Multi-channel strain data integration method for metal structure life monitoring

Abstract

The application provides a multichannel strain data integration method for metal structure life monitoring, the error between the predicted life and the theoretical life of single measuring point data is evaluated independently, the weight value of the measuring point data is calculated, so that the strain data of multiple channels can participate in load inversion and life prediction. Compared with the existing data integration method, the application considers the patching error of the strain gauge, proposes a new weight value determination method, maximally utilizes the strain data of all channels, can accurately predict the fatigue life even if part of the strain gauges are damaged, and further improves the reliability of the fatigue life prediction method based on strain monitoring under the condition that the prediction accuracy is unchanged.

Description

Technical Field

[0001] This invention belongs to the field of structural mechanics fatigue life prediction, specifically involving a multi-channel strain data processing method for monitoring the life of metal structures. Background Technology

[0002] During aircraft operation, repeated loading can lead to structural fatigue damage, resulting in fatigue cracks. If these cracks are not detected and repaired promptly, they can pose a serious threat to aircraft safety. Therefore, researching the fatigue life of aircraft and its real-time detection methods can ensure the safety and reliability of aircraft throughout their service life. Furthermore, fatigue life research can reduce aircraft maintenance costs and downtime, improving operational efficiency and having significant economic implications.

[0003] Due to the influence of materials, processing technology and actual flight conditions, the fatigue life of aircraft structures is dispersed. The traditional fleet management concept, which focuses on the average life of the fleet, has gradually revealed many shortcomings. As a result, the concept of single-aircraft life management has emerged and has become the main development direction of modern aircraft life management.

[0004] The most widely used and technologically mature single-aircraft life management concept internationally is strain / load-based single-aircraft life monitoring. This concept continuously measures and tracks the strain of critical structural parts of each aircraft during its service life to obtain the true strain history data of each aircraft. Then, through effective single-aircraft tracking analysis methods and structural load inversion, the damage status of critical structures of each aircraft in service is assessed in real time, thereby determining the maintenance intervals and service life of each critical component of each aircraft.

[0005] In practice, since the locations of measuring points near critical structural areas are not uniquely determined, strain-monitored fatigue tests often select multiple measuring points to obtain multi-channel strain data. Due to the lack of mature and comprehensive data integration and processing methods, in practice, strain data from multiple measuring points that best reflect strain field changes in the critical area or best meet the selection criteria are often chosen for load inversion. However, this method cannot effectively utilize multi-channel strain monitoring data and has low reliability; the accuracy of life prediction drops significantly after strain sensor failure. Furthermore, existing strain-monitored fatigue life prediction methods do not quantitatively consider the impact of strain sensor patch errors, and this error, if further propagated, may lead to a decrease in the final life prediction accuracy.

[0006] Commonly used multi-channel data integration methods include: mean method, voting method, and learning combination method. The latter two methods require a large amount of data and are complex; the traditional mean method uses Lagrange multipliers to determine the optimal weights, and the calculation process may result in ill-conditioned matrices, leading to computational difficulties.

[0007] How to quantitatively evaluate the patch error of strain sensors, fuse multi-channel strain monitoring data, and use a new, simple, and accurate weighting calculation method to perform load inversion and further predict structural fatigue life is a current research challenge in the field of fatigue life prediction based on strain detection. Summary of the Invention

[0008] To address the issues of low reliability and low accuracy associated with the inability to simultaneously integrate strain data from all measuring points when using traditional strain monitoring methods to process multi-channel strain data, this invention provides a multi-channel strain data processing method for life monitoring of metal structures.

[0009] To achieve the above objectives, the technical solution provided by this invention is: a multi-channel strain data processing method for life monitoring of metal structures, comprising the following steps:

[0010] Step 1: Establish a finite element model of the structure, determine the critical section and strain measurement points. The strain measurement points are: P1, P2, ..., P n , n≥2, where the maximum strain at the strain measurement point is lower than the maximum range of the strain gauge used, and the average strain gradient under unit load is less than 0.5με / (kN·mm) (micro-strain per kN·mm);

[0011] Step 2: Analyze and calculate the relationship between the strain at the measuring point and the nominal stress:

