Acoustic emission detection method and system based on optical stress waves

Abstract

The invention belongs to the technical field of acoustic emission detection sensors, and particularly relates to an acoustic emission detection method and system based on optical stress waves. Instantaneous stress of a to-be-detected material is converted into instantaneous voltage through a piezoelectric energy collection layer, and an electroluminescent layer is excited to emit an optical signal; an optical signal is converted through array holes in the micropore array layer to form a light ray array, the photochromic layer forms a three-dimensional light blocking spot, the excitation optical fiber emits multicolor light rays to the photochromic layer, and the multicolor light rays penetrate through the three-dimensional light blocking spot to be received by the receiving optical fiber and converted into a spectrum measurement signal to complete spectrum characteristic analysis. According to the invention, the cascade effect of the laminated structure is utilized to capture tiny stress waves, and variable light-blocking spot signals are formed on the photochromic layer, so that the principle that three-dimensional light-blocking spots absorb, diffract and diffract light waves with different frequencies is utilized, the light spot characteristic change signals which change rapidly are converted into spectral measurement signals, and spectrum characteristic analysis is completed; and the stress wave detection with wider frequency band and higher sensitivity is realized.

Description

Technical Field

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

[0002] Acoustic emission testing is a non-destructive testing (NDE) method that can be used to examine the internal condition of a material or structure, using specialized sensors and recording equipment to capture stress waves (acoustic emission activity) generated and emitted within the material. Acoustic emission technology is widely used in various engineering fields such as oil and gas, chemical, 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, it releases local high-frequency elastic waves, which propagate to the surface of the material in the form of sound waves. After the sound waves on the surface of the material 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 vibrations into electrical signals. The preamplifier performs preliminary amplification on the electrical signals, and finally transmits them to the signal conditioning and processing system for signal processing and analysis. This traditional acoustic emission sensor is limited by its frequency response range and sensitivity, making it difficult to capture subtle changes in microscopic scales and ultrafast processes, making it difficult to achieve high temporal resolution and high spatial resolution at the same time. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of low frequency response range and low sensitivity of acoustic emission sensors of traditional structures in the prior art, thereby providing an acoustic emission detection method and detection system based on optical stress waves.

[0005] An acoustic emission detection method based on optical stress waves comprises the following steps: Step S1: stacking a piezoelectric energy collection layer, an electroluminescent layer, a micropore array layer and a photochromic layer in sequence, and placing the piezoelectric energy collection layer against the object to be measured; Step S2: injecting multicolor light at one end of the photochromic layer through an exciting optical fiber, and receiving the multicolor light at the other end of the photochromic layer through a receiving optical fiber; Step S3: The calculation unit acquires the optical signal of the receiving optical fiber, obtains a spectrum measurement signal, and performs spectrum characteristic analysis on the spectrum measurement signal.

[0006] Furthermore, the step S3 includes the following steps: Step S3.1: Obtain the spectrum of the spectral measurement signal, fit the spectrum offset by the least square method, and calculate the fitting parameters and the fitting spectrum; Step S3.2: Processing the fitting spectrum by means of a window function to obtain a preprocessed spectrum by calculation; Step S3.3: extracting the energy and frequency information of the preprocessed spectrum by short-time Fourier transform, and calculating a time-frequency representation function; Step S3.4: extracting the energy distribution of the time-frequency representation function at different time points, and calculating the energy time-varying function; Step S3.5: extract the frequency distribution of the time-frequency representation function at different time points, and calculate the frequency time-varying function.

[0007] Furthermore, the step S3.1 includes the following method steps: Step S3.1.1: Define the model function: S(λ) = w1 * I(λ) + w2 * I(θ(λ)) + w3 * T(λ); 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 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: Minimize the residual using gradient descent: ; Where η represents the learning rate, wi(new) represents the i-th fitting parameter of the current iteration, and wi(old) represents the i-th fitting parameter after the update; Step S3.1.5: Repeat steps S3.1.3 to S3.1.4 until convergence, and obtain fitting parameters w1, w2, w3 and fitting spectrum S fitted (λ).

