A simulation detection system and detection method for aircraft thin-wall structure

Abstract

The present invention relates to the field of damage detection of large-scale structures of modern aircraft, and specifically to a method and system for simulating detection of thin-walled structures of aircraft; detection is performed based on Lamb waves, and includes multiple spliced ​​thin-walled structures and array sensors arranged on the thin-walled structures; the array sensor includes N sensors arranged in the form of a phased array, which are used to generate an excitation signal and receive a response signal of a target; and further includes a detection device, which determines whether there is damage on the thin-walled structure based on the relationship between the response signal and the excitation signal; the multiple thin-walled structures are connected by gluing; this embodiment establishes a relationship between the damage scattering signal and the damage position by fusing the existing sparse representation with the elliptical imaging algorithm, obtaining the sparse characteristics of the monitoring signal, and the accurate judgment of the damage position by the elliptical imaging algorithm, thereby achieving qualitative and quantitative judgment of the damage position and making the result more accurate and specific.

Description

Technical Field

[0001] The present invention relates to the field of damage detection of large-scale structures of modern aircraft, and in particular to a simulation detection system and a detection method for thin-walled structures of aircraft. Background Art

[0002] Affected by dynamic / static loads, operating conditions, material properties, and external factors, modern aircraft structures often experience performance degradation and more severe structural damage, which affects their normal operation and may further lead to safety hazards or even catastrophic accidents. In addition, during the aircraft design and manufacturing stage, it is difficult to accurately predict the distribution of factors affecting the structural life during service. Even the most perfect design is difficult to avoid the risks brought about by changes in service conditions. As people's requirements for the safe, reliable, and economical operation of modern aircraft become increasingly prominent, the development of a reliable and efficient technology that can monitor structural damage and its development in real time and analyze the structural life is becoming a development trend and requirement in the modern aviation field. It is also the key to solving the problem of structural service safety.

[0003] Traditional inspection and repair methods for structural damage to metal or composite materials are primarily preventive inspection methods based on non-destructive testing / evaluation (NDT / NDE) technology. These methods involve local inspections of structural properties based on operating / service time, and require empirical support to estimate possible damage locations in advance. Furthermore, these inspection methods require a large number of specialized monitoring instruments that are large, complex, and heavy. Modern aircraft have even stricter weight requirements, and traditional methods generally cannot meet the needs for real-time monitoring and online detection of aircraft structural damage status.

[0004] Structural Health Monitoring (SHM) technology is an intelligent structural system that integrates sensors, actuators, and signal / information processing components into traditional structures through a specific method, combined with external computer data acquisition and analysis systems. SHM technology enables rapid, real-time, and accurate detection, diagnosis, and treatment of structural damage, addressing the shortcomings of traditional monitoring methods and attracting increasing attention from researchers.

[0005] Currently, SHM technologies for these types of damage are categorized by their implementation principles, including acoustic emission, Lamb wave detection, electromechanical impedance, structural vibration analysis, and stress-strain distribution. Among these, Lamb wave-based active structural health monitoring (SHM) methods, which excite and sense Lamb waves through the forward and inverse piezoelectric effects, are considered one of the most promising online structural health monitoring technologies due to their long propagation distance and high efficiency. These methods have been widely used in health monitoring of thin-walled structures.

[0006] The propagation characteristics of Lamb waves in a medium are determined by the excitation frequency and the material properties and thickness of the plate. When metal structures experience cracking, corrosion, or composite structures experience cracking, debonding, delamination, and impact damage, the structural properties change, affecting the propagation of Lamb waves within the structure. Therefore, in Lamb-wave-based SHM technology, by monitoring the changes that occur as Lamb waves propagate through thin-walled structures in real time and comparing and analyzing the signals obtained before and after structural damage, further structural damage identification can be performed. Specifically, when a structure is damaged, the monitoring signal changes simultaneously, and this change provides a basis for determining the type of structural damage and locating the damage. How to efficiently and quickly process the changes in the monitoring signal to obtain damage information in thin-walled structures is a hot topic of research.

[0007] In plate structures, Lamb wave damage scattering signals are sparse and transient. Combining sparse and redundant representation methods, the characteristics of damage scattering signals can be extracted. This process can be called sparse damage imaging.

