WO2019113994A1 - 基于激励波束成型和加权图像融合的music腐蚀监测方法 - Google Patents

基于激励波束成型和加权图像融合的music腐蚀监测方法 Download PDF

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WO2019113994A1
WO2019113994A1 PCT/CN2017/117066 CN2017117066W WO2019113994A1 WO 2019113994 A1 WO2019113994 A1 WO 2019113994A1 CN 2017117066 W CN2017117066 W CN 2017117066W WO 2019113994 A1 WO2019113994 A1 WO 2019113994A1
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array
corrosion
corrosion damage
signal
damage
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French (fr)
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袁慎芳
鲍峤
邱雷
郭方宇
任元强
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南京航空航天大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/22—Details, e.g. general constructional or apparatus details
    • G01N29/26—Arrangements for orientation or scanning by relative movement of the head and the sensor
    • G01N29/262—Arrangements for orientation or scanning by relative movement of the head and the sensor by electronic orientation or focusing, e.g. with phased arrays
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/04—Analysing solids
    • G01N29/041—Analysing solids on the surface of the material, e.g. using Lamb, Rayleigh or shear waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/04—Analysing solids
    • G01N29/06—Visualisation of the interior, e.g. acoustic microscopy
    • G01N29/0654—Imaging
    • G01N29/069—Defect imaging, localisation and sizing using, e.g. time of flight diffraction [TOFD], synthetic aperture focusing technique [SAFT], Amplituden-Laufzeit-Ortskurven [ALOK] technique
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
    • G01N29/4418—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison with a model, e.g. best-fit, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/251—Fusion techniques of input or preprocessed data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/28—Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/772—Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
    • G01N2291/02—Indexing codes associated with the analysed material
    • G01N2291/023—Solids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
    • G01N2291/02—Indexing codes associated with the analysed material
    • G01N2291/025—Change of phase or condition
    • G01N2291/0258—Structural degradation, e.g. fatigue of composites, ageing of oils
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
    • G01N2291/04—Wave modes and trajectories
    • G01N2291/042—Wave modes
    • G01N2291/0427—Flexural waves, plate waves, e.g. Lamb waves, tuning fork, cantilever
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
    • G01N2291/10—Number of transducers
    • G01N2291/106—Number of transducers one or more transducer arrays
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/06—Recognition of objects for industrial automation

Definitions

  • the invention belongs to the technical field of engineering structure health monitoring, and particularly relates to a MUSIC corrosion damage monitoring method.
  • Lamb wave based structural health monitoring method has a good application prospect because Lamb wave has long-distance propagation ability and is sensitive to small damage. Since the sensor array is easy to arrange on the structure and has the function of direction scanning, the array signal processing method is gradually introduced into the structural health monitoring area.
  • the MUSIC (Multiple Signal Classification) algorithm is one of the representative algorithms.
  • the MUSIC algorithm belongs to the subspace class algorithm. The basic idea of the algorithm is to decompose the covariance matrix of the output data of any array to obtain the signal subspace corresponding to the signal component and the noise subspace orthogonal to the signal component. Then, the orthogonality of the two subspaces is used to estimate the parameters of the signal.
  • MUSIC corrosion damage monitoring corrosion damage does not produce Lamb waves. Therefore, active Lamb wave method is needed and the scattering signal of corrosion damage is used to achieve positioning and degree evaluation. However, the scattering signal of corrosion damage is very weak, and the signal-to-noise ratio is low, resulting in low positioning accuracy of the MUSIC method.
  • the usual MUSIC damage location method uses a one-dimensional uniform linear array to accept Lamb waves, but the one-dimensional line array has a blind spot problem in the vicinity of 0° and 180° angles. If the corrosion damage occurs in the blind zone, ie [0°30°] and [150°180°], it cannot be positioned. In addition, the current MUSIC algorithm cannot evaluate corrosion loss. The extent of the injury.
  • the present invention aims to provide a MUSIC corrosion monitoring method based on excitation beamforming and weighted image fusion, enhance the scattering signal of corrosion damage and improve its signal-to-noise ratio, and eliminate the traditional one-dimensional uniform linear array. Monitoring blind spots and achieving location and depth assessment of corrosion damage
  • the array A and the array S are alternately excited to emit Lamb waves, and the response signals of the corresponding sensor arrays are collected and recorded as monitoring signals; the reference signals are subtracted from the monitoring signals and recorded as scattering signals of corrosion damage. ;
  • step (6) superimposing the large eigenvalues of the covariance matrix of the scattered signal according to the weight obtained in step (6), and further The corrosion factor is calculated and the corrosion damage depth is evaluated using the corrosion factor.
  • the array A and the array S are both one-dimensional uniform linear arrays, and both arrays include 2N+1 array elements.
  • Each array element in the array A is represented by A i , and each array element in the array S is used.
