Test bench performance degradation evaluation method, system and device based on defect detection

Abstract

The present application relates to aero-engine test bench structure health monitoring and performance evaluation technology, provide a kind of test bench performance attenuation evaluation method, system and equipment based on defect detection, the method comprises: based on at least two types of non-destructive testing method to construct the finite element numerical model of test bench;By singular value decomposition method to finite element numerical model order reduction and solution obtain order reduction model;The predicted structure displacement of test bench is obtained in order reduction model, the actual structure displacement of test bench under test load is collected, and the structure performance attenuation evaluation of test bench is carried out by predicted structure displacement and actual structure displacement.This application method can carry out online structure health identification and performance early warning to test bench by the degradation model constructed, with good accuracy, scalability and engineering practical value, can be widely applied in the state evaluation and fault prediction of aero-engine test bench.

Description

Technical Field

[0001] This invention belongs to the field of health management of test equipment, and relates to the technology of structural health monitoring and performance evaluation of aero-engine test benches. Specifically, it relates to a method, system and equipment for evaluating the performance degradation of test benches based on defect detection. Background Technology

[0002] Aero-engine test benches are used in extreme environment simulation experiments. Long-term operation may lead to gradual performance degradation or even local structural failure due to factors such as fatigue, vibration, and high temperature. Traditional performance evaluation methods mostly rely on manual inspection or periodic disassembly and inspection, which are inefficient, have large errors, and are difficult to detect structural problems in a timely manner.

[0003] In recent years, structural defect detection technologies, such as ultrasonic, infrared thermography, acoustic emission, and visual inspection, have made significant progress, providing richer evidence for structural condition assessment. However, how to effectively correlate defect information with performance degradation patterns through reduced-order numerical models to achieve quantitative performance assessment of the entire life cycle of the test bench remains a current technical challenge. Summary of the Invention

[0004] To address the technical challenge of effectively correlating defect information with performance degradation patterns using a reduced-order numerical model to achieve quantitative performance evaluation of test benches throughout their entire lifecycle, this invention discloses a test bench performance degradation evaluation method based on defect detection. The method includes the following steps:

[0005] S1. Construct a finite element numerical model of the test bench based on at least two types of non-destructive testing methods;

[0006] S2. Generate a displacement snapshot matrix using the finite element numerical model, and perform singular value decomposition and order reduction on the displacement snapshot matrix to obtain a reduced-order basis matrix.

[0007] S3. An initial reduced-order model is established using the reduced-order basis matrix, and the reduced-order model is obtained by solving the initial reduced-order model using the design load and stiffness matrix;

[0008] S4. Input the test load into the reduced-order model to obtain the predicted structural displacement of the test bench, collect the actual structural displacement of the test bench under the test load, and evaluate the structural performance degradation of the test bench by using the predicted structural displacement and the actual structural displacement.

[0009] Further, in step S1, a finite element numerical model of the test bench is constructed based on at least two types of non-destructive testing methods, including:

[0010] S11. Select at least two types of non-destructive testing methods to test the test bench and obtain a defect dataset. Model and fuse all the defect data to obtain a defect description vector. The non-destructive testing methods include at least two of ultrasonic testing, eddy current testing, three-dimensional laser scanning and X-ray testing.

[0011] S12. Using material performance parameters and design loads as inputs and bench structure displacement as output, an initial finite element numerical model is constructed. The initial finite element numerical model is then trained using the defect description vector to obtain the trained finite element numerical model.

[0012] Furthermore, in step S11, all the defect data are standardized and fused using a unified spatial coordinate and feature extraction method to obtain a defect description vector with a unified format, which includes the defect location and defect geometric parameters.

