An acoustic emission detection method and detection system based on optical stress waves

Abstract

The present invention belongs to the technical field of acoustic emission detection sensors, and particularly relates to an acoustic emission detection method and detection system based on optical stress waves. The instantaneous stress of a material to be measured is converted into an instantaneous voltage by a piezoelectric energy harvesting layer, which excites an electroluminescent layer to emit an optical signal. The optical signal is converted into an optical ray array through the array holes on the microporous array layer, and a three-dimensional blocking light spot is formed on the photochromic layer. An optical fiber is excited to emit multi-color light rays towards the photochromic layer, and the light rays passing through the three-dimensional blocking light spot are received by a receiving optical fiber and converted into a spectral measurement signal to complete the spectral feature analysis. The present invention utilizes the cascade effect of the laminated structure to capture tiny stress waves, and forms a changing blocking light spot signal on the photochromic layer. Therefore, by using the principles of absorption, diffraction, and diffraction of different frequency light waves by the three-dimensional blocking light spot, the rapidly changing light spot feature change signal is converted into a spectral measurement signal to complete the spectral feature analysis, realizing stress wave detection with a wider frequency band and higher sensitivity.

Description

Technical Field

[0001] The present invention belongs to the technical field of acoustic emission detection sensors, and particularly relates to an acoustic emission detection method and a detection system based on optical stress waves. Background Art

[0002] Acoustic emission detection is a non-destructive testing (NDE) method that can be used to inspect the internal condition of materials or structures. Special sensors and recording devices are used to capture stress waves (acoustic emission activities) generated and emitted within the material. Acoustic emission technology is widely applied in various engineering fields, such as oil and gas, chemistry, power supply, and other heavy industries.

[0003] The structure of an acoustic emission detection sensor generally includes a piezoelectric element, a coupling layer, a preamplifier, and a signal conditioning and processing system. When a material is subjected to an external force, local high-frequency elastic waves are released, and these elastic waves propagate to the material surface in the form of sound waves. After the sound waves on the material surface reach the sensor, they are transmitted to the piezoelectric element through the coupling layer of the sensor. The piezoelectric element uses the piezoelectric effect to convert mechanical vibration into an electrical signal, and the preamplifier preliminarily amplifies the electrical signal, and finally transmits it to the signal conditioning and processing system for signal processing and analysis. This traditional acoustic emission sensor is limited by the frequency response range and sensitivity, and it is difficult to capture subtle changes at the microscale and in ultrafast processes, thus it is difficult to achieve high time resolution and high spatial resolution simultaneously. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the defect that the traditional acoustic emission sensor with a traditional structure has a low frequency response range and sensitivity in the prior art, so as to provide an acoustic emission detection method and a detection system based on optical stress waves.

[0005] An acoustic emission detection method based on optical stress waves includes the following steps:

[0006] Step S1: Stack a piezoelectric energy harvesting layer, an electroluminescent layer, a microporous array layer, and a photochromic layer in sequence, and attach the piezoelectric energy harvesting layer to the object to be measured.

[0007] Step S2: Inject multi-color light into one end of the photochromic layer through an excitation optical fiber, and receive the multi-color light at the other end of the photochromic layer through a receiving optical fiber.

[0008] Step S3: A calculation unit acquires the optical signal of the receiving optical fiber to obtain a spectral measurement signal, and performs spectral feature analysis on the spectral measurement signal.

[0009] Further, step S3 includes the following steps:

[0010] Step S3.1: Obtain the spectrum of the spectral measurement signal, fit the spectral shift by the least squares method, and calculate the fitting parameters and the fitted spectrum;

[0011] Step S3.2: Process the fitted spectrum through a window function to calculate the preprocessed spectrum;

[0012] Step S3.3: Extract the energy and frequency information of the preprocessed spectrum through the short-time Fourier transform to calculate the time-frequency representation function;

[0013] Step S3.4: Extract the energy distribution of the time-frequency representation function at different time points to calculate the energy time-varying function;

[0014] Step S3.5: Extract the frequency distribution of the time-frequency representation function at different time points to calculate the frequency time-varying function.