[0008] In current sparse damage imaging, the orthogonal matching pursuit (OMP) algorithm is primarily used. However, traditional OMP algorithms often only image small damage. This is primarily because for a few pixels within a small area, the propagation time difference of the Lamb wave is almost zero due to the relatively small distance between them. This results in a small difference in the corresponding atoms constructed. The OMP algorithm achieves a sparse representation of the signal by searching for the best matching atom. This results in only one pixel in the corresponding area being identified as the primary damage pixel, while ignoring other pixels that may contain damage. Therefore, traditional OMP algorithms require pixels of comparable size to the damage to effectively localize damage, resulting in low imaging accuracy. Furthermore, when the pixels are small, it is difficult to image larger damage within the structure. Therefore, improvements to the OMP algorithm are needed to meet the precision requirements for structural damage imaging. Summary of the Invention

[0009] The present application provides a simulation detection system and method for thin-walled structures of aircraft, which can identify damage points and determine the extent of damage in large-scale thin-walled structures of aircraft. By constructing an overcomplete dictionary and optimizing the existing OMP algorithm, it can determine the location of damage and the extent of damage with greater accuracy. To achieve the above objectives, the present application adopts the following technical solutions:

[0010] In a first aspect, this embodiment provides a simulation detection system for thin-walled structures of aircraft, which performs detection based on ultrasonic Lamb waves and includes a plurality of spliced ​​thin-walled structures and an array sensor arranged on the thin-walled structures; the array sensor includes N sensors arranged in the form of a phased array, which is used to generate an excitation signal and receive a response signal of a target; and further includes a detection device that determines whether there is damage on the thin-walled structure based on the relationship between the response signal and the excitation signal; the plurality of thin-walled structures are connected by gluing.

[0011] With respect to the second possible implementation, in combination with the first aspect, the detection device includes a control module for controlling the sensors in the Lamb wave array to perform N combined detections on the target. In each combined detection, one sensor in the array is controlled as an excitation sensor, and the remaining N-1 sensors are controlled as receiving sensors to receive the structural response signal of the target; and also includes a calculation module for determining whether there is damage on the thin-walled structure based on the response signal.

[0012] This embodiment also proposes a simulation detection method for aircraft thin-walled structures based on the first aspect, which achieves accurate determination of the defect damage location and the damage extent.

[0013] In a second aspect, a method for simulating and detecting thin-walled structures of aircraft is provided to determine whether there is damage on the thin-walled structure and the extent of the damage; the method comprises the following steps: combining the corresponding exciters and sensors during the wave signal transmission process to obtain N(N-1) sensor pairs; obtaining N(N-1) Lamb wave response signals and their reference signals through the sensor pairs; determining the damage scattering signal based on the response signal and the reference signal; performing a preliminary sparse representation on the damage scattering signal based on an overcomplete dictionary to obtain a sparse representation of the damage scattering signal; corresponding to L×M virtual units based on the positions of the sensors and exciters in the N(N-1) sensor pairs and the sparse representation of the damage scattering signal to obtain N(N-1) elliptical band images; performing image fusion on the N(N-1) elliptical band images to obtain the intersection area of ​​the N(N-1) elliptical bands, and using the obtained intersection area as the defect damage location; using the center of the intersection area as the damage center, and obtaining a final sparse representation.

[0014] For the second possible implementation, in combination with the second aspect, obtaining N(N-1) Lamb wave response signals and their reference signals also includes the following processing: denoising the N(N-1) Lamb waves obtained by the sensor, and obtaining the Lamb response signal and its reference signal through the Lamb after denoising.

[0015] For the third possible implementation, in combination with the second possible implementation, the damage scattered signal is determined based on the response signal and the reference signal, specifically: the difference between the response signal and the reference signal is the damage scattered signal, specifically:

[0016] Where: y i，d，j (t) is the differential signal, In response to the signal, is the reference signal.

