Classification Method for Electromechanical Impedance Damage Monitoring Based on Environment Matching

Abstract

The present invention discloses a classification method for electromechanical impedance damage monitoring based on environmental matching, comprising: obtaining the conductance signal of a structure; decomposing the conductance signal into real and imaginary part signals and storing them in a baseline data set, and storing the conductance signals after damage at different temperatures in corresponding test data groups; calculating the correlation coefficients of the baseline data set and the test data groups; obtaining the real part signals corresponding to the test data groups; importing the cross-correlation function to calculate the damage diagnosis index of the structure; and finding two sets of baseline data closest to each set of test data in the baseline data set; taking the maximum value of the cross-correlation function of the real part signal of one set of baseline data as the damage diagnosis index, and taking the maximum value of the cross-correlation function of the real part signals of the two sets of baseline data as the judgment threshold for damage, so as to classify the damage of the structure. The present invention can perform non-destructive damage monitoring on composite material structural parts under temperature change conditions, and reduce false alarms caused by temperature changes.

Description

Technical Field

[0001] The present invention belongs to the technical field of non-destructive damage detection, and particularly relates to a classification method for electromechanical impedance damage monitoring based on environmental matching. Background Art

[0002] In the aviation field, composite materials are increasingly widely used due to their advantages such as light weight and high strength. However, the reaction mechanisms of damage are diverse, making it difficult to correctly judge damage. In order to enable aircraft structures to adapt to harsh operating environments and monitor micro-damage in real time, structural health monitoring technology has emerged. Most aircraft structures serve under time-varying conditions, such as environmental temperature changes. Temperature conditions make the characteristics of most damage signals uncertain, reducing the reliability and stability of damage monitoring. Therefore, research on structural guided wave damage monitoring under temperature conditions has become a crucial task.

[0003] Electromechanical impedance technology has been widely used in the field of structural health monitoring because it is sensitive to micro-damage. Its basic idea is that damage existing in a structure will cause a change in mechanical impedance, and by analyzing the admittance signal, it can be determined whether there is damage in the structure and its degree. Electromechanical impedance technology is a new type of local technology based on the electromechanical coupling characteristics of piezoelectric ceramics. It uses the electromechanical impedance information in the structure to judge damage. If there are damages such as cracks in the structure, the mechanical impedance will change, and then the impedance is reflected through the interaction between the piezoelectric ceramics fixed on the structure and the structure. In other words, damage in the structure will cause a change in mechanical impedance. By keeping the parameters and performance of the piezoelectric ceramics constant to uniquely determine the impedance of the structural mechanical impedance, any change in impedance reflects defects, damage, or other physical changes in the structure. Summary of the Invention

[0004] Object of the Invention: In order to overcome the deficiencies in the prior art, the present invention provides a classification method for electromechanical impedance damage monitoring based on environmental matching; it can accurately perform non-destructive damage monitoring on structural components under temperature change conditions, classify the degree of damage, reduce false alarms caused by temperature changes, and is applicable to complex structure damage detection.

[0005] Technical Solution: In a first aspect, the present invention provides a classification method for electromechanical impedance damage monitoring based on environmental matching, including:

[0006] Assemble the structure to be measured, and collect signals from the structure by the voltage-current method to obtain the conductance admittance signal of the structure;

[0007] Decompose the conductance admittance signal into real and imaginary part signals and store them in the baseline data set, and conduct damage experiments on the structure under different temperature environments in sequence, and store the conductance admittance signals after damage under different temperature environments into the corresponding test data groups;

[0008] Test data groups will be selected one by one to match with the baseline data set. The correlation coefficients between the baseline data set and each test data group will be calculated respectively, and the imaginary part signals of the test data group and all the imaginary part signals in the baseline data set will be compared based on the correlation coefficients.

[0009] Select the imaginary part signals in the baseline data set with two correlation coefficients greater than others. Determine the temperature range of the test data group based on the selected imaginary part signals, and obtain the real part signals corresponding to the imaginary part signals of the test data.