[0015] Furthermore, the step S3.1 includes the following method steps:

[0016] Step S3.1.1: Define the model function:

[0017] S(λ) = w1 * I(λ) + w2 * I(θ(λ)) + w3 * T(λ);

[0018] where λ is the wavelength, I(λ) is the incident spectrum, θ(λ) is the phase function, T(λ) is the transmission function; w1, w2, w3 are the fitting parameters;

[0019] Step S3.1.2: Initialize the fitting parameters w1, w2, w3;

[0020] Step S3.1.3: Calculate the residual:

[0021] R = Σ(S measured (λ) - S(λ))^2;

[0022] where, S measured (λ) represents the spectral measurement signal;

[0023] Step S3.1.4: Use the gradient descent method to minimize the residual:

[0024] ;

[0025] where, η represents the learning rate, wi(new) represents the i-th fitting parameter of the current iteration, and wi(old) represents the updated i-th fitting parameter;

[0026] Step S3.1.5: Repeat steps S3.1.3 to S3.1.4 until convergence to obtain the fitting parameters w1, w2, w3 and the fitted spectrum Sfitted (λ).

[0027] Further, the step S3.2 includes the following method steps:

[0028] Step S3.2.1: Normalize the fitted spectrum;

[0029] Step S3.2.2: Select the window function w(λ);

[0030] Step S3.2.3: Apply the window function to each time point of the fitted spectrum to obtain the preprocessed spectrum:

[0031] S windowed (λ) = S fitted (λ) * w(λ).

[0032] Further, the step S3.3 includes the following method steps:

[0033] Step S3.3.1: Discretize the continuous preprocessed spectrum S windowed (λ) into a discrete spectrum x[n]:

[0034] x[n] = S fitted (λn);

[0035] where λn is the discrete wavelength point;

[0036] Step S3.3.2: Select the window function w[n];

[0037] Step S3.3.3: Perform a short-time Fourier transform on the discrete spectrum to obtain the time-frequency representation function:

[0038] X[m,k] = ∑[x[n]w[n-m]e^(-j2πkn / N)];

[0039] where m is the time index, k is the frequency index, and N is the fast Fourier transform length.

[0040] Further, the step S3.4 includes the following method steps:

[0041] Step S3.4.1: Calculate the short-time Fourier transform magnitude spectrum of the time-frequency representation function to obtain the short-time Fourier transform magnitude spectrum:

[0042] |X[m,k]| = sqrt(Re{X[m,k]}^2 + Im{X[m,k]}^2);

[0043] Step S3.4.2: Calculate the spectral energy at each time point of the short-time Fourier transform magnitude spectrum:

[0044] E[m] = ∑(|X[m,k]|^2);

[0045] Step S3.4.3: Calculate the change of spectral energy over time based on the spectral energy to obtain the energy time-varying function:

[0046] E(t) = E[m];

[0047] where t = m * Δt, and Δt represents the time resolution.

[0048] Further, the step S3.5 includes the following method steps:

[0049] Step S3.5.1: For each time point, calculate the frequency index corresponding to the maximum amplitude:

[0050] k max [m] = argmax k |X[m,k]|;

[0051] Step S3.5.2: Convert the frequency index to the actual frequency:

[0052] f[m] = k max [m] * (F s / N);

[0053] where F s is the sampling frequency;

[0054] Step S3.5.3: Based on the actual frequency, obtain the frequency time-varying function:

[0055] f(t) = f[m];

[0056] where t = m * Δt.

[0057] An acoustic emission detection system based on optical stress waves realizes spectral feature analysis through the above method, including a piezoelectric energy harvesting layer, an electroluminescent layer, a microporous array layer, a photochromic layer, an excitation optical fiber, a receiving optical fiber, and a calculation unit;

[0058] The photochromic layer, the microporous array layer, the electroluminescent layer, and the piezoelectric energy harvesting layer are sequentially stacked;

[0059] One side of the piezoelectric energy harvesting layer away from the electroluminescent layer is in contact with the object to be measured, and is used to convert the stress wave into a voltage signal;

[0060] The electroluminescent layer is used to convert the voltage signal into an optical signal;

[0061] Array holes are provided on the microporous array layer, and are used to convert the optical signal into an optical ray array;

[0062] The photochromic layer is used to form a three-dimensional blocking light spot under the irradiation of the light ray array;

[0063] The excitation optical fiber and the receiving optical fiber are respectively located at both ends of the photochromic layer. The excitation optical fiber is used to emit multi-color light to the photochromic layer, and the receiving optical fiber is used to obtain the multi-color light passing through the photochromic layer;

[0064] The calculation unit is respectively connected to the excitation optical fiber and the receiving optical fiber, and is used to control the emission of the excitation optical fiber, obtain the multi-color light received by the receiving optical fiber, and perform spectral feature analysis.