  • step (2) the driving array A is excited to emit Lamb waves, that is, each array element in the array A is sequentially used as an excitation to obtain a corresponding scattering signal of corrosion damage:
  • Vector array A and the array element A i serves as an incentive, the scattering signal array S of all array elements to obtain a composition
  • N (t) is background array S each array element acquisition channel noise vector composed
  • x a (t ) is the scattering signal of the corrosion damage acquired by the reference array element in array S
  • A(r, ⁇ ) is the steering vector
  • j is the imaginary unit.
  • d is the spacing of adjacent array elements in the array
  • c is the signal propagation speed
  • r and ⁇ are the distance and angle of the damage relative to the array S, respectively
  • t represents the moment of the signal;
  • step (3) first calculating Covariance matrix
  • U S and U N are respectively a signal subspace and a noise subspace
  • ⁇ S and ⁇ N are respectively large eigenvalues corresponding to the signal subspace and small eigenvalues corresponding to the noise subspace
  • step (4) the distance between each array element in the excitation source array and the estimated position of the corrosion damage is calculated:
  • l is the distance between the excitation source array and the sensor array
  • r 1 is the distance information of the initial estimated position of the corrosion damage
  • ⁇ 1 is the direction information of the initial estimated position of the corrosion damage
  • step (6) if the corrosion damage of one of the corrosion imaging results is located in the monitoring dead zone of the sensor array, the weight of the corrosion imaging result is set to 0, and the weight of the other corrosion imaging result is 1 If the corrosion damages located by the two corrosion imaging results are within the monitorable area of the respective sensor array, the weights of the two corrosion imaging results are set to 0.5.
  • step (7) the corrosion factor is calculated according to the following formula:
  • ⁇ max is a large eigenvalue of the scatter signal covariance matrix superimposed according to the weight
  • u A and u S are the peaks of the excitation signal and the corrosion scatter signal, respectively.
  • step (7) the corrosion damage depth is calculated according to the following formula:
  • the invention effectively improves the corrosion damage location accuracy based on the MUSIC algorithm; can monitor the corrosion damage in the blind area of the one-dimensional line array monitoring, expands the monitoring area; can evaluate the depth of corrosion damage, and can be effectively applied to the real corrosion damage of the aviation structure Monitoring.
  • Figure 1 is a flow chart of the method of the present invention
  • FIG. 2 is a schematic view showing an arrangement of an aluminum plate and a double array in the embodiment
  • FIG. 3 is a diagram showing the results of MUSIC corrosion damage localization under a single excitation source in the embodiment, wherein (a) is a corrosion damage scattering array signal diagram under a single excitation source, and (b) is a spatial spectrum diagram;
  • 5 is a spatial spectrum of the weighted fusion in the embodiment, wherein (a) is a spatial spectrum when the array S is used as an excitation source array; and (b) is a weighted fusion spatial spectrum;
  • Figure 6 is a graph showing the relationship between corrosion factor and corrosion depth in the examples.
  • the invention proposes a MUSIC corrosion monitoring method based on excitation beamforming and weighted image fusion, as shown in Fig. 1, the process is as follows.
  • Step 1 When the structure is in a healthy state, the array A and the array S are alternately excited to emit Lamb waves, and the response signals of the corresponding sensor arrays are collected and recorded as reference signals;
  • Step 2 In the process of corrosion damage monitoring, the array A and the array S are alternately excited to emit Lamb waves. And collecting the response signal of the corresponding sensor array, recorded as a monitoring signal; subtracting the reference signal from the monitoring signal, and recording it as a scattering signal of corrosion damage;
  • Step 3 Based on the MUSIC algorithm and the scattering signal of the corrosion damage, the corrosion imaging result is obtained, and the peak point of the corrosion imaging result is searched, which is the initial estimation position of the corrosion damage;
  • Step 4 Calculate the time delay of the excitation element relative to the location of the corrosion damage according to the initial estimated position, and superimpose the scattering signal of the corrosion damage according to the scattering signal of the corrosion forward or backward movement corrosion damage to obtain enhanced corrosion damage. Scattering signal
  • Step 5 Substituting the scatter signal of the enhanced corrosion damage into the MUSIC algorithm to obtain a corresponding eigenvalue of the corrosion imaging result and the scatter signal covariance matrix;
  • Step 6 setting the corresponding weights for the corresponding corrosion imaging results when the array A and the array S are respectively used as the excitation source array, and merging the corrosion imaging results according to the weights to search for the peak point of the fusion imaging, that is, the corrosion damage position;
  • Step 7 According to the weight obtained in step 6, the large eigenvalues of the coherence matrix of the scattered signal are superimposed, and then the corrosion factor is calculated, and the corrosion damage depth is evaluated by the corrosion factor.
  • the size of the aluminum plate was 50 cm x 50 cm x 0.3 cm.