[0008] Furthermore, the step S3.2 includes the following method steps: Step S3.2.1: normalizing the fitted spectrum; Step S3.2.2: Select the window function w(λ); Step S3.2.3: Apply the window function to each time point of the fitted spectrum to obtain the preprocessed spectrum: S windowed (λ) = S fitted (λ) * w(λ).

[0009] Furthermore, the step S3.3 includes the following method steps: Step S3.3.1: Convert the continuous pre-processed spectrum S windowed (λ) is discretized into a discrete spectrum x[n]: x[n] = S fitted (λn); Among them, λn is a discrete wavelength point; Step S3.3.2: Select window function w[n]; Step S3.3.3: Perform short-time Fourier transform on the discrete spectrum to obtain the time-frequency representation function: X[m,k] = ∑[x[n]w[nm]e^(-j2πkn / N)]; Among them, m is the time index, k is the frequency index, and N is the fast Fourier transform length.

[0010] Furthermore, the 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 spectrum energy over time based on the spectrum energy to obtain the energy time-varying function: E(t) = E[m]; Here, t = m * Δt, where Δt represents the time resolution.

[0011] Furthermore, the 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: f[m] = k max [m] * (F s / N); Among them, 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]; where t = m * Δt.

[0012] An acoustic emission detection system based on optical stress waves, which realizes spectrum feature analysis by the above method, comprises a piezoelectric energy collection layer, an electroluminescent layer, a micropore array layer, a photochromic layer, an excitation optical fiber, a receiving optical fiber and a calculation unit; The photochromic layer, the micropore array layer, the electroluminescent layer, and the piezoelectric energy collection layer are stacked in sequence; The piezoelectric energy collection layer has a side away from the electroluminescent layer in contact with the object to be measured, and is used to convert the stress wave into a voltage signal; The electroluminescent layer is used to convert the voltage signal into a light signal; The micro-hole array layer is provided with array holes for converting the optical signal into an optical ray array; The photochromic layer is used to form a stereoscopic light blocking spot under the irradiation of the light ray array; The excitation optical fiber and the receiving optical fiber are respectively located at two ends of the photochromic layer, the excitation optical fiber is used to emit polychromatic light to the photochromic layer, and the receiving optical fiber is used to obtain the polychromatic light passing through the photochromic layer; The calculation unit is connected to the excitation optical fiber and the receiving optical fiber respectively, and is used to control the emission of the excitation optical fiber and obtain the polychromatic light received by the receiving optical fiber, and realize spectrum feature analysis.

[0013] Furthermore, array holes are arranged on the microporous array layer, and the array holes are in a multi-row staggered rectangular structure, the holes in each row are arranged at equal intervals, adjacent rows are relatively staggered in horizontal positions, and the intervals between adjacent rows are equal.

[0014] Furthermore, the thickness of the photochromic layer is 1-10 μm; the thickness of the micropore 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; and the thickness of the piezoelectric energy collection layer is 50-100 μm.

[0015] Beneficial effects: The present invention discloses an acoustic emission detection method and detection system based on optical stress waves. The detection system includes a stacked piezoelectric energy collection layer, an electroluminescent layer, a microporous array layer, a photochromic layer, an excitation optical fiber, a receiving optical fiber and a computing unit. The instantaneous stress of the material to be tested is converted into an instantaneous voltage through the piezoelectric energy collection layer. The instantaneous voltage excites the electroluminescent layer to emit a light signal. The light signal is converted through the array holes on the microporous array layer to form a light ray array. The photochromic layer forms a stereoscopic light blocking spot under the irradiation of the light ray array. The excitation optical fiber emits multi-color light to the photochromic layer. The light passes through the stereoscopic light blocking spot and is received by the receiving optical fiber, and is converted into a spectrum measurement signal in the computing unit to complete the spectrum feature analysis. The present invention utilizes the cascade effect of the stacked structure to capture tiny stress waves and forms a changing light-blocking spot signal on the photochromic layer. The invention then utilizes the principle of absorption, diffraction and scattering of light waves of different frequencies by the three-dimensional light-blocking spot to convert the rapidly changing light spot characteristic change signal into a spectral measurement signal, completes the spectrum characteristic analysis, and realizes stress wave detection with a wider bandwidth and higher sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0017] Figure 1 It is a schematic flow chart of the detection method of the present invention; Figure 2 It is a schematic diagram of the overall structure of the detection system of the present invention; Figure 3 It is a schematic diagram of the array hole structure of the present invention; Figure 4 A schematic diagram of a three-dimensional light blocking spot morphology of the photochromic layer of the present invention; Figure 5 A schematic diagram of another three-dimensional light blocking spot morphology of the photochromic layer of the present invention; Figure 6 FIG. 4 is a schematic diagram of another three-dimensional light blocking spot morphology of the photochromic layer of the present invention.