[0013] Further, in step S2, a displacement snapshot matrix is ​​generated using the finite element numerical model, and singular value decomposition and order reduction are performed on the displacement snapshot matrix to obtain a reduced-order basis matrix, including:

[0014] S21. Obtain the structural displacements under multiple load conditions using the finite element numerical model, and generate a displacement snapshot matrix;

[0015] S22. Perform singular value decomposition on the displacement snapshot matrix to obtain the basis matrix and the singular value matrix;

[0016] S23. Select multiple singular value vectors with energies greater than an energy threshold from the singular value matrix, and extract the eigenvectors corresponding to each singular value vector from the basis matrix to generate a reduced-order basis matrix.

[0017] Further, in step S3, an initial reduced-order model is established using the reduced-order basis matrix, and the final reduced-order model is obtained by solving the initial reduced-order model using the design load and stiffness matrix, including:

[0018] S31, The expression for the initial order reduction model is: ,in, For a reduced-order basis matrix, To predict structural displacement, 'a' is the weighting coefficient of the reduced-order model;

[0019] S32. Obtain the stiffness matrix based on the material performance parameters of the test bench, obtain the structural displacement under the design load through the stiffness matrix, and solve the weight coefficients of the reduced-order model through the structural displacement and the reduced-order basis matrix to obtain the reduced-order model.

[0020] Furthermore, step S3 also includes:

[0021] S33. Obtain the structural displacement under a given load using the finite element numerical model and the reduced-order model respectively, calculate the error between the two structural displacements, and iteratively correct the weight coefficients of the reduced-order model according to the error and the error threshold until a new reduced-order model weight coefficient is obtained that satisfies the error being less than the error threshold, thus obtaining the final reduced-order model.

[0022] Further, in step S4, the residual between the predicted structural displacement and the actual structural displacement is calculated, and the residual is used as an indicator of structural functional degradation. If the residual is greater than or equal to the residual threshold, the structural performance of the test bench is judged to have degraded.

[0023] In an improved embodiment of the above-described defect detection-based test bench performance degradation assessment method, the method further includes:

[0024] S5. Based on the predicted structural displacement, use the Miner linear cumulative damage method or the Coffin-Manson model to identify the shortest life path and potential failure location on the test bench.

[0025] This invention also provides a test bench performance degradation assessment system based on defect detection, including a first construction module, a reduced-order basis matrix acquisition module, a second construction module, and a structural performance degradation assessment module.

[0026] The first construction module is used to construct a finite element numerical model of the test bench based on at least two types of non-destructive testing methods.

[0027] The reduced-order basis matrix acquisition module is used to generate a displacement snapshot matrix through the finite element numerical model, and to perform singular value decomposition and order reduction on the displacement snapshot matrix to obtain a reduced-order basis matrix.

[0028] The second building module is used to establish an initial reduced-order model using the reduced-order basis matrix, and to solve the initial reduced-order model using the design load and stiffness matrix to obtain the reduced-order model;

[0029] The structural performance degradation assessment module is used to input the test load into the reduced-order model to obtain the predicted structural displacement of the test bench, collect the actual structural displacement of the test bench under the test load, and conduct a structural performance degradation assessment of the test bench using the predicted structural displacement and the actual structural displacement.

[0030] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned defect detection-based test bench performance degradation assessment methods, in order to solve the technical problem of how to effectively correlate defect information with performance degradation laws through a reduced-order numerical model, and realize quantitative performance assessment of the test bench throughout its entire life cycle.

[0031] Compared with the prior art, the beneficial effects that at least one technical solution adopted in the embodiments of this specification can achieve include at least:

[0032] 1. Multi-source defect data fusion improves assessment accuracy: By jointly modeling multi-source heterogeneous detection information, the comprehensiveness and accuracy of defect identification and performance evaluation are improved. Compared with single-sensor diagnostic methods, multi-sensor data fusion can leverage the advantages of each detection technology, expand the depth and breadth of structural state perception, reduce the probability of misjudgment due to single data bias, and thus more reliably quantify the health status of the test bench.