[0017] With respect to the fourth possible implementation, in combination with the third possible implementation, a preliminary sparse representation is performed on the damage scattering signal based on an overcomplete dictionary, including the following steps: decomposing the damage scattering signal to obtain a decomposed damage scattering signal, constructing a damage scattering signal dictionary matrix for the decomposed damage scattering signal based on the overcomplete dictionary, and performing sparse representation;

[0018] The decomposed damage scattering signal is:

[0019] Where: x p is the damage scattering factor, which represents the pixel value of the p-th pixel (x, y), and p = (x-1)n y +n x , n x and n y are the number of pixels in the L and M directions in the L×M virtual units respectively; a i，p，j is the damage scattering atom, which represents the damage scattering signal received by the jth sensor after the wave excited by the i-th sensor is scattered by the damage at the p-th pixel;

[0020] And in one excitation and response, the damage scattering signal on a certain path is:

[0021]

[0022] For the fifth possible implementation, combined with the fourth possible implementation, the damage scattering signal dictionary matrix is:

[0023] Y=A·X

[0024]

[0025] Where: A is the dictionary matrix; Y is the damage scattering signal matrix; X is the unknown vector matrix, representing the sparse damage location.

[0026] For the sixth possible implementation, in combination with the fifth possible implementation, performing a preliminary sparse representation on the damage scattering signal based on an overcomplete dictionary to obtain a sparse representation of the damage scattering signal includes the following steps:

[0027] A sparse representation of the damage scattered signal is obtained based on the damage scattered signal dictionary matrix, and the sparse representation of the damage scattered signal is taken as:

[0028]

[0029] For the seventh possible implementation, in combination with the sixth possible implementation, image fusion is performed on the N(N-1) elliptical band images to obtain an intersection area of ​​the N(N-1) elliptical bands, and the obtained intersection area is used as the defect damage location, including the following methods:

[0030] By x i,j Get the elliptical band image, for x i,j The obtained images are fused;

[0031] The |x i,j |Equal to x.

[0032] For the eighth possible implementation, combined with the seventh possible implementation, the final sparse representation is obtained by obtaining the best matching atom and the threshold in the iterative process based on the overcomplete dictionary, so that the best matching atom and the threshold satisfy the first relationship, thereby determining the sparse representation value; the first relationship is specifically:

[0033]

[0034] in: is the atom matched in the nth iteration, γ n To match the atomic number of the atom;

[0035] The sparse representation value is:

[0036]

[0037] The embodiments of the present application provide a system and method for simulating and detecting thin-walled structures of aircraft. By integrating the existing sparse representation with the elliptical imaging algorithm, the sparse characteristics of the monitoring signal are obtained, and the elliptical imaging algorithm accurately determines the damage location, a relationship between the damage scattering signal and the damage location is established, and qualitative and quantitative judgments of the damage location are achieved, making the results more accurate and specific. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] The methods, systems, and / or programs in the accompanying drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example numerals represent similar structures in the various views of the drawings.

[0040] Figure 1 is a schematic diagram of an aircraft thin-wall structure simulation and detection system according to an embodiment of the present application;

[0041] Figure 2 is a schematic diagram of a thin-walled structure according to an embodiment of the present application;

[0042] Figure 3 is a flow chart of a method for simulating and detecting thin-walled structures of aircraft according to an embodiment of the present application;

[0043] Figure 4 This is the first comparison diagram of the experimental example;

[0044] Figure 5 This is the second comparison chart of the experimental example. DETAILED DESCRIPTION

[0045] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0046] In the following detailed description, numerous specific details are set forth by way of example in order to provide a thorough understanding of the relevant teachings. However, it will be apparent to one skilled in the art that the present application can be practiced without these details. In other instances, well-known methods, procedures, systems, compositions, and / or circuits have been described at a relatively high level, without detail, to avoid unnecessarily obscuring aspects of the present application.

[0047] Flowcharts are used in this application to illustrate the execution processes performed by the system according to the embodiments of the present application. It should be clearly understood that the execution processes of the flowcharts may not be executed in sequence. Instead, these execution processes may be executed in reverse order or simultaneously. In addition, at least one additional execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0048] See Figure 1 This embodiment provides a simulated inspection system 100 for aircraft thin-walled structures. This system uses ultrasonic Lamb waves for inspection. The inspection target is a thin-walled structure composed of multiple spliced ​​structures. An array sensor is installed on the thin-walled structure. The array sensor comprises N sensors arranged in a phased array, configured to generate excitation signals and receive target response signals. The system also includes a detection device that determines whether damage exists on the thin-walled structure based on the relationship between the response signal and the excitation signal.

