CN105184364B - A kind of pulling force compensation method for non-destructive tests - Google Patents
A kind of pulling force compensation method for non-destructive tests Download PDFInfo
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Abstract
本发明公开了一种用于损伤识别的拉力补偿方法,包括以下步骤:(1)将扫频频率及不同的受力状态作为输入层,导纳信息作为输出层,形成RBF神经网络结构;(2)采用包含频率、拉力及导纳的样本数据对该RBF神经网络进行训练;(3)将训练完成后的测试数据的频率及拉力作为输入,通过仿真输出相应的导纳数据,并对实测的导纳数据与仿真所得的导纳数据进行对比,根据RMSD损伤指标衡量补偿的效果;补偿外部拉力对导纳数据的影响;该方法通过一个无损受拉钢梁试验和一个有损受拉钢梁得到验证;通过本发明提供的方法,消除了结构所受拉力对EMI技术损伤识别的影响,具有很好的实用性。
The invention discloses a tension compensation method for damage identification, which comprises the following steps: (1) taking frequency sweep frequency and different stress states as input layer, and admittance information as output layer to form RBF neural network structure; ( 2) Use sample data including frequency, tension and admittance to train the RBF neural network; (3) take the frequency and tension of the test data after the training as input, output the corresponding admittance data through simulation, and compare the measured Comparing the admittance data obtained from the simulation with the admittance data obtained by simulation, the compensation effect is measured according to the RMSD damage index; the influence of external tension on the admittance data is compensated; the method passes a non-destructive tensile steel beam test and a The beam is verified; through the method provided by the invention, the influence of the tensile force on the structure on the damage identification of EMI technology is eliminated, and it has good practicability.
Description
技术领域technical field
本发明属于结构健康监测技术领域,更具体地,涉及一种用于损伤识别的拉力补偿方法。The invention belongs to the technical field of structural health monitoring, and more particularly relates to a tension compensation method for damage identification.
背景技术Background technique
土木工程结构由于连续使用,材料退化、环境影响、地震等造成结构损伤,而绝大部分结构潜在的微小损伤很难通过肉眼识别。结构早期的微小损伤若未及时发现,会引发结构的累计损伤,从而导致结构的突发性失效,及时准确地识别结构早期微弱和潜在的损伤,是结构工程领域的难题;现有的结构健康监测技术有视觉检验法,低频振动技术,静态结构响应技术,以及局部无损检测技术;这些技术的缺陷主要在于:工程结构系统庞大,检测难以到达每个部位,耗时耗力;由于其低频特性对局部损伤无效,且易受环境噪声影响;测量庞大结构的静态特性(如位移、速度、加速度等)不具有可操作性。Civil engineering structures are damaged due to continuous use, material degradation, environmental impact, earthquakes, etc., and the potential micro damages of most structures are difficult to identify with the naked eye. If the small damage in the early stage of the structure is not found in time, it will cause the cumulative damage of the structure, which will lead to the sudden failure of the structure. It is a difficult problem in the field of structural engineering to identify the weak and potential damage in the early stage of the structure in time and accurately; the existing structure is healthy Monitoring technologies include visual inspection method, low-frequency vibration technology, static structural response technology, and local non-destructive testing technology; the defects of these technologies mainly lie in: the engineering structure system is huge, and it is difficult to reach every part of the inspection, which is time-consuming and labor-intensive; due to its low-frequency characteristics It is ineffective for local damage and is susceptible to environmental noise; it is not operable to measure the static characteristics (such as displacement, velocity, acceleration, etc.) of huge structures.