  • the structure surface is arranged in a double array consisting of two one-dimensional uniform linear arrays.
  • array A The above array is named array A, and the corresponding array elements are named PZT A -3 ,..., PZT A 3 .
  • array S The array below is named array S, and the corresponding array elements are named PZT S -3 ,..., PZT S 3 .
  • the array spacing of each array is 1.3 cm.
  • the spacing between the double arrays is 30 cm.
  • a chemical reaction was made with dilute hydrochloric acid and aluminum to cause corrosion damage.
  • the corrosion damage location was set at (220 cm, 120 °) and the corrosion diameter was 1.3 cm.
  • Corrosion was carried out in five stages, and the corrosion damage depth was measured by an ultrasonic C-scan after each corrosion stage to verify the corrosion damage depth evaluation method in the present invention.
  • the measured five stages of corrosion damage depth were 0.012 cm, 0.027 cm, 0.037 cm, 0.049 cm and 0.059 cm, respectively.
  • PZT A -3 , ..., PZT A 3 is respectively driven to excite the Lamb wave, and the Lamb wave response signals received by PZT S -3 , ..., PZT S 3 are acquired. Then, PZTS -3 , . . . , PZT S 3 is respectively driven in order to excite the Lamb wave, and the Lamb wave response signals received by PZT A -3 , . . . , PZT A 3 are acquired. Save these as a reference signal.
  • the PZT A -3 , ..., PZT A 3 is respectively excited to drive the Lamb wave, and the Lamb wave response signals received by PZT S -3 , ..., PZT S 3 are acquired. Then, PZT S -3 , ..., PZT S 3 is respectively driven in order to excite the Lamb wave, and the Lamb wave response signals received by PZT A -3 , ..., PZT A 3 are acquired. Save these as a monitoring signal.
  • a scatter signal of corrosion damage is obtained by monitoring the difference between the signal and the reference signal.
  • Array A is used as the excitation source array and array S is used as the sensor array.
  • ⁇ 0 is the center frequency of the propagated signal
  • ⁇ q represents the time delay of the wave of each array element in the sensor array relative to the reference array element
  • r, ⁇ represent the distance and angle of the damage relative to the sensor array, respectively
  • c is Signal propagation speed
  • d is the array element spacing.
  • K is the acquisition signal length
  • U S and U N are respectively the signal subspace and the noise subspace
  • ⁇ S and ⁇ N are small eigenvalues corresponding to the large eigenvalue corresponding to the signal subspace and the noise subspace, respectively.
  • the two-dimensional search of azimuth and distance is performed in the region, and the search steps of the azimuth and the distance are 1 degree and 1 mm, respectively, and a two-dimensional spatial spectrum is obtained according to formula (6), as shown in (b) of FIG. Show.
  • a two-dimensional spatial spectrum is obtained according to formula (6), as shown in (b) of FIG. Show.
  • the abscissa indicates the direction of arrival of the signal source, which is the preliminary estimate of the direction of corrosion damage ⁇ 1 .
  • the ordinate indicates the distance of the signal source, which is the preliminary estimate r 1 of the direction of corrosion damage.
  • the distance between each array element in the excitation source array and the estimated position can be calculated:
  • the distance between the reference array element in the source array and the initial estimated position According to the calculated delay, the corresponding sensor array response signal is moved forward or backward, so that the Lamb wave excited by each array element of the excitation source array virtually reaches the corrosion damage at the same time, that is, the focused corrosion damage scattering signal is obtained.
  • the focused corrosion damage scattering signal is shown in (a) of FIG.
  • the two-dimensional spatial spectrum can be retrieved. As shown in (b) of FIG. 4, the exact direction and distance of the corrosion damage can be obtained.
  • the array S is used as an excitation source array, and the array A is used as a sensor array. Consistent with the steps in 2, the MUSIC algorithm based on the excitation beamforming is applied, and the corrosion damage imaging result when the array A is used as the sensor array is also obtained, as shown in (a) of FIG.
  • the corrosion damage is in the monitorable area of the two arrays. Therefore, the weights of the corrosion damage imaging obtained twice before and after the setting are 0.5 and 0.5, respectively.
  • the imaging result after the superposition fusion is the same as (b) in FIG. By searching for the peak point of the image, it is the exact location of the corrosion damage.
  • the large eigenvalues are superimposed twice according to the weights in step 4, and the large eigenvalues of the final five corrosion damage stages are 0.019, 0.1304, 0.2285, 0.3268 and 0.4478.
  • the peak value of the excitation signal is 70V
  • the peak value of the corrosion damage scattering signal is 0.35V.
  • the corrosion factors of the five corrosion stages can be calculated, as shown in Fig. 6.
  • the corrosion depths of the five stages obtained by the combination are 0.012 cm, 0.027 cm, 0.037 cm, 0.049 cm and 0.059 cm.
