CN102353718A - Lamb wave damage probability imaging method for damage monitoring of composite plate structure - Google Patents

Abstract

The invention discloses a Lamb wave damage probability imaging method for damage monitoring of a composite plate structure, which belongs to the field of monitoring of engineering structure health of composite plates. According to the invention, the technology of excitation/sensing arrays is utilized; reference signals of the composite plate structure in a sound state and monitoring signals of the composite plate structure in a damage state are acquired by loading Lamb wave; then the means of wavelet analysis is employed to extract an energy difference coefficient of damage, and the position where damage is located is predicated; finally, a structure damage image is obtained by using the probability imaging method. The invention enables rapid and effective damage identification of the composite plate structure to be realized and high precision in damage identification to be obtained, and has a good engineering application value.

Description

The Lamb ripple damage probability formation method that is used for the composite panel structure damage monitoring

Technical field

The present invention relates to a kind of probability formation method, relate in particular to a kind of Lamb ripple damage probability formation method that is used for the composite panel structure damage monitoring, belong to composite panel class engineering structure health monitoring field.

Background technology

Advanced composite material has obtained widespread use at aerospace field, but composite structure can damage in production and use inevitably.In order to find the damage that these possibly exist in time; And judge damage the position, confirm the degree of damage; So structural health monitoring technology is as a kind of online, in real time, damage detecting method becomes a big focus of current concern fast; Wherein, can monitor fast and accurately large tracts of land structures such as aircraft wings with it based on Lamb wave structure health monitor method and receive growing interest.

Using the Lamb ripple that structure is carried out online health monitoring has an important feature, can develop the damage formation method exactly and structural damage carried out real-time visual, for confirming damage position, identification of damage degree a kind of approach of quicklook is provided.Existing multiple damage imaging algorithm mainly comprises phase array method, time reversal method and deflection method etc.These methods generally than higher, all need be subtracted each other the scattered signal to obtain damaging to the signal before and after the damage to the requirement of signal quality, the image that obtains damaging through multiple signal processing means again, and the precision of images and sharpness are not high.These imaging algorithms often need the too much time to carry out signal analysis and damage identification because calculated amount is excessive simultaneously, are difficult to satisfy online, the requirement fast of structural healthy monitoring system.Therefore in active Lamb wave structure health monitoring technology, adopt conventional equipment and method to be difficult to obtain the image of damage at present.

Summary of the invention

The present invention is directed to the defective that prior art exists, and propose fast, the effective Lamb ripple damage probability formation method that is used for the composite panel structure damage monitoring of a kind of speed.

This method comprises the steps:

Step 1: gather composite panel structural response signal, concrete steps are following:

Step 1-1: on the compound substance plate structure, arrange excitation/sensor array of being made up of n piezoelectric element, n gets 10～20, and n is a natural number;

Step 1-2: a piezoelectric element among the selected step 1-1 in the excitation/sensor array is as driver; Through signal generator and power amplifier; Lamb ripple narrow band signal is loaded on the selected driver excites pumping signal, another piezoelectric element in the selected simultaneously excitation/sensor array is gathered structural response as sensor;

Step 1-3: with the structural response under the compound substance plate structure serviceable condition as reference signal; With the structural response under the faulted condition as monitor signal; Piezoelectric element in the selected successively excitation/sensor array obtains reference signal and monitor signal on all excitation/sensing paths as driver;

Step 2: extract the excitation/sensing path damage characteristic of wavelet transformation, particular content is following:

Reference signal and monitor signal thatstep 1 is obtained carry out wavelet transformation respectively, extract the local time-energy density of the main frequency band of signal;

Confirm the damage criterion in all excitation/sensing paths based on local time-energy density of extracting; When the monitor signal on the excitation/sensing path is compared reference signal and is changed; Then there is damage in the composite plate structure, predicts damage position with damage criterion;

The imaging of step 3:Lamb ripple damage probability, particular content is following:

Utilize the probability formation method that the structural excitation of composite panel/sensor array monitored area is divided into set one by one; Exist probable value to carry out linear superposition the damage of the point on all excitation/sensing paths, promptly obtain the damage image of compound substance plate structure.

Technique effect:

1, the present invention can fast and effeciently realize the damage identification of composite structure, guarantees composite structure security in use.

2, the present invention need not change or increase equipment and parameter in implementation procedure, utilizes existing hardware device just can realize.