基于PZT的压电阻抗(EMI)技术主要基于局部高频激励,利用PZT同时作为传感器和驱动器,对结构局部激励获取结构性能变化的信息,从而实现对结构微小损伤的识别;其基本原理是利用高强粘结剂将PZT粘贴结构表面或植入结构内部,利用PZT正逆压电效应,通过压电阻抗仪施加电压对结构局部激振,获得与结构性能(质量,刚度,阻尼等)相关的监测信号,此信号作为结构健康衡量的基准,将来通过观察信号的改变来识别结构是否发生损伤。由于其高频特性,具有对结构微小损伤敏感,且能避开环境噪声影响的优点;但该技术仍然局限于试验件,未有计入外部拉力对工件的影响。PZT-based piezoelectric impedance (EMI) technology is mainly based on local high-frequency excitation, using PZT as a sensor and driver at the same time, to obtain information on structural performance changes for local excitation of the structure, so as to realize the identification of small damage to the structure; the basic principle is to use The high-strength adhesive pastes PZT on the surface of the structure or implants it inside the structure. Using the positive and negative piezoelectric effect of PZT, the voltage is applied to the structure through the piezoelectric impedance meter to locally excite the structure, and the structure performance (mass, stiffness, damping, etc.) is obtained. Monitoring signal, which is used as a benchmark for structural health measurement, will identify whether the structure is damaged by observing the change of the signal in the future. Due to its high-frequency characteristics, it has the advantages of being sensitive to small structural damage and avoiding the influence of environmental noise; however, this technology is still limited to the test piece and does not take into account the influence of external tension on the workpiece.
而实际工况下,工程结构始终处于受力状态;且在工程结构的整个服役过程中,所承受的荷载是不断变化的;当结构发生损伤时局部应力会发生很大变化;EMI技术通过测量导纳信号并通过判别导纳曲线的偏移实现结构损伤识别;试验表明,结构在受拉力的状态下的压电阻抗导纳信号与不受拉力状态的不同,结构受拉会引起导纳曲线偏移,从而影响EMI技术对结构损伤的识别的准确度;因此,排除拉力变化对工程结构损伤识别的干扰,建立有效的拉力补偿方法有待解决。In actual working conditions, the engineering structure is always under stress; and during the entire service process of the engineering structure, the load it bears is constantly changing; when the structure is damaged, the local stress will change greatly; EMI technology can measure The admittance signal and realize structural damage identification by judging the offset of the admittance curve; the test shows that the piezoelectric impedance admittance signal of the structure under the state of tension is different from that of the state without tension, and the tension of the structure will cause the admittance curve Therefore, the establishment of an effective tension compensation method needs to be solved to eliminate the interference of tension changes on the identification of engineering structure damage.
发明内容Contents of the invention
针对现有技术的以上缺陷或改进需求,本发明提供了一种用于损伤识别的拉力补偿方法,其目的在于通过采用包含频率、拉力及导纳的样本数据对RBF神经网络进行训练,实现对结构所受拉力的补偿,为结构精确的损伤识别排除干扰。In view of the above defects or improvement needs of the prior art, the present invention provides a tension compensation method for damage identification, the purpose of which is to train the RBF neural network by using sample data including frequency, tension and admittance to realize the The compensation of the tensile force on the structure eliminates interference for the accurate damage identification of the structure.
为实现上述目的,按照本发明的一个方面,提供了一种用于损伤识别的拉力补偿方法,该拉力补偿方法基于RBF(多变量插值的径向基函数,Radical Basis Function)神经网络具体包括如下步骤:In order to achieve the above object, according to one aspect of the present invention, a kind of tension compensation method for damage identification is provided, the tension compensation method is based on RBF (radial basis function of multivariable interpolation, Radical Basis Function) neural network specifically includes as follows step:
(1)将扫频频率及拉力作为输入层,以导纳值作为输出层,与隐含层一起构成RBF神经网络;(1) The sweep frequency and pulling force are used as the input layer, the admittance value is used as the output layer, and the RBF neural network is formed together with the hidden layer;
(2)采用包含扫频频率、拉力及导纳值的样本数据对所述RBF神经网络进行训练,直到输出导纳值与相应样本数据的导纳值之间的差值在样本数据导纳值的±5%以内,结束RBF神经网络训练;(2) adopt the sample data that comprises sweeping frequency, pulling force and admittance value to train described RBF neural network, until the difference between output admittance value and the admittance value of corresponding sample data is within sample data admittance value Within ±5% of , end RBF neural network training;