  • the relationship between the corrosion depth D and the corrosion factor can be obtained by straight line fitting:
  • the depth of corrosion damage can be evaluated based on the corrosion factor.

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Abstract

本发明公开了基于激励波束成型和加权图像融合的MUSIC腐蚀监测方法。该方法针对损伤散射信号弱并影响MUSIC精度，首先将激励波束成型和双阵列引入到MUSIC方法中，增强腐蚀损伤的散射信号和提高其信噪比。其次将双阵列轮流作为激励或传感阵列，通过加权融合双阵列的腐蚀损伤成像，实现了一维线阵盲区内的腐蚀损伤监测。最后提出了基于阵列信号协方差矩阵特征值的腐蚀损伤因子，用以判别腐蚀损伤深度。本发明提高了MUSIC腐蚀监测的定位精度，扩大了传统一维线阵的监测区域，实现了腐蚀损伤深度的评估，在真实航空结构的腐蚀损伤监测方法具有广泛的应用前景。

Description

基于激励波束成型和加权图像融合的MUSIC腐蚀监测方法 技术领域
本发明属于工程结构健康监测技术领域，特别涉及了一种MUSIC腐蚀损伤监测方法。
背景技术
目前全球有超过14000架老龄化飞机，而老龄化飞机通常需要更加频繁的检测和维护。作为一种频繁发生的损伤，很多老龄化飞机正遭受着结构腐蚀。根据美国联邦航空管理局统计，老龄化飞机有关腐蚀的维护花费占总维护费用的30％。因此，腐蚀损伤监测成为当前结构健康监测中一个很重要的课题。
由于Lamb波具有长距离的传播能力且对小损伤敏感，基于Lamb波的结构健康监测方法具有很好的应用前景。由于传感器阵列易于在结构上布置且具有方向扫描的功能，阵列信号处理方法逐渐被引入结构健康监测区域。近些年，MUSIC(多重信号分类)算法是其中一种代表性的算法。MUSIC算法属于子空间类算法，其算法的基本思想是将任意阵列输出数据的协方差矩阵进行特征值分解，从而得到与信号分量相对应的信号子空间和与信号分量相正交的噪声子空间，然后利用这两个子空间的正交性来估计信号的参数。
在MUSIC腐蚀损伤监测中，腐蚀损伤并不能产生Lamb波，因而需要采用主动Lamb波方法并利用腐蚀损伤的散射信号来实现定位和程度评估。然而腐蚀损伤的散射信号很微弱，信噪比低，从而导致MUSIC方法的定位精度低。其次，通常的MUSIC损伤定位方法中采用一维均匀线阵来接受Lamb波，但一维线阵在靠近0°和180°角度范围内存在监测盲区问题。若腐蚀损伤发生在盲区内，即[0°30°]和[150°180°]，则对其不能定位。此外，目前MUSIC算法还不能评估腐蚀损 伤的程度。
发明内容
为了解决上述背景技术提出的技术问题，本发明旨在提供基于激励波束成型和加权图像融合的MUSIC腐蚀监测方法，增强腐蚀损伤的散射信号并提高其信噪比，消除传统一维均匀线阵的监测盲区，并实现腐蚀损伤的定位和深度评估
为了实现上述技术目的，本发明的技术方案为：
基于激励波束成型和加权图像融合的MUSIC腐蚀监测方法，包括以下步骤：
(1)在结构处于健康状态时，轮流驱动阵列A和阵列S激发Lamb波，并采集对应传感器阵列的响应信号，记为基准信号；
(2)在腐蚀损伤监测过程中，轮流驱动阵列A和阵列S激发Lamb波，并采集对应传感器阵列的响应信号，记为监测信号；将监测信号减去基准信号，记为腐蚀损伤的散射信号；
(3)基于MUSIC算法和腐蚀损伤的散射信号，得到腐蚀成像结果，搜索腐蚀成像结果的峰值点，即为腐蚀损伤的初估位置；
(4)根据此初估位置，计算激励阵元相对腐蚀损伤位置的时延，并根据该时延前移或后移腐蚀损伤的散射信号，叠加这些腐蚀损伤的散射信号获得增强的腐蚀损伤的散射信号；
(5)将增强的腐蚀损伤的散射信号再次代入MUSIC算法，得到对应的腐蚀成像结果和散射信号协方差矩阵的大特征值；
(6)对阵列A和阵列S分别作为激励源阵列时对应的腐蚀成像结果设置相应的权值，根据权值融合两次腐蚀成像结果，搜索融合成像的峰值点，即为腐蚀损伤位置；
(7)根据步骤(6)得到的权值叠加散射信号协方差矩阵的大特征值，进而 计算腐蚀因子，利用腐蚀因子评估腐蚀损伤深度。
进一步地，所述阵列A和阵列S均为一维均匀线阵，两个阵列均包含2N+1个阵元，阵列A中的各个阵元用Ai表示，阵列S中的各个阵元用Si表示，i＝-N,-(N-1),…,0,…,N-1,N。