3, the damage criterion of confirming among the present invention can characterize the feature difference of structural damage front and back Lamb ripple signal well, and simultaneously, the method for distilling of this index is simple, quick, can reduce the influence of environmental factor to the damage criterion accuracy effectively.

4, damage recognition result of the present invention accurately, clear picture, can be simply, apace that STRUCTURE DAMAGE LOCATION and degree is visual, having preferably, practical engineering application is worth.

Description of drawings

Excitation/sensor array and the path synoptic diagram of Fig. 1 for arranging among the present invention.

Fig. 2 is range of influence, excitation/sensing path (the oval distribution) synoptic diagram.

Fig. 3 is excitation signal waveforms figure.

Fig. 4 (a) and (b) are respectively the benchmark and the monitor signal oscillogram in two excitation/sensing paths.

Fig. 5 (a) and (b) are respectively the local time-energy density figure of Fig. 4 (a) and (b).

Fig. 6 is the damage recognition image.

Embodiment

Below in conjunction with accompanying drawing the inventive method is described further.

The inventive method specifically comprises the steps:

Step 1: gather composite panel structural response signal, concrete steps are following:

Step 1-1: on the compound substance plate structure, arrange excitation/sensor array of being made up of n piezoelectric element, n gets 10～20, and n is a natural number;

Step 1-2: a piezoelectric element Sj in the selected excitation/sensor array is as driver; Through signal generator and power amplifier; Lamb ripple narrow band signal is loaded on the selected driver excites pumping signal, another piezoelectric element Si in the selected simultaneously excitation/sensor array gathers structural response as sensor;

Step 1-3: with the structural response under the compound substance plate structure serviceable condition as reference signal; With the structural response under the faulted condition as monitor signal; Piezoelectric element in the selected successively excitation/sensor array obtains reference signal and monitor signal on all excitation/sensing paths as driver.

Step 2: extract the excitation/sensing path damage characteristic of wavelet transformation, particular content is following:

Adopt the Gabor wavelet basis function that reference signal and the monitor signal thatstep 1 obtains carried out wavelet transformation respectively, extract the local time-energy density of the main frequency band of signal;

Confirm the damage criterion in all excitation/sensing paths according to local time-energy density of extracting; When the monitor signal on the excitation/sensing path is compared reference signal and is changed; Then there is damage in the compound substance plate structure, predicts damage position with damage criterion.

The definition damage criterion is following:

$\mathrm{DI}=|1-\frac{{\∫}_{b_1}^{b_2}{E_{V_D}}^{\′}\left(b\right)\mathrm{db}}{{\∫}_{b_1}^{b_2}{E_{V_B}}^{\′}\left(b\right)\mathrm{db}}|$

In the formula: V_BIt is the Lamb ripple reference signal that records under the structure serviceable condition; V_DIt is the Lamb ripple monitor signal that records under the structural damage state; E ' is at yardstick [a behind the signal process wavelet analysis (b)₁, a₂], the local time-energy density under the b constantly; [b₁, b₂] expression carries out the time range of wavelet analysis to signal.

In the ideal case, there is not damage as if in the structure, so V_BWith V_DIdentical, i.e. damage criterion DI=0; If have damage, V so in the structure_BWith V_DCan there are differences, difference is big more, and damage criterion DI is just big more, and maximum can be near 1.

The imaging of step 3:Lamb ripple damage probability, particular content is following:

Utilize the probability formation method that the structural excitation of composite panel/sensor array monitored area is divided into set one by one; Because each piezoelectric element both can encourage the Lamb ripple in structure; Also can receive the Lamb ripple, so n piezoelectric element constituted the bar excitation/sensing path of n * (n-1).If existence damage in the structure damages the variation maximum that the Lamb ripple signal on the excitation/sensing path that belongs to takes place so, along with the increase of distance between injury region and the excitation/sensing path, the Lamb ripple signal variation that damage causes will diminish gradually.

The damage of any point obtains after existing probable value to be multiplied each other by the damage criterion on this excitation/sensing path, some place and this fiducial probability corresponding on the path in the monitored area; Exist probable value to carry out linear superposition the damage of the point on all excitation/sensing paths, finally obtain damage image.

