A defect detection method and device for nonlinear system identification

Abstract

The present invention provides a defect detection method and device for nonlinear system identification, which relates to the technical field of system identification; the method comprises: using a modal force hammer to perform hammer excitation on a test piece according to a test schedule to generate an excitation signal and a response signal; using a laser vibrometer to collect the excitation signal and the response signal, performing parameter calculation on the excitation signal and the response signal to obtain model parameters, constructing an initial Hammerstein model according to the model parameters, optimizing the initial Hammerstein model, and obtaining a Hammerstein model of the test piece; adjusting the Hammerstein model of the test piece by a cross-validation method, and performing defect judgment on the adjusted Hammerstein model of the test piece and a pre-constructed template test piece model, and determining whether the test piece has a defect according to the judgment result. Since the defect of the test piece will produce a nonlinear effect, the signal generated by the nonlinear effect is used to establish the nonlinear model of the test piece, so as to determine whether the test piece has a defect according to the nonlinear model.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of system identification, and in particular to a defect detection method and device for nonlinear system identification. Background Art

[0002] In the production and use of metal materials, defects in metal materials are a common problem. Metal parts are made of metal materials. If there are defects, the structure of the metal parts will be destroyed, thus affecting the normal operation and safety of the equipment. Therefore, metal material defect detection is a very important detection work in the metal industry. During the detection process, it is also necessary to ensure that no damage is caused to the detection object to avoid increasing defects, so non-destructive testing is performed on the detection object.

[0003] In the field of system identification, system identification is carried out through linear models and nonlinear models, and the parameter identification technology for linear models is relatively mature. However, if the state and output variables of a system cannot be described by a linear relationship under the influence of external conditions, the system belongs to a nonlinear system. Since nonlinear effects will occur when there are defects in metal materials, the research on combining system identification technology with nondestructive testing technology and putting it into actual production and testing is still in its infancy. Therefore, how to combine system identification technology with nondestructive testing technology has become a difficult problem that needs to be solved urgently. Summary of the invention

[0004] The technical problem to be solved by the present invention is to combine nondestructive testing technology with nonlinear system identification technology to detect defects in metal materials and provide a defect detection method and device for nonlinear system identification.

[0005] The technical solution of the present invention to solve the above technical problem is as follows: A defect detection method for nonlinear system identification comprises the following steps:

[0006] A test schedule is set according to the size of the specimen, a modal force hammer is used to perform hammer excitation on the specimen according to the test schedule to generate an excitation signal, and the specimen is vibrated based on the excitation signal to generate a response signal;

[0007] The excitation signal and the response signal are collected respectively by a laser vibrometer;

[0008] Performing nonlinear parameter calculation on the excitation signal and the response signal to obtain initial nonlinear parameters, performing linear parameter calculation on the excitation signal and the response signal to obtain initial linear parameters, constructing an initial Hammerstein model according to the initial nonlinear parameters and the initial linear parameters, and optimizing the initial Hammerstein model to obtain a Hammerstein model of the specimen;

[0009] The Hammerstein model of the specimen is adjusted by a cross-validation method, and the adjusted Hammerstein model of the specimen is compared with a pre-built template specimen model for defect judgment, and whether the specimen has defects is determined according to the judgment result.

[0010] Another technical solution of the present invention to solve the above technical problems is as follows:

[0011] A defect detection device for nonlinear system identification, comprising: a signal generating unit, a signal collecting unit, a model building unit and a defect detection unit;

[0012] The signal generating unit is used to set a test schedule according to the size of the specimen, perform hammer excitation on the specimen according to the test schedule by using a modal force hammer to generate an excitation signal, vibrate the specimen based on the excitation signal, and generate a response signal;

[0013] The signal acquisition unit is used to respectively acquire the excitation signal and the response signal through a laser vibrometer;

[0014] The model building unit is used to perform nonlinear parameter calculation on the excitation signal and the response signal to obtain initial nonlinear parameters, perform linear parameter calculation on the excitation signal and the response signal to obtain initial linear parameters, build an initial Hammerstein model according to the initial nonlinear parameters and the initial linear parameters, and optimize the initial Hammerstein model to obtain a Hammerstein model of the specimen;

[0015] The defect detection unit is used to adjust the Hammerstein model of the specimen by a cross-validation method, perform defect judgment on the adjusted Hammerstein model of the specimen and a pre-built template specimen model, and determine whether the specimen has defects according to the judgment result.