进一步地，在步骤(2)中，驱动阵列A激发Lamb波，即依次将阵列A中每个阵元作为激励，得到对应的腐蚀损伤的散射信号：
Figure PCTCN2017117066-appb-000001
上式中，
Figure PCTCN2017117066-appb-000002
为阵列A中阵元Ai作为激励时，阵列S中所有阵元得到的散射信号组成的向量；N(t)为阵列S中各阵元采集通道的背景噪声组成的向量；xa(t)为阵列S中参考阵元获取的腐蚀损伤的散射信号；A(r,θ)为导向矢量，A(r,θ)中的元素aq(r,θ)＝exp(-jω0τq)，q＝N,-(N-1),...,0,...,N-1,N，ω0为传播信号的中心频率，j为虚数单位，
Figure PCTCN2017117066-appb-000003
d为阵列中相邻阵元的间距，c为信号传播速度，r和θ分别为损伤相对于阵列S的距离和角度；t表示信号的时刻；
驱动阵列S激发Lamb波的过程与阵列A相同。
进一步地，在步骤(3)中，首先计算
Figure PCTCN2017117066-appb-000004
的协方差矩阵
Figure PCTCN2017117066-appb-000005
Figure PCTCN2017117066-appb-000006
上式中，上标H表示Hermitian转置，K为采集信号长度；
然后对
Figure PCTCN2017117066-appb-000007
进行特征值分解：
Figure PCTCN2017117066-appb-000008
上式中，US、UN分别为信号子空间、噪声子空间，∑S、∑N分别为信号子空间对应的大特征值、噪声子空间对应的小特征值；
基于MUSIC算法的空间谱估计：
Figure PCTCN2017117066-appb-000009
在空间谱图中存在一个明显的波峰，即为腐蚀损伤的初估位置。
进一步地，在步骤(4)中，计算激励源阵列中各阵元与腐蚀损伤的初估位置的距离：
Figure PCTCN2017117066-appb-000010
上式中，l为激励源阵列与传感器阵列之间的间距，r1为腐蚀损伤初估位置的距离信息，θ1为腐蚀损伤初估位置的方向信息；
计算激励源阵列中各阵元至腐蚀损伤的时延：
Figure PCTCN2017117066-appb-000011
上式中，
Figure PCTCN2017117066-appb-000012
为激励源阵列中参考阵元与腐蚀损伤的初估位置的距离；
叠加各腐蚀损伤的散射信号获得增强的腐蚀损伤的散射信号：
Figure PCTCN2017117066-appb-000013
进一步地，在步骤(6)中，若其中一个腐蚀成像结果定位的腐蚀损伤位于其传感器阵列的监测盲区，则设置该腐蚀成像结果的权值为0，另一腐蚀成像结果的权值为1；若两个腐蚀成像结果定位的腐蚀损伤均处于各自传感器阵列的可监测区域内，则设置两个腐蚀成像结果的权值均为0.5。
进一步地，在步骤(7)中，按照下式计算腐蚀因子：
Figure PCTCN2017117066-appb-000014
上式中，λmax为根据权值叠加的散射信号协方差矩阵的大特征值，uA和uS分别是激励信号和腐蚀散射信号的峰值。
进一步地，在步骤(7)中，根据下式计算腐蚀损伤深度：
D＝1.29×CI+0.115(cm)。
采用上述技术方案带来的有益效果：
本发明有效提高了基于MUSIC算法的腐蚀损伤定位精度；能够监测一维线阵监测盲区内的腐蚀损伤，扩大了监测区域；能够评估腐蚀损伤的深度，可有效地应用于航空结构的真实腐蚀损伤监测中。
附图说明
图1为本发明的方法流程图；
图2为实施例中铝板及双阵列布置示意图；
图3为实施例中单激励源下MUSIC腐蚀损伤定位结果图，其中(a)为单激励源下腐蚀损伤散射阵列信号图，(b)为空间谱图；
图4为实施例中基于激励波束成型的MUSIC腐蚀损伤定位结果，其中(a)为聚焦的腐蚀损伤散射阵列信号图；(b)为空间谱图；
图5为实施例中加权融合的空间谱图，其中(a)为阵列S作为激励源阵列时的空间谱图；(b)为加权融合空间谱；
图6为实施例中腐蚀因子与腐蚀深度的关系图。
具体实施方式
以下将结合附图，对本发明的技术方案进行详细说明。
本发明提出了基于激励波束成型和加权图像融合的MUSIC腐蚀监测方法，如图1所示，过程如下。
步骤1：在结构处于健康状态时，轮流驱动阵列A和阵列S激发Lamb波，并采集对应传感器阵列的响应信号，记为基准信号；
步骤2：在腐蚀损伤监测过程中，轮流驱动阵列A和阵列S激发Lamb波， 并采集对应传感器阵列的响应信号，记为监测信号；将监测信号减去基准信号，记为腐蚀损伤的散射信号；