[0010] Import the corresponding real part signals into the cross-correlation function to calculate the structural damage diagnosis index; and find the two groups of baseline data closest to each group of test data in the baseline data set.

[0011] In each round of matching, select the maximum value of the cross-correlation function of the real part signals of one group of baseline data as the damage diagnosis index of the same degree, and use the maximum value of the cross-correlation function of the real part signals of the two groups of baseline data as the judgment threshold of the damage of the same degree.

[0012] Obtain the damage diagnosis index and judgment threshold under different degrees of damage based on multiple groups of test data, and classify the damage of the structure.

[0013] In a further embodiment, the method for assembling the structure to be tested is as follows:

[0014] Install two piezoelectric ceramics equidistantly on the structure to be tested, determine one of the piezoelectric ceramics as the exciter and the other as the sensor, so as to form a signal acquisition channel for the conductance signal.

[0015] In a further embodiment, the method for acquiring the conductance signal of the structure by signal acquisition of the structure through the voltage-current method includes:

[0016] According to the voltage-current measurement method, connect a voltage-dividing resistor in series between the exciter and the sensor. The signal generator inputs a sinusoidal excitation signal to the exciter, and the conductance signal is calculated according to the vector Ohm's law by collecting the voltage signal at both ends of the sensor and the resistance value of the voltage-dividing resistor.

[0017] In a further embodiment, the range of the frequency of the excitation signal is 20 kHz - 120 kHz, the amplitude of the sinusoidal excitation signal is 9 V, and the voltage-dividing resistor is 9080 Ω.

[0018] In a further embodiment, the method for decomposing the conductance signal into real and imaginary part signals and storing them in the baseline data set includes:

[0019] When the structure is in a healthy state, the conductance susceptance signal is collected every 5 °C at a preset experimental temperature, continuously collected as a baseline data set, and the conductance susceptance signal is decomposed into a real part signal and an imaginary part signal during the storage process;

[0020] Among them, the real part signal is associated with the structural damage and temperature data, and the imaginary part signal is only associated with the temperature data. Therefore, the real part signal is used for damage monitoring calculation, and the imaginary part signal is used for temperature compensation application.

[0021] In a further embodiment, the calculation formula of the correlation coefficient is:

[0022]

[0023] Among them, Cc represents the correlation between two groups of signals, Z b (k) and Z t (k) represent the imaginary part signals of the admittance of the baseline data set and the test data respectively, and represent the average values of the imaginary part signals of the admittance of the baseline data set and the test data respectively, and the frequency range is between ω I and ω F .

[0024] In a further embodiment, the expression formula of the cross-correlation function is:

[0025]

[0026] Among them, X(u) and Y(u) represent the closest set of baseline data and test data in the frequency domain respectively, ω I represents the starting frequency, ω F represents the final frequency, and Δu represents the frequency shift of the admittance signal.

[0027] In a further embodiment, the judgment threshold is also obtained through formula (2); where X(u) and Y(u) are two sets of baseline data closest to the test data respectively.

[0028] If the damage diagnosis index in the test data is greater than the threshold, it indicates that the structure is healthy; if the damage diagnosis index is less than or equal to the threshold, it indicates that there is damage in the electromechanical impedance structure.

[0029] Beneficial effects: The present invention has the following advantages compared with the prior art:

[0030] 1. The electromechanical impedance technology adopted by the present invention is extremely sensitive to the monitoring of micro damages.

[0031] 2. The electromechanical impedance technology adopted by the present invention does not require the aid of a structural model and is applicable to complex structures.