[0065] Furthermore, array holes are provided on the microporous array layer. The array holes are a multi-row staggered rectangular structure. The holes in each row are arranged at equal intervals, and adjacent rows are staggered horizontally, and the distance between adjacent rows is equal.

[0066] Furthermore, the thickness of the photochromic layer is 1-10 μm; the thickness of the microporous array layer is 10-100 μm, and the hole diameter is 1-100 μm; the emission wavelength of the electroluminescent layer is 200-700 μm; the thickness of the piezoelectric energy harvesting layer is 50-100 μm.

[0067] Beneficial effects: The present invention discloses an acoustic emission detection method and a detection system based on an optical stress wave. The detection system includes a piezoelectric energy harvesting layer, an electroluminescent layer, a microporous array layer, a photochromic layer, an excitation optical fiber, a receiving optical fiber, and a calculation unit that are stacked. The piezoelectric energy harvesting layer converts the instantaneous stress of the material to be measured into an instantaneous voltage, and the instantaneous voltage excites the electroluminescent layer to emit a light signal. The light signal is converted into a light ray array through the array holes on the microporous array layer. The photochromic layer forms a three-dimensional blocking light spot under the irradiation of the light ray array. The excitation optical fiber emits multi-color light to the photochromic layer, and the light passes through the three-dimensional blocking light spot and is received by the receiving optical fiber, and is converted into a spectral measurement signal in the calculation unit to complete spectral feature analysis. The present invention uses the cascade effect of the stacked structure to capture tiny stress waves, forms a changing blocking light spot signal on the photochromic layer, and thus uses the principle of absorption, diffraction, and diffraction of different frequency light waves by the three-dimensional blocking light spot to convert the rapidly changing light spot feature change signal into a spectral measurement signal, complete spectral feature analysis, and achieve stress wave detection with a wider frequency band and higher sensitivity. Description of the Drawings

[0068] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0069] Figure 1 It is a schematic flowchart of the detection method of the present invention;

[0070] Figure 2 It is a schematic diagram of the overall structure of the detection system of the present invention;

[0071] Figure 3 It is a schematic diagram of the array hole structure of the present invention;

[0072] Figure 4 It is a schematic diagram of a three-dimensional blocking light spot morphology of the photochromic layer of the present invention;

[0073] Figure 5 It is another schematic diagram of a three-dimensional blocking light spot morphology of the photochromic layer of the present invention;

[0074] Figure 6 It is another schematic diagram of a three-dimensional blocking light spot morphology of the photochromic layer of the present invention.

[0075] Explanation of reference numerals:

[0076] 1. Piezoelectric energy harvesting layer; 2. Electroluminescent layer; 3. Microporous array layer; 31. Array holes; 4. Photochromic layer; 5. Excitation optical fiber; 6. Receiving optical fiber; 7. Computing unit; 8. Object under test. Detailed implementation manners

[0077] To make the above objects, features, and advantages of the present application more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present application with reference to the drawings. Many specific details are set forth in the following description in order to fully understand the present application. However, the present application can be implemented in many other ways different from those described herein. Those skilled in the art can make similar improvements without departing from the connotation of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0078] In the description of the present application, it should be understood that terms such as "installed", "connected", "joined", "fixed", etc. should be construed in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements or the interaction relationship between two elements, unless otherwise clearly defined. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0079] In the present application, unless otherwise clearly specified and limited, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "below" and "beneath" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0080] Example 1:

[0081] Referring to Figure 1 As shown, this embodiment discloses an acoustic emission detection method based on optical stress waves, including the following steps:

[0082] Step S1: Stack the piezoelectric energy harvesting layer 1, the electroluminescent layer 2, the microporous array layer 3 and the photochromic layer 4 in sequence, and attach the piezoelectric energy harvesting layer 1 to the object to be measured.

[0083] Step S2: Inject polychromatic light into one end of the photochromic layer 4 through the excitation optical fiber 5, and receive the polychromatic light at the other end of the photochromic layer 4 through the receiving optical fiber 6.