[0016] The beneficial effect of the present invention is that the present invention optimizes the structural damage detection technology of the test piece so that defects can be identified by establishing a nonlinear system model.

[0017] The system identification technology is used to perform non-destructive testing on the specimen. Since the micro defects on the specimen will produce nonlinear effects, the Hammerstein model is selected as the nonlinear model. It introduces fewer parameters to be identified and has strong flexibility. The model corresponding to the specimen is compared with the template model, and the model characteristics are summarized to perform qualitative analysis to achieve the purpose of defect detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a defect detection method for nonlinear system identification provided by an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the structure of a defect detection device for nonlinear system identification provided by an embodiment of the present invention;

[0020] Figure 3 A unit block diagram of a defect detection device for nonlinear system identification provided by an embodiment of the present invention.

[0021] In the accompanying drawings, the names of the components represented by the various marks are as follows:

[0022] 1. Test piece; 2. Modal force hammer; 3. Laser vibrometer; 4. Signal processing platform. DETAILED DESCRIPTION

[0023] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0024] Metal material defect detection technology is used in aerospace, petrochemical and automobile manufacturing, so it is very necessary to detect metal material defects. In the detection process, it is also necessary to ensure that no damage is caused to the detection object to avoid increasing defects, so non-destructive testing is performed on the detection object. However, the research on combining system identification technology with non-destructive testing technology and putting it into actual production and testing is still in its infancy. How to combine system identification technology with non-destructive testing technology has become a problem that needs to be solved urgently.

[0025] like Figure 1 As shown, a defect detection method for nonlinear system identification provided by an embodiment of the present invention includes the following steps:

[0026] A test schedule is set according to the size of the specimen, the specimen is hammered with a modal force hammer according to the test schedule to generate an excitation signal, and the specimen is vibrated based on the excitation signal to generate a response signal;

[0027] The excitation signal and the response signal are collected respectively by a laser vibrometer;

[0028] Performing nonlinear parameter calculation on the excitation signal and the response signal to obtain initial nonlinear parameters, performing linear parameter calculation on the excitation signal and the response signal to obtain initial linear parameters, constructing an initial Hammerstein model according to the initial nonlinear parameters and the initial linear parameters, and optimizing the initial Hammerstein model to obtain a Hammerstein model of the specimen;

[0029] The Hammerstein model of the specimen is adjusted by a cross-validation method, and the adjusted Hammerstein model of the specimen is compared with a pre-built template specimen model for defect judgment, and whether the specimen has defects is determined according to the judgment result.

[0030] Specifically, Figure 2 As shown, a piece of ordinary aluminum alloy sheet with a length of 250mm, a width of 50mm, and a thickness of 2mm is selected as a test piece, a micro crack is made on the test piece 1, and the test piece is fixed with a clamp. A modal force hammer 2 is used to apply an exciting force to the test piece according to the test schedule to make the test piece 1 vibrate. The force signal generated by the force is the excitation signal of the nonlinear system where the test piece 1 is located, and the response signal is obtained from the vibration displacement function (also called response function) of the test piece 1 under the corresponding excitation signal; after all the test groups in the test schedule are completed, the response signal and the excitation signal can be derived, and the vibration condition of the test piece 1 in each time period is processed in real time through the VSM-TEST signal processing platform (i.e., the signal processing platform 4);

[0031] Each time period can be understood as follows: since the hammering positions set in the test schedule are different, the hammering time periods are different, so the specimen 1 will vibrate in multiple time periods (ie, the test time corresponding to multiple test groups).

[0032] The laser vibrometer 3 includes a laser controller and a control module, wherein the control module includes an acquisition card; the control module of the laser vibrometer 3 is used to set relevant parameters of the laser vibrometer 3, including acquisition channels, sampling frequencies, trigger settings, and preprocessing functions;

[0033] The laser vibrometer 3 is used to collect excitation signals and response signals, and a laser vibrometer of the Julight brand is selected.

[0034] Before setting the test schedule according to the specimen size, it also includes:

[0035] Determine the setting parameters such as the size, material and metal plate defect range of the test piece 1, set the laser focal length according to the setting parameters through the Vibro Remote Console laser controller of the laser vibrometer 3, and turn on the laser.