[0049] Specifically, the test platform includes an excitation component and a data acquisition component. Furthermore, its complete operation requires corresponding monitoring specimens. The excitation component of the test platform consists of a signal generator 110, a piezoelectric driver 120, and an excitation element 130; the data acquisition component primarily comprises a sensor element 140, a charge amplifier 150, and an engineering host computer 160. In this embodiment, the test platform software on the engineering host computer can be used to set the desired excitation waveform, which in this case is a Lamb wave. The signal generator generates a corresponding excitation signal, which is converted into a voltage signal by the piezoelectric driver to drive the piezoelectric actuator to generate an actuating force / displacement, causing the thin-walled structure of the test specimen to generate a corresponding waveform fluctuation at the location of the actuator. The fluctuation further propagates along the thin-walled structure of the specimen and is received by piezoelectric sensor elements distributed on the surface or embedded within the thin-walled structure. The piezoelectric sensor elements convert the vibration signal into a corresponding charge and output it. After signal conditioning and transformation by the charge amplifier, the signal is transmitted to the data acquisition system of the engineering host computer. The monitoring waveform and corresponding data are then output on the test platform software to meet the test requirements and verify various damage monitoring algorithms.

[0050] In this embodiment, the thin-walled structure uses small pieces of fiberglass reinforced plastic (FRP) plate specimens, which are spliced ​​together to form a larger thin-walled plate structure. In addition, the piezoelectric ceramic elements used in the experiment will be embedded in the FRP plate specimens to simulate the smart material structure.

[0051] In real-world aircraft structures, thin-walled composite structures are often connected using bolting, slotting, and gluing. Bolting typically relies on the framework, specifically the aircraft's structural beams and ribs. Adhesive bonding, on the other hand, typically uses epoxy-based adhesives, which bond between the structures and also connect the main structure to the external frame. During aircraft assembly, thin-walled structures often utilize a combination of these methods to meet the requirements of the aircraft.

[0052] For this detection system, when bolted connections are used, assembly and disassembly of thin-walled structures is relatively simple. However, the propagation of Lamb waves between thin walls relies solely on the bolted connections. Therefore, the influence of the tightness of the bolted connections, that is, the influence of their preload on Lamb wave propagation, needs to be studied. Furthermore, because the bolted connections do not tightly bind the boundaries between the thin walls, waves in the thin-walled structures will be reflected and scattered at the boundaries of the thin walls, increasing the complexity of subsequent signal processing.

[0053] When using adhesive bonding to connect structures, the assembly and disassembly of thin-walled structures is relatively complex. To facilitate testing, the bonding method must ensure that the thin-walled structure is relatively stable at room temperature and can be quickly debonded using a method such as heating or a debonding agent. Furthermore, the bonding requires a positioning reference to ensure the distance between the structures and a uniform thickness of the adhesive layer to reduce the impact of adhesive thickness on wave propagation. However, through adhesive bonding, the connection between the structures is tight, and Lamb waves can propagate from one thin-walled structure to another through the cured adhesive, effectively reducing the reflection and scattering of waves at the boundaries between structures.

[0054] Based on an analysis of the joining methods commonly used for large, thin-walled structures on the two aforementioned aircraft, and in light of the test conditions and practical circumstances, this experiment will employ adhesive bonding to achieve the joining of thin-walled structures. This method simplifies the connection between thin-walled structures in aircraft, while also ensuring Lamb wave propagation between thin-walled structures and reducing boundary reflections, effectively simulating damage monitoring of thin-walled structures.

[0055] See Figure 2 , is a connection structure between thin-walled structures in this detection system, wherein two adjacent thin-walled structures 200 are connected by an adhesive layer 300.

[0056] When exciting and sensing Lamb waves and their response signals in an FRP plate, the excitation and sensor elements must conform to a specific pattern, namely, an array of excitation and sensor elements. In this detection system, an FRP plate with embedded piezoelectric ceramic elements is mounted at the four corners of a thin-walled FRP structure.