Suppose in the monitored area total N bar excitation/sensing path, the every bit in the monitored area is damaged have probability estimate:

$P(x,y)=\underset{k=1}{\overset{N}{\mathrm{\Σ}}}{p}_k(x,y)=\underset{k=1}{\overset{N}{\mathrm{\Σ}}}{A}_k[\frac{-1}{\mathrm{\β}-1}\·R(x,y,x_{\mathrm{ak}},y_{\mathrm{ak}},x_{\mathrm{sk}},y_{\mathrm{sk}})+\frac{\mathrm{\β}}{\mathrm{\β}-1}]$

Wherein:

$R(x,y,x_{\mathrm{ak}},y_{\mathrm{ak}},x_{\mathrm{sk}},y_{\mathrm{sk}})=\left\{\begin{array}{cc}{R}_c(x,y,x_{\mathrm{ak}},y_{\mathrm{ak}},x_{\mathrm{sk}},y_{\mathrm{sk}}),& R_c(x,y,x_{\mathrm{ak}},y_{\mathrm{ak}},x_{\mathrm{sk}},y_{\mathrm{sk}})<\mathrm{\&beta;}\\ \mathrm{\&beta;},& R_c(x,y,x_{\mathrm{ak}},y_{\mathrm{ak}},x_{\mathrm{sk}},y_{\mathrm{sk}})\&GreaterEqual;\mathrm{\&beta;}\end{array}\right.$

$R_c(x,y,x_{\mathrm{ak}},y_{\mathrm{ak}},x_{\mathrm{sk}},y_{\mathrm{sk}})=\frac{d_a+d_s}{d_{\mathrm{as}}}=\frac{\sqrt{{(x-x_{\mathrm{ak}})}^2+{(y-y_{\mathrm{ak}})}^2}+\sqrt{{(x-x_{\mathrm{sk}})}^2+{(y-y_{\mathrm{sk}})}^2}}{\sqrt{{(x_{\mathrm{ak}}-x_{\mathrm{sk}})}^2+{(y_{\mathrm{ak}}-y_{\mathrm{sk}})}^2}}$

In the formula: d_a(x is y) to driver center (x for imaging point_a, y_a) distance; d_s(x is y) to center sensor position (x for imaging point_s, y_s) distance; d_AsBe driver center (x_a, y_a) to center sensor position (x_s, y_s) distance; p_k(x y) is the probability estimate that has damage on the k bar excitation/sensing path; A_k=DI is the signal difference coefficient in k bar excitation/sensing path, i.e. damage criterion; β be one greater than 1 dimensional parameters, it is controlling the size of excitation/range of influence, sensing path, gets β=1.04 here.

As shown in Figure 2, as R (x, y, x_Ak, y_Ak, x_Sk, y_Sk)=1 o'clock, (x y) is located immediately on excitation/sensing path p to imaging point_k(x, y)=A_kAs R (x, y, x_Ak, y_Ak, x_Sk, y_SkDuring)=β, (x y) is positioned at oval edge, p to imaging point_k(x, y)=0.P (x, value y) is big more, and (x y) locates to exist the probability of damage just big more at imaging point.

Introduce one embodiment of the present of invention below:

The hardware components that uses among the embodiment is identical with the hardware components of traditional monitoring system, by forming with the lower part: control computer, piezoelectric excitation/sensing network, multi-channel switch, signal generator, power amplifier, charge amplifier/voltage amplifier and data collector.

Composite panel adopts the carbon fibre reinforced composite plate that is of a size of long 350mm, wide 300mm, thick 3mm.Excitation/sensor array of arranging among the embodiment and path are true origin with the plate center as shown in Figure 1, adopt 12 piezoelectric elements to be arranged to the circular piezoelectric element arrays that radius is 100mm onboard, the center of Simulation Damage be (15mm, 28mm).

Selected driver (piezoelectric element Sj) excites pumping signal in structure, this signal is a sinusoidal modulation signal, and centre frequency is 100kHz, as shown in Figure 3.Selected sensor (piezoelectric element Si, i ≠ j, i, j=1,2,3...), amplify the structural response signals collecting in computing machine through charge amplifier.Embodiment records earlier Lamb ripple reference signal under the undamaged state of structure;, structure records Lamb ripple monitor signal then under having faulted condition; With reference to Fig. 1; With excitation/sensing path 4-11 (not through damage) and path 3-8 (through damage) is example, and the benchmark on two paths, monitor signal are respectively shown in Fig. 4 (a) and (b).