[0036] Specifically, according to the basic data of the force hammer (i.e., modal force hammer), the basic parameters (i.e., related parameters) of the signal channel of the control module of the laser vibrometer 3 are set. Among them, channel 1 is set as the input channel, and its sensitivity is 2.42mV / N, and channel 2 is set as the output channel; and according to the size, material, and metal plate defect range of the tested piece, the sampling frame rate is set to 5.12KHz, the selected spectrum line is set to 1600, and the bandwidth is set to 2KHz; using linear average mode excitation, the trigger delay is set to -20ms, the trigger threshold is set to 0.4%, and the trigger hysteresis is set to 0.2%; the window function added to the input signal (i.e., the excitation signal) is set to a rectangular window function, and the window function added to the output signal (i.e., the response signal) is set to an exponential window, and its main window offset is 5ms and the attenuation constant is 100ms; in the preprocessing stage, a high-pass filter is applied to it, and the 3dB frequency 1 is 5Hz and the 3dB frequency 2 is 1.2KHz.

[0037] It also includes setting the direction of laser emission from the laser controller of the laser vibrometer 3 to the positive direction of the Z axis of the world coordinate system. Since the excitation signal is an exciting force applied to the specimen 1 to cause the specimen 1 to vibrate, the direction of the excitation signal is set to the -Z axis direction (i.e., opposite to the laser emission direction) in setting the test plan table; because the response signal is a signal representation of the vibration displacement function generated after the specimen 1 is excited, the direction of the response signal is set to the +Z axis direction (i.e., the same as the laser emission direction) in setting the test plan table. The laser (i.e., the laser in the positive direction of the Z axis) is turned on by the laser controller, and the modal dynamometer 2 performs hammering excitation on the specimen 1 according to the test schedule, generates an excitation signal and sends it to the control module; after the excitation signal is applied to the specimen 1, the specimen 1 is vibrated based on the excitation signal, and the vibration causes the laser to produce a slight change. The laser controller receives the slight change produced by the vibration (i.e., the generated response signal) and sends it to the control module; the excitation signal and the response signal are collected by the control module of the laser vibrometer 3, and the collected excitation signal and response signal are transmitted to the signal processing platform 4; the excitation signal and the response signal are processed by the signal processing platform 4, and the initial Hammerstein model and the specimen Hammerstein model are constructed to determine whether the specimen 1 has defects.

[0038] It should be understood that system identification is to determine a model equivalent to the observed system from a model (such as a linear model or a nonlinear model) based on the input data and output data of the observed system (that is, system identification is a technology that uses the input data and output data of the system to model), wherein the observed system is: a test system that generates corresponding signals (that is, excitation signals and response signals) by hammering the specimen with a modal force hammer according to the test schedule.

[0039] Nondestructive testing is performed using a modal force hammer. Since nondestructive testing does not cause any harm or damage to the object being tested while measuring it, the biggest advantage of nondestructive testing is that accurate data can be obtained without destroying the object being tested, thereby evaluating its performance, degree of damage and other information.

[0040] In the embodiment of the present invention, the nonlinear system identification technology is used to optimize and improve the model corresponding to the nonlinear system, that is, to improve the model parameter optimization algorithm, so as to improve the superiority and performance of the model. Referring to the hammering method in modal analysis, the metal material is hammered and excited by a modal force hammer to achieve the purpose of nondestructive testing, and the laser vibrometer is used to complete the collection and processing of the excitation signal and the response signal, so as to obtain the input data and output data (i.e., the excitation signal and the response signal) of the nonlinear system where the specimen is located. Since the defects of the specimen will produce nonlinear effects, the excitation signal and the response signal collected after the hammering of the specimen are processed to establish a nonlinear model of the specimen, so as to determine whether the specimen has defects according to the nonlinear model.

[0041] Preferably, the test schedule is set according to the specimen size, specifically:

[0042] The number of detection contacts is selected according to the size of the test piece, and the arrangement of the detection contacts is set, and the test schedule is set according to the arrangement.

[0043] It should be understood that the number and arrangement of the detection contacts of the laser vibrometer on the test piece are selected according to the size of the test piece, and then a test schedule is set up, and hammer excitation is applied in sequence according to the test sequence in the test schedule, and the response signal and excitation signal of each time period are collected by the control module of the laser vibrometer.