[0057] The embodiment of this city provides a simulation detection method for thin-walled structures of aircraft based on the detection system to determine whether there is damage on the thin-walled structure and the extent of the damage.

[0058] See Figure 3 , specifically including the following steps:

[0059] 101. Combine the corresponding exciters and sensors during the wave signal transmission process to obtain N (N-1) sensor pairs.

[0060] In this embodiment, the basis of the analog detection method is the Delay-and-Sum (DAS) method. According to the signal excitation and acquisition principle of the DAS method, if it is assumed that the number of sensors in the sensor array is N, then the number of exciter-sensor pairs in the system is N(N-1), and at this time, a total of N(N-1) groups of signals are collected.

[0061] 102. Obtain N(N-1) Lamb wave response signals and their reference signals through sensor pairs.

[0062] In this embodiment, the response signal is the signal generated by the Lamb wave received by the sensor, and the reference signal is the signal strength that the sensor should obtain when the object to be measured is not damaged. Based on these two signals, the damage scattering signal can be determined.

[0063] 103. De-noise the N(N-1) Lamb waves obtained by the sensor pair, and obtain the Lamb response signal and its reference signal through the de-noised Lamb waves.

[0064] 104. Determine a damage scattering signal based on the response signal and the reference signal.

[0065] In this embodiment, when the actuator i generates an excitation, the signal collected by the sensor j is It can be expressed as:

[0066]

[0067] Where: y i，j (t) represents the Lamb signal propagating from the excitation point to the receiving point, including the structure boundary reflection. In this embodiment, it can be equivalent to the reference signal in the intact thin-walled structure.

[0068] y i，d，j (t) is the damage scattering signal. To obtain the damage scattering signal, the difference between the signals collected by the corresponding sensors on the monitored thin-walled structure and the reference undamaged thin-walled structure can be used as the damage scattering signal. That is, the damage scattering signal can be expressed as:

[0069]

[0070] in: In response to the signal, is the reference signal.

[0071] 105. Perform preliminary sparse representation on the damage scattering signal based on the overcomplete dictionary to obtain a sparse representation of the damage scattering signal.

[0072] In this embodiment, the sparse representation algorithm provides an extremely flexible signal expression method. By giving an appropriate overcomplete atomic dictionary, the sparse representation of the signal on the dictionary can be solved, thereby better obtaining the effective information contained in the signal, extracting the characteristic parameters carried by the signal, and reducing the complexity of subsequent signal processing.

[0073] The following steps are involved:

[0074] 105a. Decompose the damage scattering signal.

[0075] In this embodiment, the monitored structure is a linear structure. Since damage scattering may exist in all n p The differential signal is composed of the linear superposition of different signals, so the differential signal can be decomposed into:

[0076]

[0077] Where: x p is the damage scattering factor, which represents the pixel value of the p-th pixel (x, y), and p = (x-1)n y +n x , n x and n y are the number of pixels in the L and M directions in the L×M virtual units respectively;

[0078] a i，p，j is the damage scattering atom, which represents the damage scattering signal received by the jth sensor after the wave excited by the i-th sensor is scattered by the damage located at the p-th pixel.

[0079] And in one excitation and response, the damage scattering signal on a certain path is:

[0080]

[0081] 105b. Construct a damage dictionary matrix based on the decomposed damage scattering signal.

[0082] The decomposition of the damage scatter signal can be performed for all sensor pairs, and the damage probability is represented by the damage scatter factor. For a distributed array of N sensors, the damage scatter signal forms a linear equation Y = AX for all N (N-1) sensor pairs:

[0083] Y=A·X

[0084]

[0085] Where: A is the dictionary matrix; Y is the damage scattering signal matrix; X is the unknown vector matrix, representing the sparse damage location; x i,j The scattering signal y received by the jth sensor is the excitation of the i-th sensor i,d,j Sparse representation of A i,j For the corresponding dictionary.

[0086] In this embodiment, the overcomplete dictionary

[0087] In this embodiment, each atom in the overcomplete dictionary can simulate the forward propagation of the wave based on the given Lamb wave frequency domain signal X(f), its phase velocity cp(f) at the center frequency, and the damage of the corresponding pixel point p, that is:

[0088]

[0089] Among them, d i，p and d p，j , respectively represent the sensor pair AS ij The distance from the actuator and sensor to the p-th pixel.