Choose this frequency band of 50～150kHz as yardstick, respectively reference signal and the monitor signal of gathering carried out wavelet transformation, extract the local time-energy density of signal.Be example with excitation/sensing path 4-11 and 3-8 equally, the benchmark on two paths, the local time-energy density of monitor signal are respectively shown in Fig. 5 (a) and (b).

The damage image that utilization probability formation method obtains as shown in Figure 6; The darker regions at figure middle part representes to exist the probability of damage bigger; The lesion center position that " zero " expression in the white box identifies; Its coordinate is (18mm; 31mm); (15mm, 28mm), visible recognition result is more accurate in " * " expression actual damage center.

Claims (2)

Translated fromChinese

1.一种用于复合材料板结构损伤监测的Lamb波损伤概率成像方法，其特征在于：1. A Lamb wave damage probability imaging method for composite plate structure damage monitoring, characterized in that:该方法包括如下步骤：The method comprises the steps of:步骤1：采集复合材料板结构响应信号，具体步骤如下：Step 1: Collect the response signal of the composite material plate structure, the specific steps are as follows:步骤1-1：在复合材料板结构上布置由n个压电元件组成的激励/传感阵列，n取10～20，n为自然数；Step 1-1: Arrange an excitation/sensing array composed of n piezoelectric elements on the composite material plate structure, where n is 10-20, and n is a natural number;步骤1-2：选定步骤1-1中激励/传感阵列中的一个压电元件(Sj)作为激励器，通过信号发生器和功率放大器，将Lamb波窄带信号加载到选定的激励器上激发激励信号，同时选定激励/传感阵列中的另一个压电元件(Si)作为传感器来采集结构响应；Step 1-2: Select a piezoelectric element (Sj) in the excitation/sensing array in step 1-1 as the exciter, and load the Lamb wave narrowband signal to the selected exciter through the signal generator and power amplifier Stimulate the excitation signal, and select another piezoelectric element (Si) in the excitation/sensing array as the sensor to collect the structural response;步骤1-3：将复合材料板结构完好状态下的结构响应作为基准信号，将损伤状态下的结构响应作为监测信号，依次选定步骤1-1中激励/传感阵列中的压电元件作为激励器，获得所有激励/传感路径上的基准信号和监测信号；Step 1-3: Take the structural response of the composite plate in a sound state as the reference signal, and the structural response in the damaged state as the monitoring signal, and select the piezoelectric elements in the excitation/sensing array in step 1-1 in turn as the Actuators, to obtain reference and monitor signals on all excitation/sensing paths;步骤2：提取小波变换的激励/传感路径损伤特征，具体内容如下：Step 2: Extract the excitation/sensing path damage characteristics of the wavelet transform, the specific content is as follows:对步骤1得到的基准信号和监测信号分别进行小波变换，提取信号主要频带的局部时间-能量密度；Perform wavelet transform on the reference signal and monitoring signal obtained in step 1, respectively, to extract the local time-energy density of the main frequency band of the signal;根据提取的局部时间-能量密度确定所有激励/传感路径的损伤指标，当激励/传感路径上的监测信号相比基准信号发生变化时，则复合材料板结构中存在损伤，用损伤指标来预测损伤位置；According to the extracted local time-energy density, the damage index of all excitation/sensing paths is determined. When the monitoring signal on the excitation/sensing path changes compared with the reference signal, there is damage in the composite plate structure, and the damage index is used to determine Predict the location of the damage;步骤3：Lamb波损伤概率成像，具体内容如下：Step 3: Lamb wave damage probability imaging, the specific content is as follows:利用概率成像方法把复合材料板结构上的激励/传感阵列监测区域划分成一个个点的集合，将所有激励/传感路径上的点的损伤存在概率值进行线性叠加，即得到复合材料板结构的损伤图像。Using the probabilistic imaging method, the excitation/sensing array monitoring area on the composite material plate structure is divided into a set of points, and the damage existence probability values of all points on the excitation/sensing path are linearly superimposed to obtain the composite material plate Damaged images of structures.2.根据权利要求1所述的用于复合材料板结构损伤监测的Lamb波损伤概率成像方法，其特征在于：所述步骤2中的小波变换采用Gabor小波基函数。2. The Lamb wave damage probability imaging method for damage monitoring of composite plate structures according to claim 1, characterized in that: the wavelet transform in the step 2 uses Gabor wavelet basis functions.

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