[0044] The test schedule can be used to apply multiple (i.e. different frequencies) excitation signals to the same specimen to obtain multiple corresponding response signals, which can greatly improve the accuracy of the model and fully avoid the accidental nature of the experiment.

[0045] In the embodiment of the present invention, the hammer detection electric shock is distributed according to the size of the metal test piece, so that the defects of the metal test piece can be detected in all directions, avoiding only local detection, which leads to incomplete detection of defects of the metal test piece and creates the risk of tiny cracks.

[0046] Preferably, after the step of collecting the excitation signal and the response signal respectively by using a laser vibrometer, the method further comprises:

[0047] Adding a rectangular window function to the excitation signal and adding an exponential window function to the response signal;

[0048] The excitation signal after adding the rectangular window function and the response signal after adding the exponential window function are subjected to noise reduction processing respectively.

[0049] It should be understood that the window function is a mathematical function used for signal processing and spectrum analysis. The corresponding window function is added to the excitation signal and the response signal respectively, and a window is applied to the original signal, so that the two ends of the original signal gradually decay, thereby reducing the impact of spectrum leakage (i.e. reducing spectrum leakage and improving spectrum resolution) for better analysis and processing. Among them, the rectangular window function has the advantage of the highest frequency recognition accuracy; the exponential window function can smooth the sudden sample points after weighting the signal, making the signal more continuous.

[0050] In the embodiment of the present invention, adding a window function to the signal helps to reduce spectrum leakage and suppress signal noise, and also helps to improve frequency resolution, and performs noise reduction processing on the signal to reduce or eliminate noise, so as to restore the clarity of the original signal and reduce errors generated during signal processing.

[0051] Preferably, the nonlinear parameter calculation is performed on the excitation signal and the response signal to obtain the initial nonlinear parameters, specifically:

[0052] The nonlinear parameters of the excitation signal and the response signal are calculated by a polynomial nonlinear function to obtain initial nonlinear parameters. The polynomial nonlinear function is:

[0053] p(x)=a0+a1x+a2x2+...+a n-1 x n-1 +a n x n ,

[0054] Wherein, p(x) represents the initial nonlinear parameter (i.e., the parameter data corresponding to the response signal generated by the specimen after the vibration of the excitation signal), x represents the static excitation parameter (i.e., the parameter data corresponding to the excitation signal), n represents the highest degree of the polynomial, a represents the polynomial coefficient, and a>0, a≠1.

[0055] It should be understood that the polynomial nonlinear function is used to describe the nonlinear relationship between the input variable (i.e., the excitation signal) and the output variable (i.e., the response signal). It approximates complex nonlinear phenomena through a series of power terms of the input variables, and is expressed as the weighted sum of the input variables.

[0056] In the embodiment of the present invention, the computational complexity of the polynomial nonlinear function is relatively low, and even high-order polynomials can be quickly calculated, which enables parameters to be quickly calculated in nonlinear systems. In addition, the polynomial nonlinear function can be used in combination with other types of models (such as linear dynamic models) to form a complex hybrid model (i.e., Hammerstein model) to describe the defects of the test piece.

[0057] Preferably, the linear parameter calculation is performed on the excitation signal and the response signal to obtain the initial linear parameters, specifically:

[0058] S1, performing linear parameter calculation on the excitation signal through a polynomial input function to obtain input linear parameters, wherein the polynomial input function is:

[0059]

[0060] Among them, A(q -1 ) represents the input linear parameter, q -1 represents the unit shift operator, Represents the input polynomial coefficients, n a represents the order of the input polynomial;

[0061] S2, calculating the linear parameters of the response signal by a polynomial output function to obtain output linear parameters, wherein the polynomial output function is:

[0062]

[0063] Among them, B(q -1 ) represents the output linear parameter, q -1 represents the unit shift operator, Represents the output polynomial coefficients, n b represents the order of the output polynomial;

[0064] S3, calculating the input linear parameter and the output linear parameter by using a discrete transfer function to obtain an initial linear parameter, wherein the discrete transfer function is:

[0065]

[0066] Among them, G(q -1 ) represents the initial linear parameters.