[0018] Description of reference numerals: 1. Piezoelectric energy collection layer; 2. Electroluminescent layer; 3. Microhole array layer; 31. Array holes; 4. Photochromic layer; 5. Excitation optical fiber; 6. Receiving optical fiber; 7. Computing unit; 8. Object under test. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0020] In the description of this application, it is necessary to understand that the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0021] In the present application, unless otherwise clearly specified and limited, a first feature being “above” or “below” a second feature may mean 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, a first feature being “above”, “above”, and “above” a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being “below”, “below”, and “below” a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0022] Embodiment 1: Reference Figure 1 As shown, this embodiment discloses an acoustic emission detection method based on optical stress waves, comprising the following steps: Step S1: stacking a piezoelectric energy collection layer 1, an electroluminescent layer 2, a micropore array layer 3 and a photochromic layer 4 in sequence, and placing the piezoelectric energy collection layer 1 against the object to be measured; Step S2: injecting multi-color light into one end of the photochromic layer 4 through the exciting optical fiber 5, and receiving the multi-color light at the other end of the photochromic layer 4 through the receiving optical fiber 6; Step S3: the calculation unit 7 acquires the optical signal of the receiving optical fiber 6, obtains a spectrum measurement signal, and performs spectrum characteristic analysis on the spectrum measurement signal.

[0023] Step S3.1: Obtain the spectrum of the spectral measurement signal, fit the spectrum offset by the least square method, and calculate the fitting parameters and the fitting spectrum; Step S3.1.1: Define the model function: S(λ) = w1 * I(λ) + w2 * I(θ(λ)) + w3 * T(λ); 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 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: Minimize the residual using gradient descent: ; Where η represents the learning rate, wi(new) represents the i-th fitting parameter of the current iteration, and wi(old) represents the i-th fitting parameter after the update; Step S3.1.5: Repeat steps S3.1.3 to S3.1.4 until convergence, and obtain fitting parameters w1, w2, w3 and fitting spectrum S fitted (λ).

[0024] Specifically, in this embodiment, the incident spectrum is expressed as: I(λ) = A * exp(-(λ-λ0)² / (2σ²)); Among them, A represents the amplitude parameter, which reflects the intensity of the incident light and is obtained by experimental measurement method; λ0 represents the central wavelength, which affects the peak position of the spectrum and is calibrated by the instrument; σ represents the spectrum width, which determines the degree of spectrum broadening and is measured using a standard spectrum; 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 process of mathematical analysis, has good symmetry, remains a Gaussian function after Fourier transform, and has excellent integral properties, which facilitates energy calculation.

[0025] The phase function is expressed as: θ(λ) = 2πnd / λ + φ0; 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 and affects the optical path difference. It is measured and obtained by the ellipsometer method or prism coupling method; d represents the propagation distance, which affects the phase accumulation and is calibrated by white light interferometry; φ0 represents the initial phase, which compensates for the initial state of the system and is calibrated by interference fringe analysis or phase stepping method. The Helmholtz wave equation form of the phase function follows Snell's law.