[0012] σ nom =k i ε i +b i

[0013] Where, σ nom ε represents the nominal stress in the critical area of ​​the test structure. i Let k be the strain value at the i-th measuring point. i and b i All of these are coefficients characterizing the linear relationship between strain and nominal stress at each measuring point;

[0014] Step 3: Consider strain gauge placement error, including the following sub-steps:

[0015] Step 3.1: Taking the measurement point position on the specimen surface as the origin, define the direction of the strain component to be measured as the x-axis, and the direction perpendicular to it as the y-axis. The positional deviation x in these two directions when attaching the strain gauges is... i and y i Independent and identically distributed, patch position deviation (x) i y i If we consider it to follow a two-dimensional normal distribution, its probability density function is:

[0016]

[0017] In the formula, the mean μ1=μ2=0mm, the correlation coefficient r=0, and the variance σ is obtained from a large number of actual sensor placement statistics. If there is no statistical data, one-third of the maximum allowable patch position error value of the corresponding type of sensor is taken.

[0018] Angular error α of strain gauge bonding i It also follows a one-dimensional normal distribution, with the following probability density function:

[0019]

[0020] In the formula, the mean μ3 = 0° and the variance σ is obtained from a large number of actual sensor placement statistics. If there is no statistical data, one-third of the maximum allowable patch position error value of the corresponding type of sensor is taken.

[0021] Step 4: Calculate the theoretical predicted lifetime and the actual predicted lifetime of the i-th strain measurement point to obtain the weight of the i-th strain measurement point. Further calculate the weight of each strain measurement point.

[0022] Step 5: Integrate multi-channel strain history data based on the weighted average method: Integrate the strain data ε1, ε2, ..., ε at each measuring point. n The integrated strain ε is obtained by normalization:

[0023]

[0024] Establish integrated strain ε and nominal stress σ at the critical section nom The relationship between them:

[0025] σ nom =kε+b (4)

[0026] Step 6: Predict fatigue life, including the following steps:

[0027] Step 6.1: Conduct fatigue tests and obtain strain history data: Attach strain gauges to the n strain measurement points determined in Step 1, conduct fatigue tests, and collect real-time strain history data ε1, ε2, ..., ε at each measurement point. n ;

[0028] Step 6.2: Integrate strain data and perform load inversion:

[0029] Step 6.3: Fatigue life prediction: Based on the nominal stress history σ of the critical section nom By combining the SN curve, the fatigue life N of this structure is predicted.

[0030] Furthermore, step 4 includes the following sub-steps:

[0031] Step 4.1: Calculate the theoretical predicted lifetime N for the i-th strain measurement point. i ;

[0032] Step 4.2: Calculate the actual predicted lifetime N′ at the i-th strain measurement point. i ;

[0033] Step 4.3: Calculate the lifetime prediction error at the i-th strain measurement point and take its absolute value:

[0034] ΔN i =|N i -N′ i |

[0035] Step 4.4: Calculate the weight of the i-th strain measurement point.

[0036] Repeat steps 4.1-4.3 to obtain the lifetime prediction errors ΔN1, ΔN2, ..., ΔN corresponding to n strain measurement points. n Then the weight w of the i-th point i The calculation formula is:

[0037]

[0038] Step 4.5: Determine the weight of the i-th strain measurement point: Repeat steps 4.1-4.4 above k times, and record the average value of the weights calculated each time. Repeat steps 4.1-4.4 again to obtain the weight w calculated in the (k+1)th time. k+1 Determine the weight and the average value. Error between Has it converged to 1×10? -3 If convergence occurs, the average value will be taken. This is the final weight for that point; if it does not converge, repeat the calculation k+2, k+3, ..., k+n times until the error meets the requirements.

[0039] Step 4.6: Calculate the weight of each strain measurement point: Repeat steps 4.1-4.5 to calculate the weights of n strain measurement points.

[0040] Furthermore, in step 1, the finite element model is established based on its geometric dimensions, material properties, boundary conditions, and loading form; the structural strain field is obtained through finite element calculation, and the critical section is determined.

[0041] Furthermore, in step 2, k i and b i It can be calculated using the least squares fitting method.