[0067] It should be understood that the unit shift operator represents the delay of the signal in time, and its function is to postpone the time index of the signal sequence or function by one unit; in discrete time signal processing, the unit shift operator can capture the change of the signal over time. The polynomial input function is the change function of the input signal (i.e., the excitation signal) after being calculated by the unit shift operator. The polynomial output function is the change function of the output signal (i.e., the response signal) after being calculated by the unit shift operator. The discrete transfer function is used to describe the linear relationship between the input signal and the output signal in discrete time, usually expressed as a difference equation, which describes the way in which the nonlinear system responds to the input signal.

[0068] In an embodiment of the present invention, the linear relationship between the input signal and the output signal in discrete time is calculated by a differential equation, and the discrete transfer function is used for linear analysis of the nonlinear system, which can more accurately analyze the dynamic response changes of the nonlinear system in which the specimen is located, and further describe the defects of the specimen.

[0069] Preferably, the initial Hammerstein model is optimized to obtain the specimen Hammerstein model, specifically:

[0070] The initial nonlinear parameters are optimized and calculated by using an APSO algorithm to obtain nonlinear parameters;

[0071] Perform fitting calculation on the initial linear parameters by the least square method to obtain linear parameters;

[0072] A Hammerstein model of the specimen is constructed according to the nonlinear parameters and the linear parameters.

[0073] It should be understood that the essence of parameter identification based on the APSO algorithm for the Hammerstein nonlinear model is to convert the parameter identification problem into a parameter space optimization problem, search the entire static nonlinear parameter domain in the model, and finally obtain the optimal parameters.

[0074] In the embodiment of the present invention, the APSO algorithm is used to optimize the parameters of the static nonlinear part of the model, and the individual local information and the group global information can be used to search for parameters, so that the convergence speed of the algorithm is improved, and the requirements for computer memory and CPU are not high, so that the optimal static parameters can be extracted while the identification speed is faster, that is, the difference between the identification model (i.e. the constructed initial Hammerstein model) and the actual model (i.e. the constructed specimen Hammerstein model) is minimized, and the model is more accurate. The least squares method is used to fit the parameters of the dynamic linear part of the model, and the fitting process is simple, the computational complexity requirement is low, the data fitting process can be completed in a short time, it is convenient and fast, and the error can be automatically corrected according to the actual situation, so as to obtain a more accurate fitting result. By adjusting the parameters calculated by the discrete transfer function, the dynamic response and steady-state error of the system can be optimized, so that the difference between the identification model and the actual model is minimized while the identification speed is faster, and the model is more accurate.

[0075] Preferably, after the step of collecting the excitation signal and the response signal respectively by a laser vibrometer, the method further comprises:

[0076] Hammering the test piece according to the test schedule using a modal force hammer to generate an excitation signal, vibrating the test piece based on the excitation signal, and generating a response signal;

[0077] The excitation signal and the response signal are collected respectively by a laser vibrometer;

[0078] The excitation signal and the response signal are taken as a set of data, the above operation is repeated multiple times, multiple sets of data are collected, and a data set is constructed based on the multiple sets of data.

[0079] In the embodiment of the present invention, a data set is constructed to adjust the specimen Hammerstein model through K-fold cross validation.

[0080] Preferably, the specimen Hammerstein model is adjusted by a cross-validation method, specifically:

[0081] The pre-constructed data set is divided into multiple training sets in the same proportion by the K-fold cross-validation method, one of the training sets is selected as the validation set, the Hammerstein model of the specimen is trained by the remaining training sets, and the trained Hammerstein model of the specimen is validated by the validation set to obtain a performance evaluation index, and this cycle is repeated, each training set is used as a validation set, and the performance evaluation indexes corresponding to all validation sets are obtained;

[0082] The performance evaluation indicators corresponding to all validation sets are averaged to obtain a performance evaluation mean, and the specimen Hammerstein model is adjusted according to the performance evaluation mean.

[0083] Specifically, the pre-constructed data set is divided into K subsets of similar size, called folds, by adopting the K-fold cross-validation method. Then, K training and validation are performed, and each time one of the folds is selected as the validation set, and the remaining K-1 folds are used as the training set. This process is repeated K times to ensure that each fold is used as a validation set. Finally, the performance indicators of the K validations are averaged as the performance evaluation results of the model. According to the performance evaluation results, the parameters of the specimen model are adjusted to improve the fitting performance of the model.

[0084] It should be understood that the cross-validation method is a validation technique for evaluating the performance of a data mining model.