(3)将测试数据的扫频频率及拉力作为输入,采用步骤(2)训练获得的RBF神经网络,获取与所述扫频频率及拉力相应的导纳值;所述导纳值包含了外部拉力信息,补偿了拉力对导纳数据的影响;(3) With the sweep frequency and pulling force of test data as input, adopt the RBF neural network that step (2) training obtains, obtain the admittance value corresponding with described sweep frequency and pulling force; Described admittance value has included external Pull force information, which compensates the influence of pull force on admittance data;
(4)根据步骤(3)获得的导纳值,获取补偿后的RMSD损伤指标;(4) Obtain the compensated RMSD damage index according to the admittance value obtained in step (3);
上述用于损伤识别的拉力补偿方法先采用包含扫频频率、拉力及导纳值的样本数据对RBF神经网络进行训练,训练完成后将测试数据的扫频频率及拉力作为RBF神经网络的输入,获取相应输出的导纳值,该导纳值包含了拉力信息,补偿了拉力对导纳数据的影响。The above tension compensation method for damage identification first uses the sample data including sweep frequency, tension and admittance value to train the RBF neural network, and after the training is completed, the frequency sweep frequency and tension of the test data are used as the input of the RBF neural network. Obtain the admittance value of the corresponding output, the admittance value contains the pulling force information, and compensates the influence of the pulling force on the admittance data.
优选的,在步骤(2)所述的RBF神经网络训练中,根据以下方法确定隐含层神经元的数量:Preferably, in the RBF neural network training described in step (2), determine the quantity of hidden layer neurons according to the following method:
a、将RBF神经网络产生的均方误差(MSE)所对应的输入向量作为权值向量,生成一个新的隐含层神经元;a. The input vector corresponding to the mean square error (MSE) generated by the RBF neural network is used as a weight vector to generate a new hidden layer neuron;
b、以测试所得导纳值作为输入,以仿真所得的导纳值作为输出,与步骤a生成的新的隐含层神经元,构成新的RBF神经网络;b. Taking the admittance value obtained from the test as an input, and taking the admittance value obtained from the simulation as an output, and the new hidden layer neurons generated in step a to form a new RBF neural network;
c、获取步骤b所述新的RBF神经网络的均方误差;c, obtaining the mean square error of the new RBF neural network described in step b;
d、重复步骤a~c,直到RBF神经网络的均方误差达到预设均方误差,以此时的隐含层神经元数量作为RBF神经网络隐含层神经元的数量;其中,预设均方误差在5%~10%;d. Repeat steps a to c until the mean square error of the RBF neural network reaches the preset mean square error, and the number of neurons in the hidden layer at this time is used as the number of neurons in the hidden layer of the RBF neural network; The square error is between 5% and 10%;
在RBF神经网络训练中,现有技术是使隐含层神经元的数量与输入向量的元素相等;但在输入向量很多时,过多的隐含层单元数会增加计算的复杂度;采用本发明提供的上述方法,逐步趋近的达到隐含层神经元数量的最佳值,在最小的计算量内即可获得最佳补偿效果。In RBF neural network training, the existing technology is to make the number of hidden layer neurons equal to the elements of the input vector; but when there are many input vectors, too many hidden layer units will increase the computational complexity; using this The above-mentioned method provided by the invention gradually approaches the optimal value of the number of neurons in the hidden layer, and can obtain the optimal compensation effect within the minimum amount of calculation.
优选地,步骤(2)所述的对RBF神经网络进行训练的步骤,包括如下子步骤:Preferably, the step of training the RBF neural network described in step (2) includes the following sub-steps:
(2.1)给定扫频频率、拉力和期望输出的导纳值;(2.1) The admittance value of the given sweep frequency, pulling force and expected output;
(2.2)将上述扫频频率和拉力作为输入向量,经步骤(1)获得的RBF神经网络处理,获取导纳值;(2.2) the above-mentioned sweep frequency and pulling force are used as input vectors, and the RBF neural network obtained through step (1) is processed to obtain the admittance value;
(2.3)将步骤(2.2)中获取的导纳值与所述期望输出的导纳值做差,获得差值;判断所述差值是否在期望输出导纳值的±5%以内;若是,则结束训练;若否,则增加神经元,并返回步骤(2.2)。(2.3) making a difference between the admittance value obtained in step (2.2) and the admittance value of the desired output to obtain a difference; judge whether the difference is within ±5% of the desired output admittance value; if so, Then end the training; if not, add neurons and return to step (2.2).