[0084] Step S3: The calculation unit 7 acquires the optical signal of the receiving optical fiber 6 to obtain a spectral measurement signal, and performs spectral feature analysis on the spectral measurement signal.

[0085] Step S3.1: Acquire the spectrum of the spectral measurement signal, fit the spectral shift by the least squares method, and calculate the fitting parameters and the fitting spectrum.

[0086] Step S3.1.1: Define the model function:

[0087] S(λ) = w1 * I(λ) + w2 * I(θ(λ)) + w3 * T(λ);

[0088] where λ is the wavelength, I(λ) is the incident spectrum, θ(λ) is the phase function, and T(λ) is the transmission function; w1, w2, and w3 are fitting parameters;

[0089] Step S3.1.2: Initialize the fitting parameters w1, w2, and w3;

[0090] Step S3.1.3: Calculate the residual:

[0091] R = Σ(S measured (λ) - S(λ))^2;

[0092] where, S measured (λ) represents the spectral measurement signal;

[0093] Step S3.1.4: Use the gradient descent method to minimize the residual:

[0094] ;

[0095] where, η represents the learning rate, wi(new) represents the i-th fitting parameter in the current iteration, and wi(old) represents the updated i-th fitting parameter;

[0096] Step S3.1.5: Repeat steps S3.1.3 to S3.1.4 until convergence to obtain the fitting parameters w1, w2, w3 and the fitted spectrum S fitted (λ).

[0097] Specifically, in this embodiment, the incident spectrum is expressed as:

[0098] I(λ) = A * exp(-(λ - λ0)² / (2σ²));

[0099] where, A represents the amplitude parameter, reflecting the intensity of the incident light, obtained by the experimental measurement method; λ0 represents the central wavelength, affecting the spectral peak position, calibrated by the instrument; σ represents the spectral width, determining the spectral broadening degree, measured with the standard spectrum;

[0100] The spectral form of the Gaussian distribution function conforms to the spectral distribution characteristics of natural light sources, follows the central limit theorem, and represents a continuous and smooth spectral energy distribution; it is continuously differentiable in the mathematical analysis process, has good symmetry, remains a Gaussian function after Fourier transform, and has excellent integral properties, facilitating energy calculation.

[0101] The phase function is expressed as:

[0102] θ(λ) = 2πnd / λ + φ0;

[0103] Among them, 2πnd / λ is the phase integral of the optical path; n represents the refractive index, which depends on the characteristics of the material, affects the optical path difference, and is measured and obtained by the ellipsometry method or the prism coupling method; d represents the propagation distance, which affects the phase accumulation and is calibrated by the white light interference method; φ0 represents the initial phase, compensates for the initial state of the system, and is calibrated by the interference fringe analysis or the phase stepping method. The Helmholtz wave equation form of the phase function follows Snell's law.

[0104] The transmission function is expressed as:

[0105] T(λ) = exp(-α(λ)d);

[0106] α(λ) represents the absorption coefficient, which affects the spectral shape and is calibrated by methods such as the spectrophotometer method or the photothermal deflection spectroscopy method; d represents the effective thickness, which affects the overall transmittance and is measured by a film thickness gauge or calibrated by an optical profiler. The Beer-Lambert form describes the relationship between the absorption strength of a substance at a wavelength, the concentration of the absorbing substance, and the thickness.

[0107] Step S3.2: Process the fitting spectrum through a window function to calculate a preprocessed spectrum;

[0108] Step S3.2.1: Normalize the fitting spectrum;

[0109] Step S3.2.2: Select a window function w(λ);

[0110] Step S3.2.3: Apply the window function to each time point of the fitting spectrum to obtain a preprocessed spectrum:

[0111] S windowed (λ) = S fitted (λ) * w(λ).

[0112] Step S3.3: Extract the energy and frequency information of the preprocessed spectrum through short-time Fourier transform to calculate a time-frequency representation function;

[0113] Step S3.3.1: Discretize the continuous preprocessed spectrum S windowed (λ) into a discrete spectrum x[n]:

[0114] x[n] = S fitted (λn);

[0115] Among them, λn is the discrete wavelength point;

[0116] Step S3.3.2: Select a window function w[n];

[0117] Step S3.3.3: Perform a short-time Fourier transform on the discrete spectrum to obtain a time-frequency representation function:

[0118] X[m, k] = ∑[x[n]w[n - m]e^(-j2πkn / N)];

[0119] Where m is the time index, k is the frequency index, and N is the length of the fast Fourier transform.