[0090] In this embodiment, considering the waveform time difference between the excitation point and the monitoring point due to the propagation process, then:

[0091] X(f)=F{x[t,t i，p，j (x, y)]};

[0092] Where: t i，p，j (x, y) is the waveform time difference between the excitation signal and the monitoring signal, that is, the time it takes for the signal to propagate from the excitation point to the p-th pixel point and then further to the monitoring point.

[0093] Among them, x[t, t i，p，j (x, y)] = w1[t, t i，p，j (x,y)]·w2[t,t i，p，j (x, y)]·sin2πf c [tt i，p，j (x, y)];

[0094] Among them, w1[t,t i，p，j(x, y)] is a rectangular window function, specifically:

[0095]

[0096] Among them, w2[t,t i，p，j (x, y)] is the Han window function, specifically:

[0097]

[0098] 105c. Obtain a sparse representation of the damage scattering signal based on the damage scattering signal dictionary matrix.

[0099] In this embodiment, for a primary excitation and response in monitoring, when there is damage in the thin-walled structure, the damage position is fixed, and the element in the vector matrix X is unique, then:

[0100] x 1，2 =x 1，3 =…=x i，j =…=x N，N-1 ;

[0101] In this embodiment, the damage scattering signal y collected by each sensor pair is i，j (t), there is a corresponding solution vector x i，j , in the process of determining the sparse representation vector, due to the influence of noise and boundary reflection, it is difficult to ensure that x 1，2 =x 1，3 =…=x i，j =…=x N，N-1 Absolutely true.

[0102] Therefore, in this embodiment, the value of the sparse representation x of the final damage position is x i，j The absolute value mean of replaces x, that is

[0103]

[0104] 106. According to the positions of the sensors and the actuators in the N(N-1) sensor pairs and the sparse representation of the damage scattering signal, corresponding to L×M virtual units, N(N-1) elliptical band images are obtained.

[0105] In this embodiment, specifically, by passing x i,j Get the elliptical band image, for x i,j The obtained images are fused.

[0106] 107. The center of the intersection region is taken as the damage center and the final sparse representation is obtained.

[0107] The best matching atom and the threshold value are obtained in the iterative process based on the overcomplete dictionary, so that the best matching atom and the threshold value satisfy a first relationship, thereby determining the sparse representation value; the first relationship is specifically:

[0108]

[0109] in: is the atom matched in the nth iteration, γ n To match the atomic number of the atom;

[0110] The final sparse representation value is:

[0111]

[0112] In one or more embodiments, a terminal device is disclosed, including a server. The server includes a memory, a processor, and a computer program stored in the memory and executable by the processor. When the processor executes the program, a method for simulating and inspecting thin-walled structures of an aircraft is implemented. For the sake of brevity, this description is omitted here.

[0113] It should be understood that in this embodiment, the term "module" may include a unit implemented in the form of hardware, software, or firmware, and may be used interchangeably with other terms such as "logic," "logic block," "component," and "circuit." A module may be a single integrated component suitable for performing one or more functions, or the smallest unit or component of the single integrated component. For example, according to an embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0114] A computer-readable signal medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may have a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. A computer-readable signal medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, device, or apparatus to communicate, propagate, or transmit a program for use. The program code on the computer-readable signal medium may be propagated via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0115] The computer program code required for the execution of various aspects of the present application can be written in any combination of one or more programming languages, including object-oriented programming, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., or similar conventional programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy or other programming languages. The programming code can be executed entirely on the user's computer, or as a separate software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or as a service such as software as a service (SaaS).

[0116] Experimental example

[0117] Specific experimental examples are given below to further illustrate the technical effects of this technical solution.

[0118] This experimental example uses Abaqus finite element simulation to simulate the presence of a single type of damage or multiple types of damage in a thin-walled structure. Damage imaging is then performed based on the simulated data. Finally, the detection method is analyzed by evaluating the damage imaging performance. The thin-walled structure used in the simulation measures 500×500×2mm. The excitation uses a five-cycle Lamb wave with a center frequency of 100 modulated by a Hann window. Single-point excitation is used to generate an A0-mode Lamb wave in the thin-walled structure. This section analyzes the performance of the aircraft thin-walled structure simulation detection method provided by this embodiment by comparing the damage images obtained using the thin-wall defect damage method in this embodiment with existing methods.