[0042] Furthermore, step 6.2 includes the following sub-steps:

[0043] Step 6.2.1: Based on the weights of each measuring point obtained in 4.6, strain integration is performed using equation (7), and load inversion is performed using equation (8) to obtain the nominal stress history σ of the critical section. nom If the measurement data is invalid, the weights are recalculated and the strain integration is performed again.

[0044] Step 6.2.2: If a strain gauge at a certain measuring point fails or an abnormal data point appears in a certain channel during the actual fatigue test, rendering the channel unusable, then the data for that channel should be discarded and the weights recalculated.

[0045]

[0046] After recalculating the weights, return to step 5 to recalculate the integrated strain ε, and re-establish the integrated strain ε with the nominal stress σ of the critical section in the finite element software after changing the weights. nom The relationship between them.

[0047] Beneficial effects

[0048] The advantages of this invention are as follows: This invention proposes a multi-channel strain data integration method for monitoring the life of metal structures. By independently evaluating the error between the predicted life and the theoretical life of individual measuring point data, the weight value of the measuring point data is calculated, thereby enabling strain data from multiple channels to participate in load inversion and life prediction. Compared with existing data integration methods, this invention considers strain gauge patching errors and proposes a new weighting method, maximizing the utilization of strain data from all channels. Even if some strain gauges are damaged, fatigue life can still be accurately predicted. Without changing the prediction accuracy, this further improves the reliability of the strain monitoring-based fatigue life prediction method. Attached Figure Description

[0049] Figure 1 This is a flowchart of the implementation process of the present invention.

[0050] Figure 2 This is a flowchart of the program for calculating the weights of measurement points in this invention.

[0051] Figure 3 Stress field in the critical area of ​​the specimen structure of this invention.

[0052] Figure 4 The location of the strain measurement point in this invention.

[0053] Figure 5 The strain field around strain measurement point 3 in this invention is an example of the strain field around strain measurement point 3.

[0054] Figure 6This invention provides a comparison between the nominal stress results obtained from strain load inversion and the nominal stress results corresponding to the input load. Detailed Implementation

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0056] See Figures 1-6 A multi-channel strain data integration method for monitoring the life of metal structures includes the following steps:

[0057] Step 1: Establish a finite element model of the structure and determine the critical section and strain measurement points.

[0058] For a given test structure, a finite element model is established based on its geometry, material properties, boundary conditions, and loading method. The strain field of the structure is obtained through finite element calculations, the critical section is determined, and n strain measurement points are selected. The maximum strain at each strain measurement point should be lower than the maximum range of the strain gauge used, the average strain gradient under unit load should be less than 0.5 με / (kN·mm) (micro-strain per kilonewton-millimeter), and the points should be far from clamping ends, rivet holes, and other locations where strain gauges cannot be applied during actual operation. These points are numbered as: P1, P2, ..., P n , where n≥2.

[0059] Step 2: Analyze and calculate the relationship between the strain at the measuring point and the nominal stress.

[0060] Based on the type of test structure and the applied load, the nominal stress method, local stress-strain method, or stress-strain field strength method are selected. Combined with the finite element simulation results, the strain at n measuring points and the nominal stress σ at the critical section of the specimen are established respectively. nom Relationship:

[0061] σ nom =f i (ε i (7)

[0062] Since the structural component is basically in the elastic stage during high-cycle fatigue testing, and the strain at the measuring point is basically linearly related to the nominal stress, then:

[0063] σ nom =k i ε i +b i (8)

[0064] In the formula, σ nom ε represents the nominal stress in the critical area of ​​the test structure. i Let k be the strain value at the i-th measuring point. i and b iThese are coefficients that characterize the linear relationship between strain and nominal stress at each measuring point, and can be calculated by least squares fitting.

[0065] Step 3: Consider strain gauge patching error.