[0033] 2. Reduced-order model accelerates simulation and supports rapid evaluation: By employing singular value decomposition-based measurements, a reduced-order model is obtained from the finite element numerical model, significantly reducing the model's dimensionality. This reduced-order model enables rapid evaluation of structural performance under multiple operating conditions. Furthermore, while maintaining prediction accuracy, the reduced-order model improves computational efficiency by several orders of magnitude, allowing for real-time iterative analysis under different defect scenarios or load conditions, meeting the speed requirements of test bench health monitoring and life prediction. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of the test bench performance degradation evaluation method based on defect detection according to the present invention.

[0036] Figure 2 This is the architecture of the test bench performance degradation evaluation system based on defect detection according to the present invention.

[0037] Among them, 201 is the first construction module; 202 is the reduced-order basis matrix acquisition module; 203 is the second construction module; and 204 is the structural performance degradation evaluation module. Detailed Implementation

[0038] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0039] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features of the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] This invention discloses a method for evaluating the performance degradation of test benches based on defect detection. See [link to relevant documentation]. Figure 1 As shown, the method includes the following steps:

[0041] S1. Construct a finite element numerical model of the test bench based on at least two types of non-destructive testing methods;

[0042] S2. Generate a displacement snapshot matrix using the finite element numerical model, and perform singular value decomposition and order reduction on the displacement snapshot matrix to obtain a reduced-order basis matrix.

[0043] S3. An initial reduced-order model is established using the reduced-order basis matrix, and the reduced-order model is obtained by solving the initial reduced-order model using the design load and stiffness matrix;

[0044] S4. Input the test load into the reduced-order model to obtain the predicted structural displacement of the test bench, collect the actual structural displacement of the test bench under the test load, and evaluate the structural performance degradation of the test bench by using the predicted structural displacement and the actual structural displacement.

[0045] Further, in step S1, a finite element numerical model of the test bench is constructed based on at least two types of non-destructive testing methods, including:

[0046] S11. Select at least two types of non-destructive testing methods to test the test bench and obtain a defect dataset. Model and fuse all the defect data to obtain a defect description vector.

[0047] The non-destructive testing method includes at least two of the following: ultrasonic testing, eddy current testing, three-dimensional laser scanning, and X-ray testing.

[0048] Ultrasonic testing methods are used to identify internal defects such as cracks, voids, or delamination in materials.

[0049] Eddy current testing is used to identify cracks and corrosion on or near the surface of conductive metal components.

[0050] Three-dimensional laser scanning is used to collect the geometric morphology and deformation features of structural surfaces to help construct a spatial distribution model of defects.

[0051] X-ray inspection methods are used to image and identify internal defects in key metal components or welded areas of a test bench. The methods described above can be used to identify defects such as cracks, porosity, inclusions, and incomplete penetration on the test bench.

[0052] For non-removable support components in the test bench, the welded joints or reinforcement areas can be inspected for detailed internal defects using digital radiography (DR) or computed tomography (CT) to improve the comprehensiveness and accuracy of the assessment.

[0053] This invention selects defect datasets obtained from at least two non-destructive detection methods and uses a multi-source fusion data processing method to jointly analyze the defect datasets to obtain defect description vectors, thereby improving the accuracy and robustness of defect identification. In implementation, data from different non-destructive detection methods can be uniformly modeled, and their respective defect descriptions can be constructed. Each defect description is then standardized according to a unified spatial reference frame and feature extraction method to construct a defect description vector in a unified format. The defect description vector includes two fields: spatial location coordinates (x, y, z) and defect geometric parameters (such as length, width, depth, and volume). It may also include the following fields:

[0054] Spatial location coordinates (x, y, z);

[0055] Defect geometric parameters (such as length, width, depth, volume);

[0056] Physical response parameters (such as echo amplitude, impedance phase change, and heat flux gradient);

[0057] Image texture features (such as grayscale moments, LBP features, Gabor textures);

[0058] Detection method encoding (identifying data source: UT, ET, IRT, 3D, X-ray);

[0059] Confidence score (probability output from the classification model).