[0120] Step S3.4: Extract the energy distribution of the time - frequency representation function at different time points, and calculate the energy time - varying function;

[0121] Step S3.4.1: Calculate the short - time Fourier transform amplitude spectrum of the time - frequency representation function to obtain the short - time Fourier transform amplitude spectrum:

[0122] |X[m, k]| = sqrt(Re{X[m, k]}^2 + Im{X[m, k]}^2);

[0123] Step S3.4.2: Calculate the spectral energy for each time point of the short - time Fourier transform amplitude spectrum:

[0124] E[m] = ∑(|X[m, k]|^2);

[0125] Step S3.4.3: Calculate the change of spectral energy with time based on the spectral energy to obtain the energy time - varying function:

[0126] E(t) = E[m];

[0127] Where t = m * Δt, and Δt represents the time resolution.

[0128] Step S3.5: Extract the frequency distribution of the time - frequency representation function at different time points, and calculate the frequency time - varying function.

[0129] Step S3.5.1: For each time point, calculate the frequency index corresponding to the maximum amplitude:

[0130] k max [m] = argmax k |X[m, k]|;

[0131] Step S3.5.2: Convert the frequency index to the actual frequency:

[0132] f[m] = k max [m] * (F s / N);

[0133] Where F s is the sampling frequency;

[0134] Step S3.5.3: Based on the actual frequency, obtain the frequency time - varying function:

[0135] f(t) = f[m];

[0136] where t = m * Δt.

[0137] Embodiment 2:

[0138] This embodiment discloses an acoustic emission detection method based on optical stress waves. The difference between this embodiment and Embodiment 1 lies in:

[0139] Step S2: Inject multi-color light into one end of the photochromic layer 4 by exciting the optical fiber 5, and receive the multi-color light at the other end of the photochromic layer 4 through the receiving optical fiber 6; in this embodiment, the multi-color light is received in multiple regions at the other end of the photochromic layer 4 through the receiving optical fiber 6;

[0140] Step S3: The calculation unit 7 acquires the optical signal of the receiving optical fiber 6 to obtain a spectral measurement signal, and performs spectral feature analysis on the spectral measurement signal. In this embodiment, the calculation unit 7 acquires the optical signals of multiple receiving optical fibers 6;

[0141] This embodiment further includes Step S3.0: Perform spectral gradient analysis;

[0142] Specifically, Step S3.0 includes:

[0143] Step S3.0.1: Calculate the local spectral gradient:

[0144] G(λ) = |S(λ,x+Δx) - S(λ,x)| / Δx;

[0145] where: S(λ,x) represents the spectral response at position x; Δx represents the spatial sampling interval; λ∈[400nm,1000nm] represents the wavelength range.

[0146] Step S3.0.2: Determine the spectral feature interval:

[0147] Divide the spectrum into three feature intervals according to the gradient value:

[0148] I1(λ): |G(λ)| < G1, corresponding to the weak change interval;

[0149] I2(λ): G1 ≤ |G(λ)| < G2, corresponding to the medium change interval;

[0150] I3(λ): |G(λ)| ≥ G2, corresponding to the strong change interval;

[0151] where G1 and G2 are gradient thresholds;

[0152] Step S3.0.3: Calculate the interval spectrum:

[0153] S1(λ) = 1 / N1 * ∑S(λ,x), where x ∈ interval I1;

[0154] S2(λ) = 1 / N2 * ∑S(λ,x), where x ∈ interval I2;

[0155] S3(λ) = 1 / N3 * ∑S(λ,x), where x ∈ interval I3;

[0156] where N1, N2, and N3 are the number of sampling points in each interval

[0157] Step S3.0.4: Generate the comprehensive spectrum:

[0158] S m (λ) = α1S1(λ) + α2S2(λ) + α3S3(λ)

[0159] where α1, α2, and α3 represent the weight coefficients, reflecting the contributions of each interval to the final spectrum, and satisfying α1 + α2 + α3 = 1 in this embodiment.