步骤3：基于MUSIC算法和腐蚀损伤的散射信号，得到腐蚀成像结果，搜索腐蚀成像结果的峰值点，即为腐蚀损伤的初估位置；
步骤4：根据此初估位置，计算激励阵元相对腐蚀损伤位置的时延，并根据该时延前移或后移腐蚀损伤的散射信号，叠加这些腐蚀损伤的散射信号获得增强的腐蚀损伤的散射信号；
步骤5：将增强的腐蚀损伤的散射信号再次代入MUSIC算法，得到对应的腐蚀成像结果和散射信号协方差矩阵的大特征值；
步骤6：对阵列A和阵列S分别作为激励源阵列时对应的腐蚀成像结果设置相应的权值，根据权值融合两次腐蚀成像结果，搜索融合成像的峰值点，即为腐蚀损伤位置；
步骤7：根据步骤6得到的权值叠加散射信号协方差矩阵的大特征值，进而计算腐蚀因子，利用腐蚀因子评估腐蚀损伤深度。
下文通过具体实施例来对本发明的技术方案进行进一步说明。
如图2所示，铝板的尺寸为50cm×50cm×0.3cm。结构表面布置了双阵列，由两个一维均匀线阵组成。其中上面的阵列命名为阵列A，对应的阵元命名为PZT A-3,…,PZT A3。下面的阵列命名为阵列S，对应的阵元命名为PZT S-3,…,PZT S3。每个阵列的阵元间距都为1.3cm。双阵列之间的间距为30cm。实验中通过稀盐酸与铝发生化学反应，从而制造腐蚀损伤。腐蚀损伤位置设置在(220cm，120°)，腐蚀直径1.3cm。腐蚀共进行了5个阶段，每次腐蚀阶段后通过超声C扫仪测量腐蚀损伤深度，用来验证本发明中的腐蚀损伤深度评估方法。测得的5个阶段腐蚀损伤深度分别是0.012cm，0.027cm，0.037cm，0.049cm和0.059cm。
1.腐蚀损伤散射阵列信号获取
1.1获取基准信号
当铝板处于健康状态时，按照顺序分别驱动PZT A-3,…,PZT A3激发Lamb波，采集PZT S-3,…,PZT S3接受的Lamb波响应信号。接着按照顺序分别驱动PZTS-3,…,PZT S3激发Lamb波，采集PZT A-3,…,PZT A3接受的Lamb波响应信号。将这些保存为基准信号。
1.2获取腐蚀损伤散射信号
当监测腐蚀损伤时，再次按照顺序分别驱动PZT A-3,…,PZT A3激发Lamb波，采集PZT S-3,…,PZT S3接受的Lamb波响应信号。接着按照顺序分别驱动PZT S-3,…,PZT S3激发Lamb波，采集PZT A-3,…,PZT A3接受的Lamb波响应信号。将这些保存为监测信号。通过监测信号与基准信号作差，获得腐蚀损伤的散射信号。
2.基于阵列A和激励波束成型实现MUSIC腐蚀成像
以阵列A作为激励源阵列，而阵列S作为传感器阵列。
2.1初步估计腐蚀损伤位置
将PZT A0激励，PZT S-3,…,PZT S3响应的散射阵列信号代入MUSIC算法，如图3中的(a)所示。根据Lamb波信号传播的近场模型，散射阵列信号
Figure PCTCN2017117066-appb-000015
可表示为
Figure PCTCN2017117066-appb-000016
式中
Figure PCTCN2017117066-appb-000017
A(r,θ)＝[a-3(r,θ),a-2(r,θ),...,a3(r,θ)]T，N(t)＝[n-3(t),n-2(t),...,n3(t)]T；x-3(t),x-2(t),...,x3(t)为对应PZT S-3，。。。，PZT S3的散射信号，x0(t)为参考阵元的散射信号；N(t)为对应采集通道的背景噪声；A(r,θ)为导向矢量，其计算公式为
aq(r,θ)＝exp(-jω0τq)，q＝-3,-2,...,3            (2)
Figure PCTCN2017117066-appb-000018
上式中，ω0为传播信号的中心频率；τq表示传感器阵列中各阵元相对参考阵元的波达到时间延迟，其中r,θ分别表示损伤相对于传感器阵列的距离和角度，c为信号传播速度，d为阵元间距。
计算
Figure PCTCN2017117066-appb-000019
的协方差矩阵
Figure PCTCN2017117066-appb-000020
即
Figure PCTCN2017117066-appb-000021
上式中，
Figure PCTCN2017117066-appb-000022
为
Figure PCTCN2017117066-appb-000023
的Hermitian转置，K为采集信号长度。
对
Figure PCTCN2017117066-appb-000024
进行特征值分解