[0066] Based on the actual situation, the positional and angular deviations that may occur during strain gauge attachment are considered. Taking the measurement point on the specimen surface as the origin, the direction of the strain component to be measured is the x-axis, and the direction perpendicular to it is the y-axis. The positional deviations x in these two directions during strain gauge attachment are... i and y i If they are independent and identically distributed, then their patch position deviation (x) i y i It can be viewed as following a two-dimensional normal distribution, and its probability density function is:

[0067]

[0068] In the formula, the mean μ1=μ2=0mm, and the variance σ 1，2 The value depends on the specific type of strain gauge used and is generally obtained based on statistical analysis of a large number of actual sensor placements. When data collection and analysis are not conducted, the 3σ criterion of the normal distribution can be used, i.e., one-third of the maximum allowable patch position error for the corresponding type of sensor can be taken as the variance σ. For example, for a common resistance strain gauge, the maximum observable patch position error is 1 mm, so 3σ is taken as the variance. 1，2 =1mm, then the variance σ 2 1，2 =0.11mm 2 Because of x i and y i Independent and identically distributed, with a correlation coefficient r = 0, i.e. (x i ,y i )~N(0,0,0.11,0.11,0).

[0069] Similarly, the angle error α of strain gauge bonding i It can be viewed as following a one-dimensional normal distribution, with the following probability density function:

[0070]

[0071] In the formula, the mean μ3 = 0°, and the variance σ3 depends on the specific type of strain gauge used. It is generally obtained through statistical analysis of a large number of actual sensor placements. When data collection and statistics are not conducted, the 3σ criterion of the normal distribution can be used, i.e., one-third of the maximum allowable patch angle error for the corresponding type of sensor can be taken as the variance σ. For example, for a common resistive strain gauge, the maximum observable patch angle error is 3°, so 3σ3 = 3°, and the variance σ... 2 3 = (1°)2 , i.e. α i ~N(0,1).

[0072] Step 4: Calculate the theoretical predicted lifetime of the i-th measurement point.

[0073] For the i-th measuring point, a load F is applied according to the actual situation to obtain the strain value ε at that measuring point. i Using equation (2), the current nominal stress σ under load F is obtained. nom In conjunction with the material's SN curve, the theoretical predicted lifetime N under no patch error is calculated using the nominal stress method, local stress-strain method, or stress-strain field strength method. i .

[0074] Step 5: Calculate the actual predicted lifetime of the i-th measurement point.

[0075] Introducing the patch error effect shown in step 3, a set of patch position and angle deviations is randomly generated using the Monte Carlo sampling method, and substituted into the strain field of measuring point i under load F obtained in step 1 to obtain the corresponding actual predicted lifetime N'. i .

[0076] Step 6: Calculate the lifetime prediction error for the i-th measurement point.

[0077] Define the lifetime prediction error ΔN at the i-th measurement point. i This is the absolute value of the difference between the theoretical predicted lifetime and the actual predicted lifetime at that point, i.e.:

[0078] ΔN i =|N i -N′ i | (11)

[0079] Step 7: Calculate the weight of the i-th measurement point.

[0080] Repeat steps 4-6 to obtain the lifetime prediction errors ΔN1, ΔN2, ..., ΔN corresponding to n measurement points. n Then the weight w of the i-th point i The calculation formula is:

[0081]

[0082] Step 8: Determine the weight of the i-th measurement point.

[0083] Since the position and angle errors of the patch generated in one iteration are random, they cannot be used as the final calculation result. Therefore, steps 4-7 are repeated k times, where k ≥ 50, and the average value of the weights obtained in each calculation is recorded. Repeat steps 4-7 again to obtain the weight w calculated in the (k+1)th iteration. k+1Determine the weight and the average value. Error between Has it converged to 1×10? -3 The following applies. If convergence occurs, the average value will be taken. This is the final weight for that point; if it does not converge, repeat the calculation k+2, k+3, ..., k+n times until the error meets the requirements.

[0084] Step 9: Calculate the weight of each measuring point.

[0085] Repeat steps 4-8 to calculate the weights of the n measurement points.

[0086] Step 10: Integrate multi-channel strain history data based on the weighted average method.

[0087] Using the weighted average method, the strain data ε1, ε2, ..., ε at each measuring point are... n The integrated strain ε is obtained by normalization:

[0088]

[0089] Establish integrated strain ε and nominal stress σ at the critical section in finite element software nom The relationship between them:

[0090] σ nom =kε+b (14)

[0091] Step 11: Conduct fatigue tests to obtain strain history data.

[0092] Strain gauges were attached to the n strain measurement points determined in step 1, and fatigue tests were conducted to collect real-time strain data ε1, ε2, ..., ε at each measurement point. n .