优选的,在上述步骤(2.3)结束对RBF神经网络的训练后,采用如下步骤确定RBF神经网络训练效果,具体为:Preferably, after the above-mentioned steps (2.3) finish the training to the RBF neural network, adopt the following steps to determine the RBF neural network training effect, specifically:
(2.4)将不同于训练样本的数据作为输入向量,用训练好的RBF神经网络进行仿真,获取仿真输出的导纳值;(2.4) Using the data different from the training sample as the input vector, simulate with the trained RBF neural network, and obtain the admittance value of the simulation output;
(2.5)根据步骤(2.4)里仿真输出的导纳值获取RMSD值,若在各拉力工况下获得的RMSD值在5%以内波动,则进入步骤(3);否则,返回步骤(2.1),采用新的样本数据重新训练RBF神经网络;(2.5) Obtain the RMSD value according to the admittance value output by the simulation in step (2.4). If the RMSD value obtained under each tension condition fluctuates within 5%, go to step (3); otherwise, return to step (2.1) , using new sample data to retrain the RBF neural network;
其中,RMSD(root mean-square deviation)是对结构在不同状态下测量的信号的均方根指数,根据该统计指标可衡量导纳信号变化前后PZT(piezoelectric ceramics,压电陶瓷)传感器电信号的变化程度,根据下式获取:Among them, RMSD (root mean-square deviation) is the root mean square index of the signal measured under different states of the structure. According to this statistical index, the electrical signal of the PZT (piezoelectric ceramics, piezoelectric ceramics) sensor before and after the change of the admittance signal can be measured. The degree of change is obtained according to the following formula:
其中,n是指采样频率数,i是指每个频率采样点的导纳值,xi表示结构损伤前测得的阻抗值;yi表示结构损伤后测得的阻抗值;其中,i=1,2,3,…,n。Among them, n refers to the number of sampling frequencies, i refers to the admittance value of each frequency sampling point, xi represents the impedance value measured before the structure damage; y i represents the impedance value measured after the structure damage; where, i= 1, 2, 3, ..., n.
总体而言,通过本发明所构思的以上技术方案与现有技术相比,能够取得下列有益效果:Generally speaking, compared with the prior art, the above technical solutions conceived by the present invention can achieve the following beneficial effects:
(1)本发明提供的用于损伤识别的拉力补偿方法,采用RBF神经网络对拉力的影响进行补偿;将扫频频率及不同的受力状态作为输入层,导纳信息作为输出层;神经网络在输入层和输出层之间形成特定的映射关系;通过训练,利用已经形成的神经网络进行仿真;在给定某一个输入后,通过神经网络仿真出相应的输出;从而达到消除拉力对导纳值的影响,降低拉力对压电阻抗技术的干扰。(1) The tension compensation method for damage identification provided by the present invention adopts RBF neural network to compensate the impact of tension; frequency sweep frequency and different stressed states are used as input layer, and admittance information is used as output layer; neural network A specific mapping relationship is formed between the input layer and the output layer; through training, the neural network that has been formed is used for simulation; after a certain input is given, the corresponding output is simulated through the neural network; thereby eliminating the pull force on the admittance The influence of the value can reduce the interference of the pulling force on the piezoresistive impedance technology.
(2)本发明提供的拉力补偿方法,其优选方案里,还包括分析网络训练效果是否合理的步骤,具有可不断调节仿真数据的灵活性,直到达到符合实际工况的补偿效果。(2) The tension compensation method provided by the present invention, in its preferred solution, also includes the step of analyzing whether the network training effect is reasonable, and has the flexibility to continuously adjust the simulation data until the compensation effect in line with the actual working conditions is achieved.