[0026] The transmission function is expressed as: T(λ) = exp(-α(λ)d); α(λ) represents the absorption coefficient, which affects the shape of the spectrum and is calibrated by spectrophotometer or photothermal deflection spectroscopy; d represents the effective thickness, which affects the overall transmittance and is measured by a film thickness meter or calibrated by an optical profiler. The Beer-Lambert form describes the relationship between the strength of a substance's absorption of wavelength, the concentration of the absorber, and the thickness.

[0027] Step S3.2: Processing the fitting spectrum by means of a window function to obtain a preprocessed spectrum by calculation; Step S3.2.1: normalizing the fitted spectrum; Step S3.2.2: Select the window function w(λ); Step S3.2.3: Apply the window function to each time point of the fitted spectrum to obtain the preprocessed spectrum: S windowed (λ) = S fitted (λ) * w(λ).

[0028] Step S3.3: extracting the energy and frequency information of the preprocessed spectrum by short-time Fourier transform, and calculating a time-frequency representation function; Step S3.3.1: Convert the continuous pre-processed spectrum S windowed (λ) is discretized into a discrete spectrum x[n]: x[n] = S fitted (λn); Among them, λn is a discrete wavelength point; Step S3.3.2: Select window function w[n]; Step S3.3.3: Perform short-time Fourier transform on the discrete spectrum to obtain the time-frequency representation function: X[m,k] = ∑[x[n]w[nm]e^(-j2πkn / N)]; Among them, m is the time index, k is the frequency index, and N is the fast Fourier transform length.

[0029] Step S3.4: extracting the energy distribution of the time-frequency representation function at different time points, and calculating the energy time-varying function; 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 spectrum energy over time based on the spectrum energy to obtain the energy time-varying function: E(t) = E[m]; Here, t = m * Δt, where Δt represents the time resolution.

[0030] Step S3.5: extract the frequency distribution of the time-frequency representation function at different time points, and calculate the frequency time-varying function.

[0031] 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: f[m] = k max [m] * (F s / N); Among them, 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]; where t = m * Δt.

[0032] Embodiment 2: This embodiment discloses an acoustic emission detection method based on optical stress waves. The difference between this embodiment and the first embodiment is that: Step S2: injecting multicolor light into one end of the photochromic layer 4 through the exciting optical fiber 5, and receiving the multicolor light at the other end of the photochromic layer 4 through the receiving optical fiber 6; in this embodiment, the multicolor light is received at multiple areas at the other end of the photochromic layer 4 through the receiving optical fiber 6; Step S3: The calculation unit 7 obtains the optical signal of the receiving optical fiber 6, obtains the spectrum measurement signal, and performs spectrum characteristic analysis on the spectrum measurement signal. In this embodiment, the calculation unit 7 obtains the optical signals of multiple receiving optical fibers 6; This embodiment further includes step S3.0: performing spectral gradient analysis; Specifically, the step S3.0 includes: Step S3.0.1: calculating the local spectral gradient: G(λ) = |S(λ,x+Δx) - S(λ,x)| / Δx; where: S(λ,x) represents the spectral response at position x; Δx represents the spatial sampling interval; λ∈[400nm,1000nm] represents the wavelength range.

[0033] Step S3.0.2: determining the spectral characteristic intervals: The spectrum is divided into three characteristic intervals according to the gradient value: I1(λ): |G(λ)| < G1, corresponding to the weak change interval; I2(λ): G1 ≤ |G(λ)| < G2, corresponding to the medium change interval; I3(λ): |G(λ)| ≥ G2, corresponding to the strong change interval; where G1 and G2 are gradient thresholds; Step S3.0.3: calculating the interval spectra: S1(λ) = 1 / N1 * ∑S(λ,x), x∈interval I1; S2(λ) = 1 / N2 * ∑S(λ,x), x∈interval I2; S3(λ) = 1 / N3 * ∑S(λ,x), x∈interval I3; where N1, N2, and N3 are the number of sampling points in each interval Step S3.0.4: generating the comprehensive spectrum: S m (λ) = α1S1(λ) + α2S2(λ) + α3S3(λ) where α1, α2, and α3 represent the weight coefficients, reflecting the contribution of each interval to the final spectrum, and in this embodiment, α1 + α2 + α3 = 1.