[0060] S12. Using material performance parameters and design loads as inputs and bench structure displacement as output, an initial finite element numerical model is constructed. The initial finite element numerical model is then trained using the defect description vector to obtain the trained finite element numerical model.

[0061] Further, in step S2, a displacement snapshot matrix is ​​generated using the finite element numerical model, and singular value decomposition and order reduction are performed on the displacement snapshot matrix to obtain a reduced-order basis matrix, including:

[0062] S21. Obtain the structural displacements under multiple load conditions using the finite element numerical model, and generate a displacement snapshot matrix;

[0063] S22. Perform singular value decomposition on the displacement snapshot matrix to obtain the basis matrix and the singular value matrix;

[0064] S23. Select multiple singular value vectors with energies greater than an energy threshold from the singular value matrix, and extract the eigenvectors corresponding to each singular value vector from the basis matrix to generate a reduced-order basis matrix.

[0065] In implementation, the structural displacements under n load conditions are first obtained through the finite element numerical model established in step S1, which can be expressed as follows: The structural displacements under n load conditions can be represented by the following displacement snapshot matrix S:

[0066] ;

[0067] The displacement snapshot matrix S is an m-row, n-column real matrix, where m is the number of degrees of freedom and n is the number of load cases. Singular value decomposition of the displacement snapshot matrix S yields U, V, ... There are 3 matrices in total. Where U is an m-order basis matrix and V is an n-order unitary matrix, It is the complex conjugate of V. It is an m x n singular value matrix.

[0068] This invention obtains a reduced-order basis matrix containing r eigenvectors by retaining the first r singular value vectors with energies greater than an energy threshold from the singular value matrix and then extracting the eigenvectors corresponding to each singular value vector from the basis matrix U. .

[0069] Further, in step S3, an initial reduced-order model is established using the reduced-order basis matrix, and the final reduced-order model is obtained by solving the initial reduced-order model using the design load and stiffness matrix, including:

[0070] S31, The expression for the initial order reduction model is: ,in, For a reduced-order basis matrix, To predict structural displacement, 'a' is the weighting coefficient of the reduced-order model;

[0071] S32. Obtain the stiffness matrix based on the material performance parameters of the test bench, obtain the structural displacement under the design load through the stiffness matrix K, and solve the weight coefficients of the reduced-order model through the structural displacement and the reduced-order basis matrix to obtain the reduced-order model.

[0072] Furthermore, step S3 also includes:

[0073] S33. Obtain the structural displacement under a given load using the finite element numerical model and the reduced-order model respectively, calculate the error between the two structural displacements, and iteratively correct the weight coefficients of the reduced-order model according to the error and the error threshold until a new reduced-order model weight coefficient is obtained that satisfies the error being less than the error threshold, thus obtaining the final reduced-order model.

[0074] In practical implementation, the structural displacement output by the finite element numerical model can be expressed as: The predicted structural displacement output by the reduced-order model is The error threshold can be set to 5.0%, which is the error between the displacements of two structures. satisfy If the above conditions are met, it indicates that the prediction accuracy of the reduced-order model is good. If the above conditions are not met, a new reduced-order basis matrix can be calculated by adjusting the energy threshold in step S23, and then the weight coefficients of the reduced-order model can be recalculated using the new reduced-order basis matrix.

[0075] Furthermore, the health status identification is based on the deviation between the predicted results and the measured results of the reduced-order model to perform damage inversion and state estimation. Therefore, in step S4, the residual between the predicted structural displacement and the actual structural displacement is calculated, and the residual is used as an indicator of structural functional degradation. If the residual is greater than or equal to a residual threshold, the structural performance of the test bench is judged to have degraded. The residual R can be expressed by the formula... Calculate, where, This represents the actual structural displacement. During implementation, threshold levels can be set to classify structural performance degradation indicators, allowing for the classification of structural performance degradation of the test bench based on these threshold levels.

[0076] In an improved embodiment of the above-described defect detection-based test bench performance degradation assessment method, the method further includes:

[0077] S5. Based on the predicted structural displacement, use the Miner linear cumulative damage method or the Coffin-Manson model to identify the shortest life path and potential failure location on the test bench.