附图说明Description of drawings
图1是RBF神经网络示意图;Fig. 1 is the schematic diagram of RBF neural network;
图2是RBF神经网络训练流程示意图;Fig. 2 is a schematic diagram of the RBF neural network training process;
图3是测量试件一的PZT2各拉力工况下导纳曲线图及局部放大图;Figure 3 is the admittance curve and partial enlarged view of PZT2 under various tension conditions of the test piece 1;
图4是测量试件二的PZT3各拉力工况下导纳曲线图及局部放大图;Figure 4 is the admittance curve and partial enlarged view of PZT3 under various tension conditions of the test piece 2;
图5是测量试件一的PZT2RBF仿真与实测导纳信息对比图;Fig. 5 is a comparison chart of PZT2RBF simulation and measured admittance information of test piece 1;
图6是测量试件二的PZT3RBF仿真与实测导纳信息对比图;Fig. 6 is a comparison chart of the PZT3RBF simulation and measured admittance information of the second test piece;
图7是试件一的PZT2导纳经RBF补偿前后RMSD指标对比图;Figure 7 is a comparison chart of the RMSD index of PZT2 admittance of specimen 1 before and after RBF compensation;
图8是试件二无损时PZT3导纳经RBF补偿前后RMSD指标对比图;Figure 8 is a comparison chart of the RMSD index of the PZT3 admittance before and after RBF compensation when the second specimen is undamaged;
图9是试件二有损伤时PZT3导纳经RBF补偿前后RMSD指标对比图。Figure 9 is a comparison chart of the RMSD index of the PZT3 admittance before and after RBF compensation when the second specimen is damaged.
具体实施方式detailed description
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅仅用以解释本发明,并不用于限定本发明。此外,下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.
本发明提供的用于损伤识别的拉力补偿方法,用于结构受拉力影响时的损伤检测,基于一种三层前向型RBF神经网络,其第一层为输入层,由信号源节点组成,第二层为隐含层,第三层为输出层;隐含层的神经元数量根据描述问题的需要确定,隐含层的变换函数是中心点径向对称且衰减的非负非线性函数;输入层到隐含层控件的变换是非线性的,而从隐含层控件到输出层的变化是线性的;The tension compensation method for damage identification provided by the present invention is used for damage detection when the structure is affected by tension. It is based on a three-layer forward RBF neural network. The first layer is the input layer, which is composed of signal source nodes. The second layer is the hidden layer, and the third layer is the output layer; the number of neurons in the hidden layer is determined according to the needs of describing the problem, and the transformation function of the hidden layer is a non-negative nonlinear function with radial symmetry and attenuation at the center point; The transformation from the input layer to the hidden layer control is nonlinear, while the change from the hidden layer control to the output layer is linear;
用RBF作为隐含层的“基”构成隐含层控件,将输出向量直接(不需要通过权连接)映射到隐空间;而隐含层空间到输出空间的映射关系是线性的,网络的输出是隐单元输出的线性加权和;此处的权即为网络可调参数从总体上看,网络由输入到输出的映射是非线性的,而网络输出对可调参数是线性的;网络的权根据线性方程获取,从而大大提供了RBF神经网络的学习速度;实施例中RBF神经网络的径向基函数采用高斯函数;实施例1其采用的RBF神经网络结构如图1所示意的,以扫频频率和拉力作为输入层,输出导纳数据,其隐含层的神经元数量根据RBF神经网络的均方误差与预设误差的趋近程度来确定。Use RBF as the "base" of the hidden layer to form the hidden layer control, and map the output vector directly (without connecting through weights) to the hidden space; while the mapping relationship between the hidden layer space and the output space is linear, the output of the network is the linear weighted sum of the hidden unit output; the weight here is the adjustable parameter of the network. On the whole, the mapping of the network from input to output is nonlinear, while the output of the network is linear to the adjustable parameter; the weight of the network is based on Linear equation obtains, thus greatly provided the learning speed of RBF neural network; The radial basis function of RBF neural network adopts Gaussian function in the embodiment; The RBF neural network structure that it adopts of embodiment 1 as shown in Figure 1 schematically, with frequency sweep The frequency and tension are used as the input layer to output the admittance data, and the number of neurons in the hidden layer is determined according to the degree of approach between the mean square error of the RBF neural network and the preset error.