Figure PCTCN2017117066-appb-000025
上式中，US、UN分别为信号子空间与噪声子空间，∑S、∑N分别为信号子空间对应的大特征值与噪声子空间对应的小特征值。
基于MUSIC算法的空间谱估计的公式：
Figure PCTCN2017117066-appb-000026
在区域内进行方位角、距离的二维搜索，方位角和距离的搜索步长分别为1度、1mm，根据公式(6)得到二维空间谱的图，如图3中的(b)所示。在空间谱图中存在一个明显的波峰，即表示腐蚀损伤的位置。其中横坐标表示信号源的波达方向，此为腐蚀损伤方向的初步估计θ1。纵坐标表示信号源的距离，此为腐蚀损伤方向的初步估计r1。
2.2激励波束成型
获得腐蚀损伤的初估位置后，可计算激励源阵列中各阵元与该初估位置的距离：
Figure PCTCN2017117066-appb-000027
式中，p表示激励源阵列中的第p个阵元，l为双阵列之间的间距。相对于参考阵元，激励源阵列中各阵元至腐蚀损伤的时延：
Figure PCTCN2017117066-appb-000028
式中，
Figure PCTCN2017117066-appb-000029
为激励源阵列中参考阵元与初估位置的距离。根据计算的时延，前移或后移对应的传感器阵列响应信号，使得激励源阵列各阵元激发的Lamb波虚拟地同时到达腐蚀损伤，即而得到聚焦的腐蚀损伤散射信号
Figure PCTCN2017117066-appb-000030
式中，
Figure PCTCN2017117066-appb-000031
表示激励源阵列中的第p个阵元激励时采集的散射阵列信号。聚焦的腐蚀损伤散射信号如图4中的(a)所示。
2.3腐蚀损伤重估计
将2.2中得到的聚焦腐蚀损伤散射信号代入MUSIC算法，按照2.1中步骤，可重新得到二维空间谱的图，如图4中的(b)所示，可获得腐蚀损伤的准确方向和距离。
3.基于阵列S和激励波束成型实现MUSIC腐蚀成像
以阵列S作为激励源阵列，而阵列A作为传感器阵列。与2中的步骤一致，应用基于激励波束成型的MUSIC算法，同样得到以阵列A为传感器阵列时的腐蚀损伤成像结果，如图5中的(a)所示。
4.加权图像融合
由图4中的(b)和图5中的(a)所示，腐蚀损伤均处于两个阵列的可监测区域。因此，设置前后两次得到的腐蚀损伤成像的权值分别是0.5和0.5。叠加融合之后的成像结果与图5中的(b)相同。通过搜索成像的峰值点，即为腐蚀损伤的准确位置。
5.腐蚀损伤深度评估
按照步骤4中的权值叠加两次大特征值，得到最终的5个腐蚀损伤阶段的大特征值为0.0189，0.1304，0.2285，0.3268和0.4478。本实验中，激励信号峰值为70V，腐蚀损伤散射信号峰值为0.35V。根据腐蚀因子的计算公式，可计算出5个腐蚀阶段的腐蚀因子，如图6所示。结合测量得到的5个阶段腐蚀深度为0.012cm，0.027cm，0.037cm，0.049cm和0.059cm，通过直线拟合可得到腐蚀深度D与腐蚀因子之间的关系：
D＝1.29×CI+0.115(cm)            (10)
根据公式(10)，则可基于腐蚀因子对腐蚀损伤的深度进行评估。
实施例仅为说明本发明的技术思想，不能以此限定本发明的保护范围，凡是按照本发明提出的技术思想，在技术方案基础上所做的任何改动，均落入本发明保护范围之内。

Claims (8)

  1. 基于激励波束成型和加权图像融合的MUSIC腐蚀监测方法，其特征在于，包括以下步骤：
    (1)在结构处于健康状态时，轮流驱动阵列A和阵列S激发Lamb波，并采集对应传感器阵列的响应信号，记为基准信号；
    (2)在腐蚀损伤监测过程中，轮流驱动阵列A和阵列S激发Lamb波，并采集对应传感器阵列的响应信号，记为监测信号；将监测信号减去基准信号，记为腐蚀损伤的散射信号；
    (3)基于MUSIC算法和腐蚀损伤的散射信号，得到腐蚀成像结果，搜索腐蚀成像结果的峰值点，即为腐蚀损伤的初估位置；
    (4)根据此初估位置，计算激励阵元相对腐蚀损伤位置的时延，并根据该时延前移或后移腐蚀损伤的散射信号，叠加这些腐蚀损伤的散射信号获得增强的腐蚀损伤的散射信号；
    (5)将增强的腐蚀损伤的散射信号再次代入MUSIC算法，得到对应的腐蚀成像结果和散射信号协方差矩阵的大特征值；
    (6)对阵列A和阵列S分别作为激励源阵列时对应的腐蚀成像结果设置相应的权值，根据权值融合两次腐蚀成像结果，搜索融合成像的峰值点，即为腐蚀损伤位置；
    (7)根据步骤(6)得到的权值叠加散射信号协方差矩阵的大特征值，进而计算腐蚀因子，利用腐蚀因子评估腐蚀损伤深度。