[0078] Based on the same inventive concept, this invention also provides a test bench performance degradation assessment system based on defect detection, as described in the following embodiments. Since the principle of the test bench performance degradation assessment system based on defect detection is similar to the test bench performance degradation assessment method based on defect detection disclosed in the above embodiments, the implementation of the test bench performance degradation assessment system based on defect detection can refer to the implementation of the test bench performance degradation assessment method based on defect detection described above, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0079] Figure 2 This is a structural block diagram of a test bench performance degradation evaluation system based on defect detection disclosed in an embodiment of the present invention, as shown below. Figure 2 As shown, the system includes a first construction module 201, a reduced-order basis matrix acquisition module 202, a second construction module 203, and a structural performance degradation evaluation module 204. The structure will be described below.

[0080] The first construction module 201 is used to construct a finite element numerical model of the test bench based on at least two types of non-destructive testing methods.

[0081] The reduced-order basis matrix acquisition module 202 acquires a displacement snapshot matrix generated by the finite element numerical model, and performs singular value decomposition and order reduction on the displacement snapshot matrix to obtain a reduced-order basis matrix.

[0082] The second construction module 203 is used to establish an initial reduced-order model using the reduced-order basis matrix, and to solve the initial reduced-order model using the design load and stiffness matrix to obtain the reduced-order model;

[0083] The structural performance degradation assessment module 204 is used to input the test load into the reduced-order model to obtain the predicted structural displacement of the test bench, collect the actual structural displacement of the test bench under the test load, and conduct a structural performance degradation assessment of the test bench through the predicted structural displacement and the actual structural displacement.

[0084] The embodiments of the present invention achieve the following technical effects:

[0085] 1. Multi-source defect data fusion improves assessment accuracy: By jointly modeling multi-source heterogeneous detection information, the comprehensiveness and accuracy of defect identification and performance evaluation are improved. Compared with single-sensor diagnostic methods, multi-sensor data fusion can leverage the advantages of each detection technology, expand the depth and breadth of structural state perception, reduce the probability of misjudgment due to single data bias, and thus more reliably quantify the health status of the test bench.

[0086] 2. Reduced-order model accelerates simulation and supports rapid evaluation: By employing singular value decomposition-based measurements, a reduced-order model is obtained from the finite element numerical model, significantly reducing the model's dimensionality. This reduced-order model enables rapid evaluation of structural performance under multiple operating conditions. Furthermore, while maintaining prediction accuracy, the reduced-order model improves computational efficiency by several orders of magnitude, allowing for real-time iterative analysis under different defect scenarios or load conditions, meeting the speed requirements of test bench health monitoring and life prediction.

[0087] In this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-described defect detection-based test bench performance degradation evaluation methods.

[0088] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0089] In this embodiment, a computer-readable storage medium is provided, which stores a computer program that executes any of the above-described defect detection-based test bench performance degradation evaluation methods.

[0090] Specifically, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0091] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0092] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A test bench performance degradation assessment method based on defect detection, characterized in that, The method comprises the following steps: constructing a finite element numerical model of the test bench based on at least two types of non-destructive detection methods; generating a displacement snapshot matrix through the finite element numerical model, performing singular value decomposition and order reduction on the displacement snapshot matrix to obtain a reduced order basis matrix; establishing an initial reduced order model using the reduced order basis matrix, and solving the initial reduced order model using a design load and a stiffness matrix to obtain a reduced order model; inputting a test load into the reduced order model to obtain a predicted structural displacement of the test bench, collecting an actual structural displacement of the test bench under the test load, and performing structural performance degradation evaluation of the test bench through the predicted structural displacement and the actual structural displacement.