[0034] Step S3.1: obtaining the spectrum of the spectral measurement signal, fitting the spectral offset by the least squares method, and calculating the fitting parameters and the fitted spectrum; Step S3.1.1: defining the model function: S(λ) = w1 * I(λ) + w2 * I(θ(λ)) + w3 * T(λ); In this embodiment, S(λ)=S m(λ), so that the spectrum of the spectral measurement signal obtained is a spectrum with a corrected gradient to treat the distribution in different regions, which is helpful for analyzing the response of the system to weak energy in a specific region.

[0035] Embodiment three: Reference Figure 2 As shown, this embodiment discloses an acoustic emission detection system based on optical stress waves, and spectrum feature analysis is realized by the method described in the first embodiment, including a piezoelectric energy collection layer 1, an electroluminescent layer 2, a micropore array layer 3, a photochromic layer 4, an excitation optical fiber 5, a receiving optical fiber 6 and a calculation unit 7; The photochromic layer 4, the micropore array layer 3, the electroluminescent layer 2, and the piezoelectric energy collection layer 1 are stacked in sequence; The piezoelectric energy collection layer 1 has a side away from the electroluminescent layer 2 in contact with the object to be measured 8, and is used to convert the stress wave into a voltage signal; The electroluminescent layer 2 is used to convert the voltage signal into a light signal; The micro-hole array layer 3 is provided with array holes 31 for converting the optical signal into an optical ray array; The photochromic layer 4 is used to form a stereoscopic light blocking spot under the irradiation of the light ray array; Specifically, refer to Figure 4 As shown, when the light rays are weak, the corresponding position of the photochromic layer 4 forms a hemispherical stereo light-blocking spot under irradiation; in this embodiment, the radius of the stereo light-blocking spot is 1-10 μm, and the height is 0.1-1 μm; the absorption rate of short-wave polychromatic light (wavelength of 400-500nm) is >80%, and the diffraction angle is small; for medium-wave polychromatic light (wavelength of 500-700nm), the diffraction effect is significant, and a ring pattern is generated; for long-wave polychromatic light (wavelength of 700-1000nm), the effect is mainly diffraction, and the scattering angle is large; Reference Figure 5 As shown, when the intensity of the light ray is medium, the corresponding position of the photochromic layer 4 forms a mound-shaped three-dimensional light-blocking spot under irradiation; in this embodiment, the base diameter of the three-dimensional light-blocking spot is 5-20μm, and the height is 1-5μm; the absorption rate of short-wave polychromatic light (wavelength of 400-500nm) is greater than 90%, and the diffraction is enhanced; for the mid-wave polychromatic light (wavelength of 500-700nm), multi-level diffraction is formed, and a complex interference pattern is generated; for the long-wave polychromatic light (wavelength of 700-1000nm), the effect is that the diffraction is enhanced, and the scattering pattern is complex; Reference Figure 6As shown, when the light rays are strong, the corresponding position of the photochromic layer 4 forms a cylindrical three-dimensional light-blocking spot under irradiation; in this embodiment, the diameter of the three-dimensional light-blocking spot is 10-30μm and the height is 5-10μm; the short-wave polychromatic light (wavelength is 400-500nm) is completely absorbed, and the diffraction disappears; the medium-wave polychromatic light (wavelength is 500-700nm) forms a strong diffraction, and the characteristic spectrum is obvious; the long-wave polychromatic light (wavelength is 700-1000nm) produces complex diffraction, and the scattering intensity is the largest.

[0036] The excitation optical fiber 5 and the receiving optical fiber 6 are respectively located at the two ends of the photochromic layer 4, the excitation optical fiber 5 is used to emit polychromatic light to the photochromic layer 4, and the receiving optical fiber 6 is used to obtain the polychromatic light passing through the photochromic layer 4; in this embodiment, the polychromatic light emitted by the excitation optical fiber 5 is parallel to the plane of the photochromic layer 4.

[0037] The calculation unit 7 is connected to the excitation optical fiber 5 and the receiving optical fiber 6 respectively, and is used to control the emission of the excitation optical fiber 5 and obtain the polychromatic light received by the receiving optical fiber 6, and realize spectrum feature analysis.