[0085] In the embodiment of the present invention, the validity of the final model is verified by a cross-validation method, and the generalization ability of the model is evaluated by simulating the performance of the model on unknown data. The data set can be more fully utilized, the fluctuation of model performance caused by different data divisions can be reduced, the optimal parameters can be selected by comparing the model performance under different parameters, and whether the model has overfitting or underfitting problems can be detected to evaluate the model performance and select the best model parameter characteristics.

[0086] Preferably, the adjusted specimen Hammerstein model is compared with the pre-built template specimen model for defect judgment, and whether the specimen has defects is determined according to the judgment result, specifically:

[0087] The adjusted Hammerstein model of the specimen is compared with the pre-built template specimen model to obtain comparison features, and it is determined whether the specimen has defects based on the comparison features and preset defect conditions.

[0088] In the embodiment of the present invention, the intact specimen model and the defective specimen model are compared, the features are summarized, and the purpose of defect identification is achieved by observing the difference in expressions between the defective specimen model and the intact specimen model.

[0089] like Figure 3 As shown, a defect detection device for nonlinear system identification provided by an embodiment of the present invention includes: a signal generating unit, a signal collecting unit, a model building unit and a defect detection unit;

[0090] The signal generating unit is used to set a test schedule according to the size of the specimen, perform hammer excitation on the specimen according to the test schedule by using a modal force hammer to generate an excitation signal, vibrate the specimen based on the excitation signal, and generate a response signal;

[0091] The signal acquisition unit is used to respectively acquire the excitation signal and the response signal through a laser vibrometer;

[0092] The model building unit is used to perform nonlinear parameter calculation on the excitation signal and the response signal to obtain initial nonlinear parameters, perform linear parameter calculation on the excitation signal and the response signal to obtain initial linear parameters, build an initial Hammerstein model according to the initial nonlinear parameters and the initial linear parameters, and optimize the initial Hammerstein model to obtain a Hammerstein model of the specimen;

[0093] The defect detection unit is used to adjust the Hammerstein model of the specimen by a cross-validation method, perform defect judgment on the adjusted Hammerstein model of the specimen and a pre-built template specimen model, and determine whether the specimen has defects according to the judgment result.

[0094] The above-mentioned defect detection device for nonlinear system identification can refer to the implementation content and beneficial effects of the defect detection method for nonlinear system identification specifically described above, which will not be repeated here.

[0095] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0096] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0099] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A defect detection method for nonlinear system identification, characterized in that: The steps include: A test schedule is set according to the size of the specimen, the specimen is hammered with a modal force hammer according to the test schedule to generate an excitation signal, and the specimen is vibrated based on the excitation signal to generate a response signal; The excitation signal and the response signal are collected respectively by a laser vibrometer; Performing nonlinear parameter calculation on the excitation signal and the response signal to obtain initial nonlinear parameters, performing linear parameter calculation on the excitation signal and the response signal to obtain initial linear parameters, constructing an initial Hammerstein model according to the initial nonlinear parameters and the initial linear parameters, and optimizing the initial Hammerstein model to obtain a Hammerstein model of the specimen; The Hammerstein model of the specimen is adjusted by a cross-validation method, and the adjusted Hammerstein model of the specimen is compared with a pre-built template specimen model for defect judgment, and whether the specimen has defects is determined according to the judgment result; The adjusted specimen Hammerstein model is used to judge defects with the pre-built template specimen model, and whether the specimen has defects is determined according to the judgment result, specifically: The adjusted Hammerstein model of the specimen is compared with the pre-built template specimen model to obtain comparison features, and it is determined whether the specimen has defects based on the comparison features and preset defect conditions.

2. The defect detection method according to claim 1, characterized in that: The test schedule is set according to the specimen size, specifically: The number of detection contacts is selected according to the size of the test piece, and the arrangement of the detection contacts is set, and the test schedule is set according to the arrangement.

3. The defect detection method according to claim 1, characterized in that: After the step of collecting the excitation signal and the response signal respectively by means of a laser vibrometer, the method further comprises: Adding a rectangular window function to the excitation signal and adding an exponential window function to the response signal; The excitation signal after adding the rectangular window function and the response signal after adding the exponential window function are subjected to noise reduction processing respectively.