  2. 根据权利要求1所述MUSIC腐蚀监测方法，其特征在于，所述阵列A和阵列S均为一维均匀线阵，两个阵列均包含2N+1个阵元，阵列A中的各个阵元用Ai表示，阵列S中的各个阵元用Si表示，i＝-N,-(N-1),…,0,…,N-1,N。
  3. 根据权利要求2所述MUSIC腐蚀监测方法，其特征在于，在步骤(2) 中，驱动阵列A激发Lamb波，即依次将阵列A中每个阵元作为激励，得到对应的腐蚀损伤的散射信号：
    Figure PCTCN2017117066-appb-100001
    上式中，
    Figure PCTCN2017117066-appb-100002
    为阵列A中阵元Ai作为激励时，阵列S中所有阵元得到的散射信号组成的向量；N(t)为阵列S中各阵元采集通道的背景噪声组成的向量；xa(t)为阵列S中参考阵元获取的腐蚀损伤的散射信号；A(r,θ)为导向矢量，A(r,θ)中的元素aq(r,θ)＝exp(-jω0τq)，q＝N,-(N-1),...,0,...,N-1,N，ω0为传播信号的中心频率，j为虚数单位，
    Figure PCTCN2017117066-appb-100003
    d为阵列中相邻阵元的间距，c为信号传播速度，r和θ分别为损伤相对于阵列S的距离和角度；t表示信号的时刻；
    驱动阵列S激发Lamb波的过程与阵列A相同。
  4. 根据权利要求3所述MUSIC腐蚀监测方法，其特征在于，在步骤(3)中，首先计算
    Figure PCTCN2017117066-appb-100004
    的协方差矩阵
    Figure PCTCN2017117066-appb-100005
    Figure PCTCN2017117066-appb-100006
    上式中，上标H表示Hermitian转置，K为采集信号长度；
    然后对
    Figure PCTCN2017117066-appb-100007
    进行特征值分解：
    Figure PCTCN2017117066-appb-100008
    上式中，US、UN分别为信号子空间、噪声子空间，∑S、∑N分别为信号子空间对应的大特征值、噪声子空间对应的小特征值；
    基于MUSIC算法的空间谱估计：
    Figure PCTCN2017117066-appb-100009
    在空间谱图中存在一个明显的波峰，即为腐蚀损伤的初估位置。
  5. 根据权利要求4所述MUSIC腐蚀监测方法，其特征在于，在步骤(4)中，计算激励源阵列中各阵元与腐蚀损伤的初估位置的距离：
    Figure PCTCN2017117066-appb-100010
    p＝N,-(N-1),...,0,...,N-1,N
    上式中，l为激励源阵列与传感器阵列之间的间距，r1为腐蚀损伤初估位置的距离信息，θ1为腐蚀损伤初估位置的方向信息；
    计算激励源阵列中各阵元至腐蚀损伤的时延：
    Figure PCTCN2017117066-appb-100011
    上式中，
    Figure PCTCN2017117066-appb-100012
    为激励源阵列中参考阵元与腐蚀损伤的初估位置的距离；
    叠加各腐蚀损伤的散射信号获得增强的腐蚀损伤的散射信号：
    Figure PCTCN2017117066-appb-100013
  6. 根据权利要求1-5中任意一项所述MUSIC腐蚀监测方法，其特征在于，在步骤(6)中，若其中一个腐蚀成像结果定位的腐蚀损伤位于其传感器阵列的监测盲区，则设置该腐蚀成像结果的权值为0，另一腐蚀成像结果的权值为1；若两个腐蚀成像结果定位的腐蚀损伤均处于各自传感器阵列的可监测区域内，则设置两个腐蚀成像结果的权值均为0.5。
  7. 根据权利要求1-5中任意一项所述MUSIC腐蚀监测方法，其特征在于，在步骤(7)中，按照下式计算腐蚀因子：
    Figure PCTCN2017117066-appb-100014
    上式中，λmax为根据权值叠加的散射信号协方差矩阵的大特征值，uA和uS分别是激励信号和腐蚀散射信号的峰值。
  8. 根据权利要求7所述MUSIC腐蚀监测方法，其特征在于，在步骤(7)中，根据下式计算腐蚀损伤深度：
    D＝1.29×CI+0.115(cm)。
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