[0119] In this experimental example, six sets of signals were first obtained from simulated measurements without simulated damage, serving as baseline signals. After introducing simulated damage, six sets of signals were measured using the simulated parameters without simulated damage. These signals were then subtracted from the baseline signals to obtain the damage scattering signals. During imaging, the coordinate origin was selected at the upper left corner of the thin-walled structure and discretized into a 2×2 mm grid.

[0120] Experimental example

[0121] See Figure 4 , which are the damage scattering signals and their envelopes under the condition of single hole damage.

[0122] Figure 4-1 The damage scattering signal and its envelope on the AS12 path when a single hole is damaged.

[0123] Figure 4-2 The damage scattering signal and its envelope on the AS13 path when a single hole is damaged.

[0124] Figure 4-3 The damage scattering signal and its envelope on the AS14 path when a single hole is damaged.

[0125] Figure 4-4 The damage scattering signal and its envelope on the AS23 path when a single hole is damaged.

[0126] Figure 4-5 The damage scattering signal and its envelope on the AS24 path when a single hole is damaged.

[0127] Figure 4 -6 is the damage scattering signal and its envelope on the AS34 path when a single hole is damaged.

[0128] See Figure 5 , which are damage images after the aircraft thin-wall structure simulation detection method provided by this embodiment, and damage images after being processed by the existing sparse imaging method.

[0129] in, Figure 5-1 This is a damage image after the aircraft thin-wall structure simulation detection method provided by this embodiment. Figure 5-2 This is the damage image processed based on the existing sparse imaging method.

[0130] Through for Figure 5-1 and Figure 5-2 It can be seen that the error of the damage image after the existing sparse imaging processing is significantly greater than the damage image after the aircraft thin-wall structure simulation detection method provided in this embodiment.

[0131] The above disclosures of the embodiments of this application are clear and complete for those skilled in the art. It should be understood that the deduction and analysis of unexplained technical terms by those skilled in the art based on the above disclosures are based on the contents described in this application, and therefore the above disclosures do not constitute a judgment on the creativity of the overall solution.

[0132] The basic concepts have been described above. It will be apparent to those skilled in the art that the detailed disclosure provided above is merely illustrative and does not limit the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to the present application. Such modifications, improvements, and amendments are suggested in the present application and remain within the spirit and scope of the exemplary embodiments of the present application.

[0133] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or more in different parts of this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in at least one embodiment of this application may be appropriately combined.

[0134] In addition, it will be understood by those skilled in the art that the various aspects of the present application can be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or material combination, or any new and useful improvement thereof. Accordingly, the various aspects of the present application can be performed entirely by hardware, can be performed entirely by software (including firmware, resident software, microcode, etc.), or can be performed by a combination of hardware and software. The above hardware or software can all be referred to as "units", "components" or "systems". In addition, the various aspects of the present application can be expressed as a computer product located in at least one computer-readable medium, and the product includes computer-readable program code.

[0135] In addition, unless expressly stated in the scope of the patent application, the order of the processing elements and sequences described in this application, the use of digital letters, or the use of other names is not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached scope of the patent application is not limited to the disclosed embodiments. On the contrary, the scope of the patent application is intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of the present application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0136] It should also be understood that, in order to simplify the presentation of this disclosure and thereby facilitate understanding of at least one embodiment of the invention, the foregoing description of the embodiments of this application sometimes combines multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this application requires more features than those recited in the claims. In fact, an embodiment may have fewer features than the totality of the features of a single disclosed embodiment.

Claims

1. A simulation detection system for aircraft thin-walled structures, based on ultrasonic Lamb waves for detection, characterized in that: The invention comprises a plurality of spliced ​​thin-walled structures and an array sensor arranged on the thin-walled structures; the array sensor comprises N sensors arranged in a phased array, which are used to generate an excitation signal and receive a response signal of a target; the invention also comprises a detection device, which determines whether there is damage on the thin-walled structure based on the relationship between the response signal and the excitation signal; the plurality of thin-walled structures are connected by gluing; the detection device comprises a control module, which is used to control the sensors in the Lamb wave array to perform N combined detections on the target, and in each combined detection, controls one sensor in the array as an excitation sensor and the remaining N-1 sensors as receiving sensors to receive the structural response signal of the target; the invention also comprises a calculation module, which is used to determine whether there is damage on the thin-walled structure based on the response signal.