图2所示,是RBF神经网络训练流程示意图,该流程包括以下步骤:As shown in Figure 2, it is a schematic diagram of the RBF neural network training process, which includes the following steps:
(2.1)给定扫频频率和拉力作为输入向量,给定期望输出的导纳;(2.1) Given sweep frequency and pulling force as input vector, given admittance of desired output;
(2.2)获取隐含层、输出层各单元输出;(2.2) Obtain the output of each unit of the hidden layer and the output layer;
(2.3)获取输出误差;判断输出误差是否满足预定目标的要求,若是,则结束训练;若否,则增加神经元,并返回步骤(2.3)。(2.3) Obtain the output error; judge whether the output error meets the requirements of the predetermined target, if so, end the training; if not, increase the neurons, and return to step (2.3).
实施例中,试件1是一根Q235钢梁,钢梁尺寸为500mm*35mm*4mm,无损伤;试件2是Q235钢梁,钢梁尺寸为500mm*35mm*4mm,具有8mm长的裂缝损伤;采用拉伸仪器对钢梁试件1和试件2施加拉力,并利用阻抗分析仪测试PZT在不同拉力工况下的导纳信号。In the embodiment, test piece 1 is a Q235 steel beam with a size of 500mm*35mm*4mm and no damage; test piece 2 is a Q235 steel beam with a size of 500mm*35mm*4mm and a crack of 8mm long Damage: Tensile force is applied to steel beam specimen 1 and specimen 2 with a tensile instrument, and the admittance signal of PZT under different tension conditions is tested with an impedance analyzer.
采用本发明提供的用于损伤识别的拉力补偿方法,对试件1和试件2进行拉伸测试;图3所示,是试件1的PZT2各拉力工况0kN,2kN,4kN,6kN,8kN下测量的导纳曲线图及局部放大图;Adopt the tension compensation method that is used for damage identification provided by the present invention, carry out tension test to test piece 1 and test piece 2; As shown in Figure 3, be the PZT2 each tension working conditions of test piece 1 0kN, 2kN, 4kN, 6kN, Admittance curve and local enlarged diagram measured at 8kN;
图4所示,是试件2的PZT3各拉力工况0kN,4kN,8kN,12kN,16kN下测量的导纳曲线图及局部放大图;As shown in Figure 4, it is the admittance curve and partial enlarged view measured under the tension conditions of PZT3 of test piece 2: 0kN, 4kN, 8kN, 12kN, 16kN;
从图3可以看出,试件1在300kHz到400kHz频率段测得的导纳曲线随着拉力逐渐增大呈现向下的趋势,尤其在导纳曲线的峰值处更为明显。It can be seen from Figure 3 that the admittance curve of specimen 1 measured in the frequency range from 300kHz to 400kHz shows a downward trend as the tension gradually increases, especially at the peak of the admittance curve.
将测量的导纳信号的扫频频率及不同的受力状态作为输入层,导纳信息作为输出层,形成RBF神经网络结构;隐含层神经元数量为10个。The frequency sweep frequency of the measured admittance signal and different stress states are used as the input layer, and the admittance information is used as the output layer to form a RBF neural network structure; the number of neurons in the hidden layer is 10.