[0038] Reference Figure 3 As shown, specifically, the microporous array layer 3 is provided with array holes 31, and the array holes 31 are a plurality of staggered rectangular structures, the holes in each row are arranged at equal intervals, the adjacent rows are relatively staggered in horizontal positions, and the spacing between adjacent rows is equal. As a preferred embodiment of this embodiment, the row spacing of the array holes 31 is 10-100 μm, the hole diameter is 1-100 μm; the displacement between adjacent rows is 0.5-1 times the aperture; it can prevent crosstalk, ensure the separation of diffraction patterns, and ensure sampling density.

[0039] In some other implementations of this embodiment, the row spacing of the array holes 31 is 5-50 μm, the hole diameter is 0.5-10 μm, and the offset between adjacent rows is 0.3-0.8 times the hole diameter, thereby achieving high resolution.

[0040] In some other implementations of this embodiment, the row spacing of the array holes 31 is 100-1000 μm, the hole diameter is 50-500 μm, and the offset between adjacent rows is 0.7-1.2 times the aperture, thereby achieving a larger dynamic range.

[0041] The thickness of the photochromic layer 4 is 1-10 μm; the thickness of the micropore array layer 3 is 10-100 μm; the emission wavelength of the electroluminescent layer 2 is 200-700 μm; and the thickness of the piezoelectric energy collection layer 1 is 50-100 μm.

[0042] 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 spectrum feature analysis can be achieved by the method described in the second embodiment.

[0043] The present embodiment discloses an acoustic emission detection method and detection system based on optical stress waves. The instantaneous stress of the material to be tested is converted into an instantaneous voltage through the piezoelectric energy collection layer 1. The instantaneous voltage excites the electroluminescent layer 2 to emit a light signal. The light signal is converted into a light ray array through the array holes 31 on the microporous array layer 3. The photochromic layer 4 forms a three-dimensional light blocking spot under the irradiation of the light ray array. The exciting optical fiber 5 emits multi-color light to the photochromic layer 4. The light passes through the three-dimensional light blocking spot and is received by the receiving optical fiber 6, and is converted into a spectrum measurement signal in the calculation unit 7 to complete the spectrum feature analysis. The present invention uses the cascade effect of the stacked structure to capture tiny stress waves, and forms a changing light blocking spot signal on the photochromic layer 4, thereby using the principle of absorption, diffraction, and diffraction of light waves of different frequencies by the three-dimensional light blocking spot to convert the extremely fast changing light spot feature change signal into a spectrum measurement signal, complete the spectrum feature analysis, and achieve wider bandwidth and higher sensitivity stress wave detection.

[0044] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0045] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. An acoustic emission detection method based on optical stress waves, characterized in that: The following steps are involved: Step S1: stacking a piezoelectric energy collection layer, an electroluminescent layer, a micropore array layer and a photochromic layer in sequence, and placing the piezoelectric energy collection layer against the object to be measured; Step S2: injecting multicolor light at one end of the photochromic layer through an exciting optical fiber, and receiving the multicolor light at the other end of the photochromic layer through a receiving optical fiber; Step S3: The calculation unit acquires the optical signal of the receiving optical fiber, obtains a spectrum measurement signal, and performs spectrum characteristic analysis on the spectrum measurement signal.

2. The acoustic emission detection method based on optical stress wave according to claim 1, characterized in that: The step S3 comprises the following steps: Step S3.1: Obtain the spectrum of the spectral measurement signal, fit the spectrum offset by the least square method, and calculate the fitting parameters and the fitting spectrum; Step S3.2: Processing the fitting spectrum by means of a window function to obtain a preprocessed spectrum by calculation; Step S3.3: extracting the energy and frequency information of the preprocessed spectrum by short-time Fourier transform, and calculating a time-frequency representation function; Step S3.4: extracting the energy distribution of the time-frequency representation function at different time points, and calculating the energy time-varying function; Step S3.5: extract the frequency distribution of the time-frequency representation function at different time points, and calculate the frequency time-varying function.

