共查询到18条相似文献，搜索用时 46 毫秒
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高密度聚乙烯管广泛用于燃气输送等领域,其热熔接头的焊接质量直接影响管道系统的安全。采用全聚焦成像方法对高密度聚乙烯试块的横通孔缺陷进行成像实验,通过对检测信号进行信号预处理,提高全聚焦成像的效果,并提出一种基于半稀疏矩阵采集的超声阵列全聚焦成像方式,将该方式与基于全矩阵、半矩阵和稀疏矩阵采集方式的全聚焦成像结果进行比较。实验结果显示,基于半稀疏矩阵采集的全聚焦成像方法可有效降低计算量,提高实时检测效率,同样能够用于高密度聚乙烯材料中横通孔缺陷的重构且成像信噪比无明显降低。 相似文献
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齿轮箱传动结构复杂,其出现故障时的振动信号往往含有强噪声。在强噪声背景下微弱信号的特征提取是振动信号处理领域的难题。稀疏分解方法能够自适应地提取强噪声背景下的微弱信号特征,但其在寻找最优匹配原子时计算量特别大。为加快匹配最优原子的速度,提出利用遗传算法优化匹配追踪的信号稀疏分解算法,优化后的算法大大降低了匹配追踪算法中寻找最优原子参数的计算量。齿轮故障振动信号的主要特征是调制现象,通过稀疏分解对含有噪声的信号进行降噪,然后进行频域分析,根据频域分析结果实现齿轮的故障诊断。对仿真的齿轮调制振动信号和实际采集的齿轮箱振动信号分析表明,该方法能够从含有强噪声的振动信号中快速且准确地提取出故障特征频率。 相似文献
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基于超声相控阵基本理论和全聚焦成像算法(Total Focus Method,TFM),以30 mm厚的Q235钢板中的孔缺陷检测为研究对象,使用ABAQUS有限元软件,建立了相控阵TFM有限元检测模型。根据模拟结果,在MATLAB软件中编写了相控阵TFM成像算法。同时,采用超声多通道实验平台,对构建的TFM有限元检测模型和编写的相控阵TFM算法进行实验验证。实验结果与有限元模拟结果有较好的一致性。 相似文献
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刘易奕;陈尧;李秋锋;王志刚;王海涛 《振动与冲击》2024,(11):279-287
针对采用高频超声水浸法检测装配式钢结构的防护涂层厚度的仿真试验中,防护涂层的界面反射回波相互混叠,导致无法提取涂层的时域信息的问题,利用基于遗传算法优化稀疏分解中的匹配追踪过程对混叠信号进行分离与重构。该算法在构建的Gabor原子库中,利用遗传算法对最佳原子参数的搜索过程进行优化,同时将传统稀疏分解匹配追踪算法中的内积运算优化为互相关运算,从而优化了稀疏分解的运算效率。与金相检测涂层厚度的结果相比较,该改进算法的检测相对误差为2.50%,在可接受的范围内,且较传统稀疏分解匹配追踪算法5.01%的检测相对误差的检测精度高,同时运算速度得到较大提升。 相似文献
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许才彬;左浩;陈一馨 《振动与冲击》2024,(11):50-57+82
针对超声导波缺陷成像中存在的入射信号能量低、缺陷成像分辨率不足等成像质量不高的问题,提出了一种超声导波相控阵脉冲压缩全聚焦缺陷成像方法。首先在长时宽、大带宽线性调频信号激励下,基于超声导波相控阵列逐元激励模式,获取被测结构的全矩阵捕获数据;然后对各响应信号做匹配滤波,对长时宽响应信号波包进行脉冲压缩;接着采用虚拟时间反转法,对脉冲压缩后所得信号进行频散和幅值补偿,以消除大带宽导致的信号相位畸变和因波扩散传播而导致的幅值下降,从而获得无相位畸变的窄时宽信号波包;最后设计了同时包含信号相位和幅值信息的成像指标,进行加权全聚焦成像。在含裂纹、表面缺陷的碳钢板中进行了超声导波缺陷成像试验,结果表明,该方法可以实现单缺陷/双缺陷的高质量成像。 相似文献
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先进树脂基复合材料因其密度低、强度高等特点,广泛应用于航空航天领域。纤维褶皱是先进树脂基复合材料制造过程中产生的一种缺陷,常规超声检测效率低,而阵列超声全聚焦成像检测技术则依赖准确的声传播延时。针对先进树脂基复合材料中的各向异性和多层折射界面而导致声波延时计算困难的问题,提出了一种使用Viterbi搜索算法的声线示踪方法,用于计算阵列超声全聚焦成像检测的时间延迟。对5.92 mm厚的多向碳纤维复合材料层压板进行阵列超声全聚焦成像检测实验,结果表明,使用声线示踪法计算延时,可以使采集的全矩阵信号被准确地相干叠加,有效检测出多向碳纤维复合材料层压板中的纤维褶皱缺陷。 相似文献
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Hao Sun Haim Waisman Raimondo Betti 《International journal for numerical methods in engineering》2013,95(10):871-900
We present a novel algorithm based on the extended finite element method (XFEM) and an enhanced artificial bee colony (EABC) algorithm to detect and quantify multiple flaws in structures. The concept is based on recent work that have shown the excellent synergy between XFEM, used to model the forward problem, and a genetic‐type algorithm to solve an inverse identification problem and converge to the ‘best’ flaw parameters. In this paper, an adaptive algorithm that can detect multiple flaws without any knowledge on the number of flaws beforehand is proposed. The algorithm is based on the introduction of topological variables into the search space, used to adaptively activate/deactivate flaws during run time until convergence is reached. The identification is based on a limited number of strain sensors assumed to be attached to the structure surface boundaries. Each flaw is approximated by a circular void with the following three variables: center coordinates (xc, yc) and radius (rc), within the XFEM framework. In addition, the proposed EABC scheme is improved by a guided‐to‐best solution updating strategy and a local search (LS) operator of the Nelder–Mead simplex type that show fast convergence and superior global/LS abilities compared with the standard ABC or classic genetic algorithms. Several numerical examples, with increasing level of difficulty, are studied in order to evaluate the proposed algorithm. In particular, we consider identification of multiple flaws with unknown a priori information on the number of flaws (which makes the inverse problem harder), the proximity of flaws, flaws having irregular shapes (similar to artificial noise), and the effect of structured/unstructured meshes. The results show that the proposed XFEM–EABC algorithm is able to converge on all test problems and accurately identify flaws. Hence, this methodology is found to be robust and efficient for nondestructive detection and quantification of multiple flaws in structures. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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In this study the layer optimization was carried out for maximizing the lowest (first) fundamental frequency of symmetrical laminated composite plates subjected to any combination of the three classical boundary conditions, and the applicability of the Artificial Bee Colony (ABC) algorithm to the layer optimization was investigated. The finite element method was used for calculating the first natural frequencies of the laminated composite plates with various stacking sequences. The ABC algorithm maximizes the first natural frequency of the laminated composite plate defined as an objective function. The optimal stacking sequences were determined for two layer numbers, twenty boundary conditions and two plate length/width ratios. The outer layers of the composite plate had a stiffness increasing effect, and as the number of clamped plate edges was increased both he stiffness and natural frequency of the plate increased. The optimal stacking sequences were in good agreement with those determined by the Ritz-based layerwise optimization method (Narita 2003: J. Sound Vibration 263 (5), 1005–1016) as well as by the genetic algorithm method combined with the finite element method. 相似文献
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将扩展有限元法与智能优化算法相结合,基于结构的实际响应值反演出结构内部缺陷信息。传统人工蜂群算法在一定程度上朝着任意的方向搜索,为了避免出现搜索的局部最优现象,该文在传统人工蜂群算法中嵌入了加权平均数突变和交叉算子,将这种改进算法用于单个圆形、椭圆形缺陷和两个不规则缺陷的反演分析,并研究了该算法在测得值有误差情况下的适应性。研究得到：这种改进人工蜂群算法能准确反演出结构的真实缺陷信息;改进人工蜂群算法相比于传统人工蜂群算法收敛速度更快且不易出现局部最优,且定位准确,鲁棒性较强。 相似文献
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The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems. 相似文献
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In this article, an innovative artificial bee colony (IABC) algorithm is proposed, which incorporates two mechanisms. On the one hand, to provide the evolutionary process with a higher starting level, genetic programming (GP) is used to generate heuristic rules by exploiting the elements that constitute the problem. On the other hand, to achieve a better balance between exploration and exploitation, a leading mechanism is proposed to attract individuals towards a promising region. To evaluate the performance of IABC in solving practical and complex problems, it is applied to the intercell scheduling problem with limited transportation capacity. It is observed that the GP-generated rules incorporate the elements of the most competing human-designed rules, and they are more effective than the human-designed ones. Regarding the leading mechanism, the strategies of the ageing leader and multiple challengers make the algorithm less likely to be trapped in local optima. 相似文献
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基于桥梁节段模型风洞试验自由振动衰减时程信号,提出了桥梁断面颤振导数识别的人工蜂群算法。基于最小二乘原理,将竖弯和扭转信号的整体残差平方和作为目标函数,使用人工蜂群算法对相关参数进行寻优搜索,识别出桥梁断面的颤振导数。与其他迭代算法相比,人工蜂群算法是受生物启发产生的寻优算法,对初值没有要求,从而避免了迭代初值对识别精度的影响。为考察人工蜂群算法在桥梁断面颤振导数识别中的有效性,进行了理想平板模型仿真以及某大桥节段模型风洞试验,结果表明,桥梁断面颤振导数识别的人工蜂群算法具有较好的稳定性和可靠性。 相似文献
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This study explores the use of teaching-learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms for determining the optimum operating conditions of combined Brayton and inverse Brayton cycles. Maximization of thermal efficiency and specific work of the system are considered as the objective functions and are treated simultaneously for multi-objective optimization. Upper cycle pressure ratio and bottom cycle expansion pressure of the system are considered as design variables for the multi-objective optimization. An application example is presented to demonstrate the effectiveness and accuracy of the proposed algorithms. The results of optimization using the proposed algorithms are validated by comparing with those obtained by using the genetic algorithm (GA) and particle swarm optimization (PSO) on the same example. Improvement in the results is obtained by the proposed algorithms. The results of effect of variation of the algorithm parameters on the convergence and fitness values of the objective functions are reported. 相似文献
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针对二进制人工蜂群算法收敛速度慢、易陷入局部最优的缺点,提出一种改进的二进制人工蜂群算法。新算法对人工蜂群算法中的邻域搜索公式进行了重新设计,并通过Bayes公式来决定食物源的取值概率。将改进后的算法应用于求解多维背包问题,在求解过程中利用贪婪算法对进化过程中的不可行解进行修复,对背包资源利用不足的可行解进行修正。通过对典型多维背包问题的仿真实验,表明了本文算法在解决多维背包问题上的可行性和有效性。 相似文献