将试件1中PZT2和试件2中PZT3测得的数据的1/2当作训练样本,1/2当作测试样本;试件1中2号压电片经过RBF神经网络仿真后不同拉力工况下的导纳信息与实测的导纳信息对比;图5所示,是试件1PZT2在拉力值为0kN,2kN,4kN,6kN拉力工况下的数据对比;图6所示,是试件2PZT3在拉力值为0kN,4kN,8kN,12kN拉力工况下的数据对比,表明在不同拉力工况下仿真导纳数据与实测导纳值基本吻合;Take 1/2 of the data measured by PZT2 in Specimen 1 and PZT3 in Specimen 2 as training samples, and 1/2 as test samples; The admittance information under the working condition is compared with the actual measured admittance information; as shown in Figure 5, it is the data comparison of the test piece 1PZT2 under the tensile force values of 0kN, 2kN, 4kN, and 6kN; as shown in Figure 6, it is the test The data comparison of piece 2PZT3 under the tension values of 0kN, 4kN, 8kN, and 12kN tension conditions shows that the simulated admittance data under different tension conditions are basically consistent with the measured admittance values;
可以看出,经过不同拉力工况的数据训练后,RBF神经网络能够仿真出不同拉力工况下的导纳数据;与实测数据对比可以看出仿真的导纳数据与实测的导纳数据基本吻合,能够很好预测出健康状态下不同拉力工况下的导纳数据。It can be seen that after data training under different tension conditions, the RBF neural network can simulate the admittance data under different tension conditions; compared with the measured data, it can be seen that the simulated admittance data is basically consistent with the measured admittance data , which can well predict the admittance data under different tension conditions in a healthy state.
再用实测的导纳数据与预测的导纳数据作对比分析,判断预测的导纳数据与实测数据的差值是否满足预设的差值期望,预设的差值期望为5%~10%,实施例中为6%,以补偿拉力对导纳数据的影响。Then use the measured admittance data and the predicted admittance data for comparative analysis, and judge whether the difference between the predicted admittance data and the measured data meets the preset difference expectation, and the preset difference expectation is 5% to 10%. , it is 6% in the embodiment to compensate the influence of tension on the admittance data.
定量分析试件在无损伤和有裂纹损伤情况下受拉的RBF补偿效果;图7表示在无损状态下,经过RBF补偿后计算所得试件1的2号压电片补偿后的RMSD损伤指标在不同拉力工况下均在0.1左右,相对于原始数据的RMSD指标基本趋于稳定;Quantitative analysis of the RBF compensation effect of the specimen under tension without damage and with crack damage; Fig. 7 shows the RMSD damage index of No. Under different tension conditions, they are all around 0.1, and the RMSD index relative to the original data is basically stable;
由此确定,试件1的健康状态未发生改变,即无损伤发生;原始数据中因为拉力影响对结构健康状态的误判经过RBF补偿后得到修正。Therefore, it can be determined that the health state of specimen 1 has not changed, that is, no damage has occurred; the misjudgment of the structural health state due to the influence of tension in the original data has been corrected after RBF compensation.
图8表示试件2的3号压电片在无损状态下的RBF补偿前后RMSD指标对比图;在试件2发生损伤后,如图9所示,损伤指标相比无损状态补偿后的RMSD指标从0.26变为0.44,明显增大,表明结构发生损伤;Figure 8 shows the comparison chart of the RMSD index before and after RBF compensation of the No. 3 piezoelectric film of specimen 2 in the non-destructive state; after the specimen 2 is damaged, as shown in Figure 9, the damage index is compared with the RMSD index after compensation in the non-destructive state From 0.26 to 0.44, it increases obviously, indicating that the structure is damaged;
随着拉力的增大,RMSD趋于稳定没有明显变化,表明结构损伤程度没有变化;由此可以看出补偿后对结构的健康状态的判断以及损伤程度的判断得到了修正,对EMI损伤检测方法的拉力补偿是有效的;采用本发明提供的方法,可以对结构所受拉力进行补偿后再识别结构是否损伤,排除了结构所受拉力对损伤识别准确性的影响。As the tension increases, the RMSD tends to be stable without significant changes, indicating that the degree of structural damage has not changed; it can be seen that the judgment of the health status of the structure and the degree of damage after compensation have been corrected, and the EMI damage detection method The tension compensation is effective; the method provided by the invention can compensate the tension on the structure and then identify whether the structure is damaged, eliminating the influence of the tension on the accuracy of damage identification.
本领域的技术人员容易理解,以上所述仅为本发明的较佳实施例而已,并不用以限制本发明,凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明的保护范围之内。It is easy for those skilled in the art to understand that the above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, All should be included within the protection scope of the present invention.
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