[0160] Step S3.1: Obtain the spectrum of the spectral measurement signal, fit the spectral offset by the least squares method, and calculate the fitting parameters and the fitted spectrum;

[0161] Step S3.1.1: Define the model function:

[0162] S(λ) = w1 * I(λ) + w2 * I(θ(λ)) + w3 * T(λ);

[0163] In this embodiment, S(λ)=S m (λ), so the spectrum of the spectral measurement signal obtained is the spectrum with modified gradient to treat the distributions in different regions, which helps to analyze the response of the system to weak energy in specific regions.

[0164] Example Three:

[0165] Referring to Figure 2 as shown, this embodiment discloses an acoustic emission detection system based on optical stress waves, which realizes spectral feature analysis through the method described in Example One, including a piezoelectric energy harvesting layer 1, an electroluminescent layer 2, a microporous array layer 3, a photochromic layer 4, an excitation optical fiber 5, a receiving optical fiber 6, and a calculation unit 7;

[0166] The photochromic layer 4, the microporous array layer 3, the electroluminescent layer 2, and the piezoelectric energy harvesting layer 1 are sequentially stacked;

[0167] One side of the piezoelectric energy harvesting layer 1 away from the electroluminescent layer 2 is in contact with the object to be measured 8, and is used to convert stress waves into voltage signals;

[0168] The electroluminescent layer 2 is used to convert the voltage signal into an optical signal;

[0169] Array holes 31 are provided on the microporous array layer 3, and are used to convert the optical signal into an optical ray array;

[0170] The photochromic layer 4 is used to form a three-dimensional blocking light spot under the irradiation of the optical ray array;

[0171] Specifically, referring to Figure 4 As shown, when the optical ray is weak, a hemispherical three-dimensional blocking light spot is formed at the corresponding position of the photochromic layer 4 under irradiation; in this embodiment, the radius of the three-dimensional blocking light spot is 1-10 μm, and the height is 0.1-1 μm; the absorption rate for short-wave multi-color light (wavelength 400-500 nm) >80%, and the diffraction angle is small; for medium-wave multi-color light (wavelength 500-700 nm), the diffraction effect is significant, and an annular pattern is generated; for long-wave multi-color light (wavelength 700-1000 nm), the influence is mainly diffraction, and the scattering angle is large;

[0172] Referring to Figure 5 As shown, when the intensity of the optical ray is medium, a mound-shaped three-dimensional blocking light spot is formed at the corresponding position of the photochromic layer 4 under irradiation; in this embodiment, the base diameter of the three-dimensional blocking light spot is 5-20 μm, and the height is 1-5 μm; the absorption rate for short-wave multi-color light (wavelength 400-500 nm) >90%, and the diffraction is enhanced; for medium-wave multi-color light (wavelength 500-700 nm), multi-level diffraction is formed, and a complex interference pattern is generated; for long-wave multi-color light (wavelength 700-1000 nm), the influence is enhanced diffraction, and the scattering mode is complex;

[0173] Referring to Figure 6 As shown, when the optical ray is strong, a cylindrical three-dimensional blocking light spot is formed at the corresponding position of the photochromic layer 4 under irradiation; in this embodiment, the diameter of the three-dimensional blocking light spot is 10-30 μm, and the height is 5-10 μm; the short-wave multi-color light (wavelength 400-500 nm) is completely absorbed, and the diffraction disappears; for medium-wave multi-color light (wavelength 500-700 nm), strong diffraction is formed, and the characteristic spectrum is obvious; for long-wave multi-color light (wavelength 700-1000 nm), complex diffraction is generated, and the scattering intensity is the largest.

[0174] The excitation optical fiber 5 and the receiving optical fiber 6 are respectively located at both ends of the photochromic layer 4. The excitation optical fiber 5 is used to emit multi-color light to the photochromic layer 4, and the receiving optical fiber 6 is used to obtain the multi-color light passing through the photochromic layer 4; in this embodiment, the multi-color light emitted by the excitation optical fiber 5 is parallel to the plane of the photochromic layer 4.

[0175] The calculation unit 7 is respectively connected to the excitation optical fiber 5 and the receiving optical fiber 6, and is used to control the emission of the excitation optical fiber 5, obtain the multi-color light received by the receiving optical fiber 6, and perform spectral feature analysis.