[0032] 3. The present invention greatly weakens the influence of temperature change on damage monitoring and improves the accuracy of the electromechanical impedance monitoring technology. Description of the Drawings

[0033] Figure 1 is the flowchart of the classification method of the present invention;

[0034] Figure 2 is the schematic diagram of the specimen structure and the piezoelectric ceramic array in the embodiment of the present invention;

[0035] Figure 3 is the change diagram of the real part signal of admittance in the baseline dataset of the present invention at different temperatures;

[0036] Figure 4 is the change diagram of the imaginary part signal of admittance in the baseline dataset of the present invention at different temperatures;

[0037] Figure 5 is the change diagram of the damage diagnosis index curve in the healthy state at 31°C of the present invention;

[0038] Figure 6 is the change diagram of the damage diagnosis index curve in the damaged state at 31°C of the present invention;

[0039] Figure 7 is the change diagram of the threshold curve calculated according to the baseline data at 30°C and 35°C; Detailed Embodiment

[0040] To more fully understand the technical content of the present invention, the technical solution of the present invention will be further introduced and described below in combination with specific embodiments, but it is not limited thereto.

[0041] The present invention relates to a method for electromechanical impedance damage monitoring and automatic classification based on environment matching, and is particularly applicable to non-destructive testing of composite material structures at different temperatures. The flowchart is as Figure 1 shown, and the main steps are as follows:

[0042] A classification method for electromechanical impedance damage monitoring based on environment matching includes:

[0043] Assemble the structure to be tested, and collect signals from the structure by the voltage-current method to obtain the conductance admittance signal of the structure;

[0044] Decompose the conductance admittance signal into real and imaginary part signals and store them in the baseline dataset, and conduct damage experiments on the structure in different temperature environments in sequence, and store the conductance admittance signals after damage in different temperature environments into the corresponding test data groups;

[0045] The test data groups will be selected one by one to match with the baseline data set, the correlation coefficients between the baseline data set and each test data group will be calculated respectively, and the imaginary part signals of the test data group and all the imaginary part signals in the baseline data set will be compared based on the correlation coefficients;

[0046] Select the imaginary part signals in the baseline data set with two correlation coefficients greater than others, determine the temperature range of the test data group based on the selected imaginary part signals, and obtain the real part signals corresponding to the imaginary part signals of the test data;

[0047] Import the corresponding real part signals into the cross-correlation function to calculate the structural damage diagnosis index; and find the two groups of baseline data closest to each group of test data in the baseline data set;

[0048] In each round of matching, select the maximum value of the cross-correlation function of the real part signals of one group of baseline data as the same-degree damage diagnosis index, and take the maximum value of the cross-correlation function of the real part signals of the two groups of baseline data as the judgment threshold for the same-degree damage;

[0049] Obtain the damage diagnosis index and judgment threshold under different degrees of damage according to multiple groups of test data, and classify the damage of the structure.

[0050] The method for assembling the structure to be tested is as follows:

[0051] Install two piezoelectric ceramics equidistantly on the structure to be tested, determine one of the piezoelectric ceramics as the exciter and the other piezoelectric ceramic as the sensor, so as to form a signal acquisition channel for the admittance signal.

[0052] In this embodiment, a composite material plate is used as the experimental structure, and its size is 450mm * 140mm * 3mm. Two piezoelectric ceramic sensors are installed on the structure, as Figure 2 shown. The left piezoelectric ceramic is used as the exciter, and the right piezoelectric ceramic is used as the sensor.

[0053] The method for acquiring the signal of the structure to obtain the admittance signal of the structure by the voltage-current method includes:

[0054] According to the voltage-current measurement method, connect a voltage-dividing resistor in series between the exciter and the sensor. The signal generator inputs a sinusoidal excitation signal to the exciter, and the admittance signal is calculated according to the vector Ohm's law by collecting the voltage signal at both ends of the sensor and the resistance value of the voltage-dividing resistor; the frequency range of the excitation signal is 20kHz - 120kHz, the amplitude of the sinusoidal excitation signal is 9V, and the voltage-dividing resistor is 9080Ω. In this embodiment, the experimental structure is placed in a constant temperature oven to simulate the temperature change, record the admittance signals in the healthy state at different temperatures and decompose them into real part signals and imaginary part signals as the baseline data set. The collected baseline data set is as Figures 3 to 4 shown.Figure 3 is the real signal of admittance, Figure 4 It is the imaginary part signal of admittance; the response signals are collected as test data in the healthy state and the crack damage state at a temperature of 31°C.