[0093] Step 12: Integrate strain data and perform load inversion.

[0094] Based on the weights of each measuring point obtained in step 9, strain integration is performed using equation (7), and load inversion is performed using equation (8) to obtain the nominal stress history σ of the critical section. nom .

[0095] Step 13: If the measurement point data is invalid, recalculate the weights and re-integrate the strain.

[0096] In actual fatigue testing, if a strain gauge at a certain measuring point fails or a large amount of abnormal data in a certain channel becomes unusable, the data from that channel can be discarded and the weights recalculated.

[0097]

[0098] After recalculating the weights, return to step 10 to recalculate the integrated strain ε, and re-establish the integrated strain ε with the nominal stress σ of the critical section after changing the weights in the finite element software. nom The relationship between them.

[0099] Step 14: Fatigue life prediction.

[0100] Based on the nominal stress history σ of the critical section nom Based on the SN curve of the material, the fatigue life N of the structure is calculated using appropriate methods such as the nominal stress method or the stress severity coefficient method.

[0101] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for explanation purposes only and are not intended to limit the scope of the invention.

[0102] The technical process of this invention is as follows: Figure 1 As shown, the flowchart for calculating the measurement point weights is as follows: Figure 2 As shown. To better understand the technical solution of the present invention, the above method is applied to the fatigue life prediction of single-hole plate specimens.

[0103] Step 1: Establish a finite element model of the structure and determine the critical section and strain measurement points.

[0104] The single-hole plate specimen is a dog-bone shaped center-hole plate conforming to standard GB / T 3075-2021. All cross-sections are rectangular, divided into a straight section, a transition section, and a clamping end. A tangential arc transitions between the straight section and the clamping end. The clamping end is 60mm wide and 300mm long, with a 4mm diameter through hole at the center. The straight section, without the hole, has a cross-sectional area of ​​60mm². 2 The net cross-sectional area of ​​the section containing the hole is 52mm². 2 The specimen material was LY12-CZ aluminum alloy, with an elastic modulus of 71 GPa and a Poisson's ratio of 0.33 at room temperature. During loading, the clamping end was fixed to the actuating cylinder using a hydraulic chuck, and the applied load direction was perpendicular to the direction of the specimen's minimum cross-sectional area. Six strain measurement points were selected based on the principle of point selection, and their locations are as follows: Figure 4 As shown, the maximum strain at the six strain measurement points is lower than the maximum range of the resistance strain gauge used (30000 με), the average strain gradient under the maximum load is less than 0.5 με / (kN·mm) (micro-strain per kilonewton-millimeter), and they are far from the clamping end and the through hole. The strain measurement points are numbered P1, P2, ..., P6. The strain field distribution at measurement point 3 is shown below. Figure 5 As shown.

[0105] Step 2: Analyze and calculate the relationship between the strain at the measuring point and the nominal stress.

[0106] Since the single-hole plate specimen has a relatively simple structure and force transmission path, the nominal stress method is selected for life prediction. Combined with finite element simulation results, the least squares method is used to establish the strain at six measuring points under different loads and the nominal stress σ at the critical section of the specimen. nom Approximate linear relationship:

[0107]

[0108] Step 3: Consider strain gauge patching error.

[0109] In this example, a resistance strain gauge is used, so it is assumed that the positional error of the strain gauge at each measuring point follows a two-dimensional normal distribution (x... i ,y i )~N(0,0,0.11,0.11,0). The angular error follows a one-dimensional normal distribution α. i ~N(0,1).

[0110] Step 4: Reference Figure 2 The process calculates the weight values ​​of the measuring points and provides the integrated strain.

[0111] The calculated weights are:

[0112]

[0113] Furthermore, using the weighted average method, the integrated strain expression is obtained:

[0114]

[0115] Furthermore, an integrated strain ε and nominal stress σ at the critical section were established in the finite element software. nom The relationship between them:

[0116] σ nom =0.04861ε+0.000827 (18)

[0117] Step 5: Conduct fatigue tests to obtain strain history data.

[0118] Strain gauges were attached to the six strain measurement points determined in step 1. A random fatigue load spectrum was used to conduct fatigue tests on three single-hole plate specimens, and the strain data history ε1, ε2, ..., ε6 at each measurement point was collected in real time.