3. The acoustic emission detection method based on optical stress wave according to claim 2, characterized in that: The step S3.1 comprises the following method steps: Step S3.1.1: Define the model function: S(λ) = w1 * I(λ) + w2 * I(θ(λ)) + w3 * T(λ); 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 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: Minimize the residual using gradient descent: ； Where η represents the learning rate, wi(new) represents the i-th fitting parameter of the current iteration, and wi(old) represents the i-th fitting parameter after the update; Step S3.1.5: Repeat steps S3.1.3 to S3.1.4 until convergence, and obtain fitting parameters w1, w2, w3 and fitting spectrum S fitted (λ).

4. The acoustic emission detection method based on optical stress wave according to claim 3 is characterized in that: The step S3.2 comprises the following method steps: Step S3.2.1: normalizing the fitted spectrum; Step S3.2.2: Select the window function w(λ); Step S3.2.3: Apply the window function to each time point of the fitted spectrum to obtain the preprocessed spectrum: S windowed (λ) = S fitted (λ) * w(λ)。 5. The acoustic emission detection method based on optical stress wave according to claim 4, characterized in that: The step S3.3 comprises the following method steps: Step S3.3.1: Convert the continuous pre-processed spectrum S windowed (λ) is discretized into a discrete spectrum x[n]: x[n] = S windowed (λn); Among them, λn is a discrete wavelength point; Step S3.3.2: Select window function w[n]; Step S3.3.3: Perform short-time Fourier transform on the discrete spectrum to obtain the time-frequency representation function: X[m,k] = ∑[x[n]w[nm]e^(-j2πkn / N)]; Among them, m is the time index, k is the frequency index, and N is the fast Fourier transform length.

6. The acoustic emission detection method based on optical stress wave according to claim 5, characterized in that: The step S3.4 comprises 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 spectrum energy over time based on the spectrum energy to obtain the energy time-varying function: E(t) = E[m]; Here, t = m * Δt, where Δt represents the time resolution.

7. The acoustic emission detection method based on optical stress wave according to claim 6, characterized in that: The step S3.5 comprises 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: f[m] = k max [m] * (F s / N); Among them, 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]; where t = m * Δt.

8. An acoustic emission detection system based on optical stress waves, characterized in that: The spectrum characteristic analysis is realized by the method described in any one of claims 1 to 7, comprising a piezoelectric energy collection layer, an electroluminescent layer, a microhole array layer, a photochromic layer, an excitation optical fiber, a receiving optical fiber and a calculation unit; The photochromic layer, the micropore array layer, the electroluminescent layer, and the piezoelectric energy collection layer are stacked in sequence; The piezoelectric energy collection layer has a side away from the electroluminescent layer in contact with the object to be measured, and is used to convert the stress wave into a voltage signal; The electroluminescent layer is used to convert the voltage signal into a light signal; The micro-hole array layer is provided with array holes for converting the optical signal into an optical ray array; The photochromic layer is used to form a stereoscopic light blocking spot under the irradiation of the light ray array; The excitation optical fiber and the receiving optical fiber are respectively located at two ends of the photochromic layer, the excitation optical fiber is used to emit polychromatic light to the photochromic layer, and the receiving optical fiber is used to obtain the polychromatic light passing through the photochromic layer; The calculation unit is connected to the excitation optical fiber and the receiving optical fiber respectively, and is used to control the emission of the excitation optical fiber and obtain the polychromatic light received by the receiving optical fiber, and realize spectrum feature analysis.

9. The acoustic emission detection system based on optical stress waves according to claim 8, characterized in that: The microporous array layer is provided with array holes, which are in a rectangular structure with multiple rows staggered. The holes in each row are arranged at equal intervals, adjacent rows are relatively staggered in horizontal positions, and the intervals between adjacent rows are equal.

10. The optical stress wave based acoustic emission detection system according to claim 8, characterized in that: The thickness of the photochromic layer is 1-10 μm; the thickness of the micropore 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 collection layer is 50-100 μm.

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