[0055] Methods for decomposing the admittance signal into real and imaginary signals and storing them in the baseline data set include:

[0056] When the structure is in a healthy state, the admittance signal is collected every 5°C at the preset experimental temperature, and is continuously collected as a baseline data set. During the storage process, the admittance signal is decomposed into a real signal and an imaginary signal;

[0057] The real signal is associated with structural damage and temperature data, and the imaginary signal is only associated with temperature data, so that the real signal is used for damage monitoring calculations, and the imaginary signal is used for temperature compensation applications.

[0058] In this embodiment, the imaginary signals of the response signals under the healthy state and the crack damage state at 31°C are extracted, and the imaginary signals of the test data and all the imaginary signals in the baseline data set are compared using the correlation coefficient Cc. The two sets of baseline data with the largest correlation coefficient Cc are selected to determine the temperature range in which the test data is located.

[0059] The calculation formula of the correlation coefficient is:

[0060]

[0061] Among them, Cc represents the correlation between the two groups of signals, Z b (k) and Z t (k) represent the imaginary part signals of the baseline data set and the test data, respectively. and Represents the average value of the imaginary part of the admittance signal of the baseline data set and the test data, respectively, with a frequency range of ω I and ω F between.

[0062] The imaginary part signal of the response signal under the healthy state and crack damage state at 31℃ has the highest correlation coefficient Cc with the imaginary part signal of 30℃ and 35℃ in the baseline data, which are 0.9643 and 0.9421 respectively. Therefore, the baseline data closest to the test data are the baseline data of 30℃ and 35℃, and the corresponding real part signal is extracted according to the imaginary part signal.

[0063] In a further embodiment, the cross-correlation function is expressed as:

[0064]

[0065] Where X(u) and Y(u) represent the closest set of baseline data and test data in the frequency domain, respectively. I represents the starting frequency, ω F represents the final frequency, and Δu represents the frequency shift of the admittance signal.

[0066] The judgment threshold is also obtained by formula (2); where X(u) and Y(u) are the two sets of baseline data closest to the test data.

[0067] If the damage diagnosis index in the test data is greater than the threshold, it means that the structure is healthy. If the damage diagnosis index is less than or equal to the threshold, it means that there is damage in the electromechanical impedance structure.

[0068] The cross-correlation function is calculated based on the real signal of the test data and the closest set of baseline data, and its maximum value is used as the damage diagnosis index. The 31℃ healthy damage diagnosis index is 494.1, as shown in Figure 5 As shown in the figure, the damage diagnosis index of the 31℃ damage state is 466.9. Figure 6 As shown, the threshold is also obtained by formula (2). The threshold is 488.2 when the two sets of baseline data closest to the test data set are selected. Figure 7 shown.

[0069] The damage diagnosis of this embodiment is based on the following premise: the correlation between the test data and the closest baseline data should always be higher than the correlation between the two selected baseline data, unless the test data set is obtained from the damaged state of the structure. For the undamaged case, this assumption is reasonable.

[0070] according to Figure 5 , 6 , Figure 7 The comparison of the maximum value can clearly determine the current state of the structure, and will not cause false alarms due to temperature changes, effectively eliminating the impact of temperature on damage monitoring signals.

[0071] To sum up, the electromechanical impedance technology adopted in the present invention is also extremely sensitive to tiny damage monitoring. Secondly, the electromechanical impedance technology adopted in the present invention does not require the aid of structural models and is suitable for complex structures. Finally, the present invention greatly weakens the impact of temperature changes on damage monitoring and improves the accuracy of electromechanical impedance monitoring technology.