[0119] Step 6: Integrate strain data and perform load inversion.

[0120] Based on the weights of each measuring point obtained in step 3, strain integration is performed using equation (11), and load inversion is performed using equation (12) to obtain the nominal stress history σ of the critical section. nomThe nominal stress result obtained from strain load inversion is compared with the nominal stress result corresponding to the input load, for example... Figure 6 As shown.

[0121] Step 7: If the measurement point data is invalid, recalculate the weights and re-integrate the strain.

[0122] During the actual test, the strain gauge at measuring point 1 failed when specimen No. 3 reached approximately 67,000 cycles. Therefore, the weight of the measuring point was recalculated according to the following formula:

[0123]

[0124] The updated weights are calculated as follows:

[0125]

[0126]

[0127] Furthermore, the integrated strain ε is recalculated:

[0128]

[0129] Furthermore, the strain ε and the nominal stress σ at the critical section were re-established in the finite element software. nom The relationship between them:

[0130] σ nom =0.04728ε+0.001325 (21)

[0131] Step 8: Fatigue life prediction.

[0132] Based on the failure status of the strain measurement points, the nominal stress history of specimen No. 3 before 67,000 cycles was calculated using the strain history data from channels 1-6 and equations (11) and (12). The nominal stress history after 67,000 cycles was calculated using the strain history data from channels 1-6 and equations (14) and (15). Finally, the nominal stress history σ of the critical section was obtained. nom No strain gauge failures were observed in the remaining specimens; the nominal stress history was calculated from the strain history data of channels 1-6 and equations (11) and (12). A comparison was made between the nominal stress history of the critical point obtained from the strain data inversion and the actual nominal stress input during the experiment. Figure 6 As shown, the load inversion data has a high degree of agreement with the input data and can be used for life prediction, further proving the scientific validity and feasibility of the multi-channel strain data integration method adopted in this paper.

[0133] Furthermore, the nominal stress history σ nomRainflow counting was performed to obtain the equivalent cycle amplitude and mean. Combined with median SN curve data under different stress concentration factors from existing data, the corresponding stress concentration factor K at the edge of the single-hole plate calculated in the finite element software was then used. T =2.547. Based on Miner's linear damage accumulation theory, the lifetime prediction results are shown in the table below:

[0134]

[0135] It can be seen that the multi-channel strain data integration processing method based on the weighted average method proposed in this invention is close to the fatigue life prediction value based on the load spectrum of the traditional method. It makes the maximum use of strain data from all channels and can still maintain good prediction accuracy even when some strain gauges fail.

[0136] The above description is merely one example of the present invention and is not intended to limit the present invention in any way. Although the process of one example of the present invention is disclosed above, it is not the only solution of the present invention. Any changes or modifications that achieve the same or similar functions as the present invention without departing from the solution of the present invention are within the protection scope of the new technical solution.

[0137] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the scope of the technology disclosed in the present invention, and such modifications or substitutions should all be covered within the scope of protection of the present invention.