4. The defect detection method according to claim 1, characterized in that: The nonlinear parameter calculation is performed on the excitation signal and the response signal to obtain the initial nonlinear parameters, specifically: The nonlinear parameters of the excitation signal and the response signal are calculated by a polynomial nonlinear function to obtain initial nonlinear parameters. The polynomial nonlinear function is: p(x)=a0+a1x+a2x2+...+a n-1 x n-1 +a n x n , Wherein, p(x) represents the initial nonlinear parameter, x represents the static excitation parameter, a represents the polynomial coefficient, and n represents the highest degree of the polynomial.

5. The defect detection method according to claim 1, characterized in that: The linear parameter calculation is performed on the excitation signal and the response signal to obtain the initial linear parameters, specifically: S1, performing linear parameter calculation on the excitation signal through a polynomial input function to obtain input linear parameters, wherein the polynomial input function is: Among them, A(q -1 ) represents the input linear parameter, q -1 represents the unit shift operator, Represents the input polynomial coefficients, n a represents the order of the input polynomial; S2, calculating the linear parameters of the response signal by a polynomial output function to obtain output linear parameters, wherein the polynomial output function is: Among them, B(q -1 ) represents the output linear parameter, q -1 represents the unit shift operator, Represents the output polynomial coefficients, n b represents the order of the output polynomial; S3, calculating the input linear parameter and the output linear parameter by using a discrete transfer function to obtain an initial linear parameter, wherein the discrete transfer function is: Among them, G(q -1 ) represents the initial linear parameters.

6. The defect detection method according to claim 1, characterized in that: The initial Hammerstein model is optimized to obtain the specimen Hammerstein model, specifically: The initial nonlinear parameters are optimized and calculated by using an APSO algorithm to obtain nonlinear parameters; Perform fitting calculation on the initial linear parameters by the least square method to obtain linear parameters; A Hammerstein model of the specimen is constructed according to the nonlinear parameters and the linear parameters.

7. The defect detection method according to claim 1, characterized in that: The cross-validation method is used to adjust the Hammerstein model of the specimen, specifically: The pre-constructed data set is divided into multiple training sets in the same proportion by the K-fold cross-validation method, one of the training sets is selected as the validation set, the Hammerstein model of the specimen is trained by the remaining training sets, and the trained Hammerstein model of the specimen is validated by the validation set to obtain a performance evaluation index, and this cycle is repeated, each training set is used as a validation set, and the performance evaluation indexes corresponding to all validation sets are obtained; The performance evaluation indicators corresponding to all validation sets are averaged to obtain a performance evaluation mean, and the specimen Hammerstein model is adjusted according to the performance evaluation mean.

8. A defect detection device for nonlinear system identification, characterized in that: include: A signal generation unit, a signal acquisition unit, a model building unit and a defect detection unit; The signal generating unit is used to set a test schedule according to the size of the specimen, perform hammer excitation on the specimen according to the test schedule by using a modal force hammer to generate an excitation signal, vibrate the specimen based on the excitation signal, and generate a response signal; The signal acquisition unit is used to respectively acquire the excitation signal and the response signal through a laser vibrometer; The model building unit is used to perform nonlinear parameter calculation on the excitation signal and the response signal to obtain initial nonlinear parameters, perform linear parameter calculation on the excitation signal and the response signal to obtain initial linear parameters, build an initial Hammerstein model according to the initial nonlinear parameters and the initial linear parameters, and optimize the initial Hammerstein model to obtain a Hammerstein model of the specimen; The defect detection unit is used to adjust the Hammerstein model of the specimen by a cross-validation method, perform defect judgment on the adjusted Hammerstein model of the specimen and a pre-built template specimen model, and determine whether the specimen has defects according to the judgment result; In the defect detection unit, the adjusted specimen Hammerstein model and the pre-built template specimen model are used to perform defect judgment, and whether the specimen has defects is determined according to the judgment result, specifically: The adjusted Hammerstein model of the specimen is compared with the pre-built template specimen model to obtain comparison features, and it is determined whether the specimen has defects based on the comparison features and preset defect conditions.

9. The defect detection device according to claim 8, characterized in that: In the signal generating unit, the test schedule is set according to the specimen size, specifically: The number of detection contacts is selected according to the size of the test piece, and the arrangement of the detection contacts is set, and the test schedule is set according to the arrangement.

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