represent the data quantities in the low, medium, and high deformation regions, and x l , x m , x h respectively represent each deformation data point in each region. Given the mean values of the data sets in each region, after normalization, the weights ω l , ω m and ω h of the low, medium, and high deformation regions are respectively expressed as:

[0032]

[0033] To reduce the data redundancy phenomenon caused by the coverage overlap between sensors, the sensor angles need to be optimized. Let the sensor angle adjustment range be from 0° to 360°. According to the sensor coordinate values, the Euclidean distance between the two sensor centers can be determined. Let (x a , y a ), (x b , y b ) respectively represent the corresponding position coordinates of sensor a and sensor b, and (θ a , θ b ) represent the sensor rotation angles, then its angle overlap cost function can be expressed as:

[0034]

[0035] Its optimization objective needs to satisfy the minimization of the overlap cost function:

[0036]

[0037] According to the theorem for calculating the area of an irregular polygon, the overlapping area of the sensing regions between sensor a and sensor b is:

[0038]

[0039] In the formula, (x i , y i ), (x i+1 , y i+1 ), (x n , y n ) represent the vertex coordinates of the overlapping area polygon. Similarly, for the constrained area of sensor arrangement, its area is:

[0040]

[0041] In the formula, (x j , y j ), (x j+1 , y j+1 ), (x m , y m ) represent the vertex coordinates of the polygon of the constraint region. At this time, the ratio of the overlapping area sensed by N sensors at different angles to the area of the constraint region is:

[0042]

[0043] Step S5, Multi-objective optimization method based on NSGA-II

[0044] Based on the above model, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) multi-objective optimization algorithm is adopted. By comprehensively considering multiple conflicting objective functions, the global optimal solution is found based on the Pareto front. According to the selected number of sensors, the optimal attitude metric function and the sensor coverage rate are used as the objective functions, and the sensor positions and angles are optimized based on the structural weight factor and the sensor overlap cost function, and the optimal positions, angles, overlap costs and coverage rates of the sensors under the optimal number are output.

[0045] The present invention first uses the attitude metric function to determine the selection of the optimal number of sensors, realizing the lightweight design of the sensors; secondly, combines the sensor coverage model, the weight factor and the sensing overlap cost function model to perform multi-objective optimization layout of the sensors; finally, based on the optimization results for structural deformation monitoring, verifies the feasibility of the optimization method, and uses the visual tracking projection positioning system to determine the positions and angles of the fiber optic sensors, reducing the monitoring errors introduced by the traditional scribing positioning method, thereby improving the sensor layout efficiency and positioning accuracy. The results show that the absolute error of deformation monitoring is within 0.1 mm, the relative error is within 6.47%, and the root mean square error is 0.066 mm. This method distributes weights according to different regions according to the overall deformation trend of the structure, and combines the sensing range to optimize the layout angles of the sensors, reducing the coverage redundancy between the sensors. On the premise of lightweight, the overall utilization efficiency of the sensors is improved, ensuring the effective monitoring of the deformation of the measured structure.

[0046] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for optimizing the arrangement of optical fiber sensors for structural monitoring, characterized in that: include: Step S1, using the model attitude measurement function to determine the optimal number of sensors; Step S2: Combining the sensor coverage model, weight factor and perception overlap cost function model to perform multi-objective optimization layout of sensors.

2. The structure monitoring optical fiber sensing optimization arrangement method according to claim 1, characterized in that: In step S1, seven modal attitude measurement functions are selected, namely effective independence, Fisher information matrix, information entropy, kinetic energy, average driving point residual, eigenvalue vector product and modal confidence criterion; one of the seven modal attitude measurement functions and the number of sensors are taken as optimization targets, and the Pareto solution between the modal function and the number of sensors is analyzed to determine the optimal number of sensors.

3. The structure monitoring optical fiber sensing optimization arrangement method according to claim 2, characterized in that: Select the optimal mode function based on the Hypervolume metric.

4. The structure monitoring optical fiber sensing optimization arrangement method according to claim 3, characterized in that: In step S2, according to the sensor coverage model, weight factor and perception overlap cost function model, the NSGA-II multi-objective optimization algorithm is used to find the global optimal solution based on the Pareto front by comprehensively considering multiple conflicting objective functions; according to the selected number of sensors, the optimal model attitude measurement function and sensor coverage are used as objective functions, and the sensor position and angle are optimized based on the structural weight factor and the sensor overlap cost function, and the optimal position, angle, overlap cost and coverage of the sensors under the optimal number are output.

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