Claims

1. A multi-channel strain data processing method for monitoring the lifespan of metal structures, characterized in that, Includes the following steps: Step 1: Establish a finite element model of the structure, determine the critical section and strain measurement points. The strain measurement points are: P1, P2, ..., P n , n≥2, where the maximum strain at the strain measurement point is lower than the maximum range of the strain gauge used, and the average strain gradient under unit load is less than 0.5με / (kN·mm) (micro-strain per kN·mm); Step 2: Analyze and calculate the relationship between the strain at the measuring point and the nominal stress: s nom =k i e i +b i Where, σ nom ε represents the nominal stress in the critical area of ​​the test structure. i Let k be the strain value at the i-th measuring point. i and b i All of these are coefficients characterizing the linear relationship between strain and nominal stress at each measuring point; Step 3: Consider strain gauge placement error, including the following sub-steps: Step 3.1: Taking the measurement point position on the specimen surface as the origin, define the direction of the strain component to be measured as the x-axis, and the direction perpendicular to it as the y-axis. The positional deviation x in these two directions when attaching the strain gauges is... i and y i Independent and identically distributed, patch position deviation (x) i y i If we consider it to follow a two-dimensional normal distribution, its probability density function is: In the formula, the mean μ1=μ2=0mm, the correlation coefficient r=0, and the variance σ is obtained from a large number of actual sensor placement statistics. If there is no statistical data, one-third of the maximum allowable patch position error value of the corresponding type of sensor is taken. Angular error α of strain gauge bonding i It also follows a one-dimensional normal distribution, with the following probability density function: In the formula, the mean μ3 = 0° and the variance σ is obtained from a large number of actual sensor placement statistics. If there is no statistical data, one-third of the maximum allowable patch position error value of the corresponding type of sensor is taken. Step 4: Calculate the theoretical predicted lifetime and the actual predicted lifetime of the i-th strain measurement point to obtain the weight of the i-th strain measurement point. Further calculate the weight of each strain measurement point. Step 5: Integrate multi-channel strain history data based on the weighted average method: Integrate the strain data ε1, ε2, ..., ε at each measuring point. n The integrated strain ε is obtained by normalization: Establish integrated strain ε and nominal stress σ at the critical section nom The relationship between them: s nom =kε+b (4) Step 6: Predict fatigue life, including the following steps: Step 6.1: Conduct fatigue tests and obtain strain history data: Attach strain gauges to the n strain measurement points determined in Step 1, conduct fatigue tests, and collect real-time strain history data ε1, ε2, ..., ε at each measurement point. n ; Step 6.2: Integrate strain data and perform load inversion: Step 6.3: Fatigue life prediction: Based on the nominal stress history σ of the critical section nom By combining the SN curve, the fatigue life N of this structure is predicted.

2. The multi-channel strain data processing method for monitoring the lifespan of metal structures as described in claim 1, characterized in that, Step 4 includes the following sub-steps: Step 4.1: Calculate the theoretical predicted lifetime N for the i-th strain measurement point. i ; Step 4.2: Calculate the actual predicted lifetime N′ at the i-th strain measurement point. i ; Step 4.3: Calculate the lifetime prediction error at the i-th strain measurement point and take its absolute value: ΔN i =|N i -N′ i | Step 4.4: Calculate the weight of the i-th strain measurement point; Repeat steps 4.1-4.3 to obtain the lifetime prediction errors ΔN1, ΔN2, ..., ΔN corresponding to n strain measurement points. n Then the weight w of the i-th point i The calculation formula is: Step 4.5: Determine the weight of the i-th strain measurement point: Repeat steps 4.1-4.4 above k times, and record the average value of the weights calculated each time. Repeat steps 4.1-4.4 again to obtain the weight w calculated in the (k+1)th time. k+1 Determine the weight and the average value. Error between Has it converged to 1×10? -3 If convergence occurs, the average value will be taken. This is the final weight for that point; if it does not converge, repeat the calculation k+2, k+3, ..., k+n times until the error meets the requirements. Step 4.6: Calculate the weight of each strain measurement point: Repeat steps 4.1-4.5 to calculate the weights of n strain measurement points.

3. The multi-channel strain data processing method for monitoring the lifespan of metal structures as described in claim 1, characterized in that, In step 1, the finite element model is established based on its geometric dimensions, material properties, boundary conditions, and loading form; the structural strain field is obtained through finite element calculation, and the critical section is determined.

4. The multi-channel strain data processing method for monitoring the lifespan of metal structures as described in claim 1, characterized in that, In step 2, k i and b i It can be calculated using the least squares fitting method.

5. The multi-channel strain data processing method for monitoring the lifespan of metal structures as described in claim 1, characterized in that, Step 6.2 includes the following sub-steps: Step 6.2.1: Based on the weights of each measuring point obtained in 4.6, strain integration is performed using equation (7), and load inversion is performed using equation (8) to obtain the nominal stress history σ of the critical section. nom If the measurement data is invalid, the weights are recalculated and the strain integration is performed again. Step 6.2.2: If a strain gauge at a certain measuring point fails or an abnormal data point appears in a certain channel during the actual fatigue test, rendering the channel unusable, then the data for that channel should be discarded and the weights recalculated. After recalculating the weights, return to step 5 to recalculate the integrated strain ε, and re-establish the integrated strain ε with the nominal stress σ of the critical section in the finite element software after changing the weights. nom The relationship between them.

Images