[0176] Refer to Figure 3 As shown, specifically, array holes 31 are provided on the microporous array layer 3. The array holes 31 are a multi-row staggered rectangular structure. The holes in each row are arranged at equal intervals, and adjacent rows are staggered horizontally, and the distance between adjacent rows is equal. As a preference of this embodiment, the row spacing of the arrangement of the array holes 31 is 10 - 100 μm, the hole diameter is 1 - 100 μm; the dislocation amount between adjacent rows is 0.5 - 1 times the hole diameter; it can prevent crosstalk, ensure the separation of diffraction patterns, and guarantee the sampling density.

[0177] In some other embodiments of this embodiment, the row spacing of the arrangement of the array holes 31 is 5 - 50 μm, the hole diameter is 0.5 - 10 μm; the dislocation amount between adjacent rows is 0.3 - 0.8 times the hole diameter; thereby achieving high resolution.

[0178] In some other embodiments of this embodiment, the row spacing of the arrangement of the array holes 31 is 100 - 1000 μm, the hole diameter is 50 - 500 μm; the dislocation amount between adjacent rows is 0.7 - 1.2 times the hole diameter; thereby achieving a larger dynamic range.

[0179] The thickness of the photochromic layer 4 is 1 - 10 μm; the thickness of the microporous array layer 3 is 10 - 100 μm, the emission wavelength of the electroluminescent layer 2 is 200 - 700 μm; the thickness of the piezoelectric energy harvesting layer 1 is 50 - 100 μm.

[0180] As a further improvement of this embodiment, a plurality of receiving optical fibers 6 are provided and distributed at one end of the photochromic layer 4, so that spectral feature analysis can be realized by the method described in Embodiment 2.

[0181] An acoustic emission detection method and detection system based on optical stress waves disclosed in this embodiment convert the instantaneous stress of a material to be measured into an instantaneous voltage through a piezoelectric energy harvesting layer 1. The instantaneous voltage excites an electroluminescent layer 2 to emit an optical signal. The optical signal is converted into an optical ray array through the array holes 31 on a microporous array layer 3. A photochromic layer 4 forms a three-dimensional blocking light spot under the irradiation of the optical ray array. An optical fiber 5 is excited to emit multi-color light rays towards the photochromic layer 4. The light rays pass through the three-dimensional blocking light spot and are received by a receiving optical fiber 6, and are converted into a spectral measurement signal in a calculation unit 7 to complete the spectral feature analysis. The present invention utilizes the cascade effect of a stacked structure to capture tiny stress waves, forms a changing blocking light spot signal on the photochromic layer 4, and thus, based on the principles of absorption, diffraction, and diffraction of different frequency light waves by the three-dimensional blocking light spot, converts the rapidly changing spot feature change signal into a spectral measurement signal to complete the spectral feature analysis, achieving stress wave detection with a wider frequency band and higher sensitivity.

[0182] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0183] The above-described embodiments only represent several implementation manners of this application, and the description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of the patent of this application shall be subject to the appended claims.