[0072] Embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0073] Embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0074] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0075] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0077] The above are only the preferred embodiments of the present invention. Without departing from the technical principles of the present invention, several improvements and modifications can also be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A classification method for electromechanical impedance damage monitoring based on environmental matching, characterized in that, Including: Assemble the structure to be measured, and collect signals from the structure by the voltage-current method to obtain the conductance susceptance signal of the structure; Decompose the conductance susceptance signal into real and imaginary part signals and store them in the baseline data set. Then, conduct damage experiments on the structure under different temperature environments in sequence, and store the conductance susceptance signals after damage under different temperature environments into the corresponding test data groups respectively; Select the test data groups one by one to match with the baseline data set, calculate the correlation coefficients between the baseline data set and each test data group respectively, and compare the imaginary part signals of the test data groups with all the imaginary part signals in the baseline data set based on the correlation coefficients; Select two groups of imaginary part signals in the baseline data set with correlation coefficients greater than others, determine the temperature range of the test data group based on the selected imaginary part signals, and obtain the real part signals corresponding to the imaginary part signals of the test data; Import the corresponding real part signals into the cross-correlation function to calculate the damage diagnosis index of the structure; and find the two groups of baseline data closest to each group of test data in the baseline data set; In each round of matching, select the maximum value of the cross-correlation function of the real part signals of one group of baseline data as the damage diagnosis index of the same degree of damage, and use the maximum value of the cross-correlation function of the real part signals of the two groups of baseline data as the judgment threshold of the same degree of damage; Obtain the damage diagnosis index and judgment threshold under different degrees of damage according to multiple groups of test data, and classify the damage of the structure; The calculation formula of the correlation coefficient is: Among them, Cc represents the correlation between two sets of signals, Z b (k) and Z t (k) respectively represent the imaginary part signals of the admittance of the baseline data set and the test data, and respectively represent the average values of the imaginary part signals of the admittance of the baseline data set and the test data, and the frequency range is between ω I and ω F .

2. The classification method for electromechanical impedance damage monitoring based on environment matching according to claim 1, wherein The method for assembling the structure to be measured is: Install two piezoelectric ceramics equidistantly on the structure to be measured, determine one of the piezoelectric ceramics as the exciter and the other as the sensor, so as to form a signal acquisition channel for the conductance susceptance signal.

3. The classification method for electromechanical impedance damage monitoring based on environmental matching according to claim 1, characterized in that, The method for collecting signals from the structure by the voltage-current method to obtain the conductance susceptance signal of the structure includes: According to the voltage-current measurement method, connect a voltage-dividing resistor in series between the exciter and the sensor. The signal generator inputs a sinusoidal excitation signal to the exciter, and calculate the conductance susceptance signal according to the vector Ohm's law by collecting the voltage signal at both ends of the sensor and the resistance value of the voltage-dividing resistor.

4. The classification method for electromechanical impedance damage monitoring based on environmental matching according to claim 3, wherein The frequency range of the excitation signal is 20 kHz - 120 kHz, the amplitude of the sinusoidal excitation signal is 9 V, and the voltage-dividing resistor is 9080 Ω.

5. The classification method for electromechanical impedance damage monitoring based on environment matching according to claim 1, characterized in that The method for decomposing the conductance susceptance signal into real and imaginary part signals and storing them in the baseline data set includes: When the structure is in a healthy state, collect the conductance susceptance signal once every 5 °C at the preset experimental temperature, continuously collect it as the baseline data set, and decompose the conductance susceptance signal into real part signals and imaginary part signals during the storage process; Among them, the real part signal is associated with the structure damage, and the imaginary part signal is associated with the temperature data, so as to use the real part signal for damage monitoring calculation and the imaginary part signal for environmental matching application.

6. The classification method for electromechanical impedance damage monitoring based on environment matching according to claim 1, wherein The expression formula of the cross-correlation function is: where X(u) and Y(u - Δu) respectively represent the closest set of baseline data and test data in the frequency domain, ω I represents the starting frequency, ω F represents the final frequency, and Δu represents the frequency shift of the admittance signal.

7. The classification method for electromechanical impedance damage monitoring based on environmental matching according to claim 1, wherein The judgment threshold is also obtained through formula (2); where X(u) and Y(u - Δu) are respectively the two groups of baseline data closest to the test data; If the damage diagnosis index in the test data is greater than the threshold, it indicates that the structure is healthy; if the damage diagnosis index is less than or equal to the threshold, it indicates that there is damage in the mechanical impedance structure.

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