2. The test bed performance degradation assessment method based on defect detection of claim 1, wherein, The method for constructing a finite element numerical model of a test bench based on at least two types of non-destructive detection methods comprises the following steps: selecting at least two types of non-destructive detection methods to detect the test bench and obtain a defect data set, and modeling and fusing all the defect data to obtain a defect description vector, wherein the non-destructive detection methods include at least two of ultrasonic detection, eddy current detection, three-dimensional laser scanning, and X-ray detection; using material performance parameters and design loads as inputs and using bench structural displacement as outputs to construct an initial finite element numerical model, and training the initial finite element numerical model through the defect description vector to obtain a trained finite element numerical model.

3. The test bed performance degradation assessment method based on defect detection of claim 2, wherein, standardizing and fusing all the defect data through a unified spatial coordinate and feature extraction method to obtain a defect description vector with unified format, wherein the defect description vector includes defect position and defect geometric parameters.

4. The defect detection based test bed performance degradation assessment method of claim 1, wherein, The method for generating a displacement snapshot matrix through the finite element numerical model, performing singular value decomposition and order reduction on the displacement snapshot matrix to obtain a reduced order basis matrix comprises the following steps: obtaining structural displacement under multiple load conditions through the finite element numerical model to generate a displacement snapshot matrix; performing singular value decomposition on the displacement snapshot matrix to obtain a basis matrix and a singular value matrix; selecting multiple singular value vectors with energy greater than an energy threshold from the singular value matrix, and extracting feature vectors corresponding to each singular value vector from the basis matrix to generate a reduced order basis matrix.

5. The defect detection based test bed performance degradation assessment method of claim 1, wherein, The method for establishing an initial reduced order model using the reduced order basis matrix, and solving the initial reduced order model using a design load and a stiffness matrix to obtain a final reduced order model comprises the following steps: The expression of the initial reduced-order model is wherein, is a reduced-order basis matrix, is a predicted structural displacement, and a is a reduced-order model weight coefficient. obtaining a stiffness matrix according to material performance parameters of the test bench, obtaining structural displacement under a design load through the stiffness matrix, solving a reduced order model weight coefficient through the structural displacement and the reduced order basis matrix, and obtaining a reduced order model.

6. The test bed performance degradation assessment method based on defect detection according to claim 1 or 5, characterized in that, The method further comprises the following steps: obtaining structural displacement under a given load through the finite element numerical model and the reduced order model respectively, calculating the error between the two structural displacements, iteratively correcting the reduced order model weight coefficient according to the error and an error threshold until a new reduced order model weight coefficient satisfying the error being less than the error threshold is obtained, and obtaining a final reduced order model.

7. The defect detection based test bed performance degradation assessment method of claim 1, wherein, calculating the residual error between the predicted structural displacement and the actual structural displacement, taking the residual error as a structural functional degradation indicator, and judging the structural performance degradation of the test bench if the residual error is greater than or equal to a residual error threshold.

8. The defect detection based test bed performance degradation assessment method of claim 1, wherein, The method further comprises the following steps: According to the predicted structure displacement, a shortest life path and a potential failure position on the test bench are identified by using a Miner linear cumulative damage method or a Coffin-Manson model.

9. A test bench performance degradation assessment system based on defect detection, characterized in that, The method comprises the following steps: A first construction module is configured to construct a finite element numerical model of the test bench based on at least two types of non-destructive detection methods; An order-reduced basis matrix acquisition module is configured to generate a displacement snapshot matrix by the finite element numerical model, perform singular value decomposition and order reduction on the displacement snapshot matrix to obtain an order-reduced basis matrix; A second construction module is configured to establish an initial order-reduced model by using the order-reduced basis matrix, and solve the initial order-reduced model by using a design load and a stiffness matrix to obtain an order-reduced model; A structure performance degradation evaluation module is configured to input a test load into the order-reduced model to obtain a predicted structure displacement of the test bench, collect an actual structure displacement of the test bench under the test load, and perform structure performance degradation evaluation of the test bench by using the predicted structure displacement and the actual structure displacement.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the defect detection-based test bench performance degradation evaluation method in any one of claims 1 to 8.

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