Claims

1. An acoustic emission detection method based on optical stress waves, characterized in that, It includes the following steps: Step S1: Stack a piezoelectric energy harvesting layer, an electroluminescent layer, a microporous array layer, and a photochromic layer in sequence, and attach the piezoelectric energy harvesting layer to the object to be measured; Step S2: Inject multicolor light into one end of the photochromic layer through an excitation optical fiber, and receive the multicolor light at the other end of the photochromic layer through a receiving optical fiber; Step S3: A calculation unit obtains the optical signal of the receiving optical fiber to obtain a spectral measurement signal, and performs spectral feature analysis on the spectral measurement signal; The said Step S3 includes the following steps: Step S3.1: Obtain the spectrum of the spectral measurement signal, fit the spectrum offset by the least squares method, and calculate the fitting parameters and the fitted spectrum; Step S3.2: Process the fitted spectrum through a window function to calculate a preprocessed spectrum; Step S3.3: Extract the energy and frequency information of the preprocessed spectrum through short-time Fourier transform to calculate a time-frequency representation function; Step S3.4: Extract the energy distribution of the time-frequency representation function at different time points to calculate an energy time-varying function; Step S3.5: Extract the frequency distribution of the time-frequency representation function at different time points to calculate a frequency time-varying function; The said Step S3.1 includes the following method steps: Step S3.1.1: Define a model function: ； where λ is the wavelength, I(λ) is the incident spectrum, θ(λ) is the phase function, T(λ) is the transmission function; w1, w2, w3 are fitting parameters; Step S3.1.2: Initialize the fitting parameters w1, w2, w3; Step S3.1.3: Calculate the residual: R = Σ(S measured (λ) - S(λ))^2; Among them, S measured (λ) represents the spectral measurement signal; Step S3.1.4: Use the gradient descent method to minimize the residual: ； where η represents the learning rate, wi(new) represents the i-th fitting parameter of the current iteration, and wi(old) represents the updated i-th fitting parameter; Step S3.1.5: Repeat Step S3.1.3 to Step S3.1.4 until convergence to obtain fitting parameters w1, w2, w3 and a fitting spectrum S fitted (λ); The said Step S3.2 includes the following method steps: Step S3.2.1: Normalize the fitted spectrum; Step S3.2.2: Select a window function w(λ); Step S3.2.3: Apply the window function to each time point of the fitted spectrum to obtain a preprocessed spectrum: ； The said Step S3.3 includes the following method steps: Step S3.3.1: Discretize the continuous preprocessed spectrum S windowed (λ) into a discrete spectrum x[n]: x[n] = S windowed (λn); where λn are discrete wavelength points; Step S3.3.2: Select a window function w[n]; Step S3.3.3: Perform short-time Fourier transform on the discrete spectrum to obtain a time-frequency representation function: X[m,k] = ∑[x[n]w[n-m]e^(-j2πkn / N)]; where m is the time index, k is the frequency index, and N is the length of the fast Fourier transform; The said Step S3.4 includes the following method steps: Step S3.4.1: Calculate the short-time Fourier transform amplitude spectrum of the time-frequency representation function to obtain the short-time Fourier transform amplitude spectrum: |X[m,k]| = sqrt(Re{X[m,k]}^2 + Im{X[m,k]}^2); Step S3.4.2: Calculate the spectral energy at each time point of the short-time Fourier transform amplitude spectrum: E[m] = ∑(|X[m,k]|^2); Step S3.4.3: Calculate the change of spectral energy over time based on the spectral energy to obtain the energy time-varying function: E(t) = E[m]; Among them, , Δt represents the time resolution; The said Step S3.5 includes the following method steps: Step S3.5.1: For each time point, calculate the frequency index corresponding to the maximum amplitude: k max [m] = argmax k |X[m,k]|; Step S3.5.1: Convert the frequency index to the actual frequency: ； where F s is the sampling frequency; Step S3.5.1: Based on the actual frequency, obtain the frequency time-varying function: f(t) = f[m]; Among them 。 2. An acoustic emission detection system based on optical stress waves, characterized in that, Implement spectral feature analysis through the method described in Claim 1, including a piezoelectric energy harvesting layer, an electroluminescent layer, a microporous array layer, a photochromic layer, an excitation optical fiber, a receiving optical fiber, and a calculation unit; The photochromic layer, the microporous array layer, the electroluminescent layer, and the piezoelectric energy harvesting layer are sequentially stacked; One side of the piezoelectric energy harvesting layer away from the electroluminescent layer is in contact with the object to be measured, and is used to convert stress waves into voltage signals; The electroluminescent layer is used to convert the said voltage signal into an optical signal; Array holes are provided on the microporous array layer, and are used to convert the optical signal into an optical ray array; The photochromic layer is used to form a three-dimensional blocking light spot under the irradiation of the optical ray array; The excitation optical fiber and the receiving optical fiber are respectively located at both ends of the photochromic layer. The excitation optical fiber is used to emit multi-color light rays to the photochromic layer, and the receiving optical fiber is used to obtain the multi-color light rays passing through the photochromic layer; The calculation unit is respectively connected to the excitation optical fiber and the receiving optical fiber, and is used to control the emission of the excitation optical fiber and obtain the multi-color light rays received by the receiving optical fiber, and implement spectral feature analysis; Array holes are provided on the microporous array layer. The array holes are a multi-row staggered rectangular structure. The holes in each row are arranged at equal intervals, and the adjacent rows are relatively staggered in the horizontal position, and the distance between adjacent rows is equal.

3. The acoustic emission detection system based on optical stress wave according to claim 2, wherein The thickness of the photochromic layer is 1 - 10 μm; the thickness of the microporous array layer is 10 - 100 μm, and the hole diameter is 1 - 100 μm; the emission wavelength of the electroluminescent layer is 200 - 700 μm; the thickness of the piezoelectric energy harvesting layer is 50 - 100 μm.

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