2. A method for simulating and detecting thin-walled structures of aircraft, characterized in that: Based on the aircraft thin-walled structure damage simulation detection module according to claim 1, determining whether there is damage on the thin-walled structure includes the following steps: Combine the corresponding exciters and sensors during the wave signal transmission process to obtain N (N-1) sensor pairs; Acquire N(N-1) Lamb wave response signals and their reference signals through the sensor pair; determining a damage scattering signal based on the response signal and the reference signal; Performing a preliminary sparse representation on the damage scattering signal based on an overcomplete dictionary to obtain a sparse representation of the damage scattering signal; According to the positions of the sensors and the actuators in the N(N-1) sensor pairs and the sparse representation of the damage scattering signal, corresponding to L×M virtual units, N(N-1) elliptical band images are obtained; Performing image fusion on the N(N-1) elliptical band images to obtain an intersection area of ​​the N(N-1) elliptical bands, and using the obtained intersection area as a defect damage location; The center of the intersection region is taken as the damage center, and the final sparse representation is obtained.

3. The aircraft thin-wall structure simulation detection method according to claim 2, characterized in that: Obtaining N(N-1) Lamb wave response signals and their reference signals also includes the following processing: The sensor is used to reduce the noise of N(N-1) Lamb waves, and the Lamb response signal and the reference signal are obtained through the Lamb waves after the noise reduction.

4. The aircraft thin-wall structure simulation detection method according to claim 2, characterized in that: Determining a damage scattering signal based on the response signal and the reference signal is specifically as follows: The difference between the response signal and the reference signal is the damage scattering signal. Specifically: ； in: is a differential signal, In response to the signal, is the reference signal.

5. The aircraft thin-wall structure simulation detection method according to claim 4, characterized in that: Performing a preliminary sparse representation on the damage scattering signal based on an overcomplete dictionary to obtain a sparse representation of the damage scattering signal includes the following steps: The damage scattering signal is decomposed, and the decomposed damage scattering signal is: ； in: is the damage scattering factor, which represents the pixels Pixel value, and , and are the number of pixels in the L and M directions in the L×M virtual units respectively; is the damage scattering atom, indicating the The wave excited by the first sensor is located at After the damage of pixel points is scattered, The damage scattered signal received by each sensor; And in one excitation and response, the damage scattering signal on a certain path is: 。 6. The aircraft thin-wall structure simulation detection method according to claim 5, characterized in that: The damage scattering signal dictionary matrix is: ； in: is the dictionary matrix; is the damage scattering signal matrix; is an unknown vector matrix, representing the sparse damage location; For the Sensor excitation, The scattered signal received by the sensor The sparse representation of For the corresponding dictionary.

7. The aircraft thin-wall structure simulation detection method according to claim 6, characterized in that: Performing a preliminary sparse representation on the damage scattering signal based on an overcomplete dictionary to obtain a sparse representation of the damage scattering signal includes the following steps: A sparse representation of the damage scattered signal is obtained based on the damage scattered signal dictionary matrix, and the sparse representation of the damage scattered signal is taken as: 。 8. The aircraft thin-wall structure simulation detection method according to claim 7, characterized in that: Performing image fusion on the N(N-1) elliptical band images to obtain an intersection area of ​​the N(N-1) elliptical bands, and using the obtained intersection area as a defect damage location, includes the following methods: pass Get the elliptical band image, The obtained images are fused; in, equal .

9. The aircraft thin-wall structure simulation detection method according to claim 7, characterized in that: The final sparse representation is obtained by obtaining the best matching atom and the threshold in the iterative process based on the overcomplete dictionary, so that the best matching atom and the threshold satisfy a first relationship, thereby determining the sparse representation value; the first relationship is specifically: ； in: For the The atoms matched by iterations, To match the atomic number of the atom; The final sparse representation value is: 。

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