基于半稀疏矩阵的聚乙烯管超声阵列全聚焦成像实验分析

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。     

基于全聚焦算法的混凝土超声阵列探测成像方法

混凝土结构内含有大量的砂石、骨料以及钢筋,形成多样性的声学界面,超声在混凝土结构中的信号成分复杂,且超声能量易衰减.为了提高混凝土检测分辨率与信噪比,提出基于全聚焦算法的混凝土超声阵列检测方法.首先通过建立混凝土有限元模型,开展混凝土超声阵列仿真研究,模拟一发多收方式依次激励阵列探头,并获得检测信号;然后,为了避免噪声...     

基于全聚焦算法的骨折超声成像研究

为研究便捷、安全、无电离辐射的骨折检测方法,提出了超声双层折射修正全聚焦算法.首先从信号处理入手,通过回波信号与标准发射信号进行互相关计算,得出噪声含量极低的回波信号.继而利用Filed-Ⅱ裂纹仿真实验验证全聚焦成像对均匀介质检测的可行性,仿真结果显示裂纹宽度平均相对误差为5.60％;最后对离体牛胫骨进行全矩阵数据采集...     

遗传算法优化稀疏分解的齿轮箱故障诊断研究

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。     

相控阵超声全聚焦成像算法的有限元仿真研究

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。     

基于IHT算法的EMT金属探伤稀疏成像方法

为提高电磁层析成像(electromagnetic?tomography,EMT)技术在金属结构缺陷检测时图像重建的效果,研究了基于迭代硬阈值(iterative?hard?thresholding,?IHT)算法的稀疏成像方法.该文对传统图像重建算法的出发点、计算过程进行分析,再根据金属结构上缺陷分布的稀疏特性,选择...     

基于位相相异方法的稀疏孔径系统图像复原

稀疏孔径成像系统的图像复原是稀疏孔径成像技术的一个关键环节.利用基于离焦的位相相异方法对同时存在子镜活塞误差和倾斜误差的Golay3结构类型稀疏孔径系统产生的退化图像进行复原研究,并采用改进的自适应遗传算法进行数值求解,同时对高斯噪声污染的图像进行复原.仿真实验结果表明,利用位相相异方法,对存在子镜配准误差的Golay3结构类型稀疏孔径系统进行图像复原,能取得非常好的效果,同时能够反演出较为精确的系统像差.     

基于人工蜂群优化的印刷色彩分色

分析了2种原色油墨叠印后的颜色光谱,并在此基础上建立了印刷中黄、品、青、黑四原色的网点面积率和相互叠印之后的光谱反射率之间的关系模型,结合人工蜂群算法具有良好寻优能力的特点,提出了人工蜂群优化的印刷色彩分色模型。以标准色谱作为测试样本进行了仿真实验,结果表明该模型具有较高的分色精度。     

先进树脂基复合材料纤维褶皱缺陷阵列超声全聚焦成像

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。     

基于遗传算法的数控伺服系统优化设计方法的研究

文章讨论了以数控机床的全数字控制伺服系统作为设计平台,采用优化设计方法中的遗传算法建立相应误差的数学模型,以减小误差为主要目的实现更精确的伺服控制,在原有的伺服控制系统基础上引入遗传算法进行优化设计。     

Nondestructive identification of multiple flaws using XFEM and a topologically adapting artificial bee colony algorithm

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 (x_c, y_c) and radius (r_c), 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.     

A new improved artificial bee colony algorithm for ship hull form optimization

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.     

A discrete artificial bee colony algorithm for single machine scheduling problems

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.     

An innovative artificial bee colony algorithm and its application to a practical intercell scheduling problem

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.     

Improved artificial bee colony algorithm based on escaped foraging strategy

This paper presents an improved artificial bee colony algorithm. Under the framework of the basic artificial bee colony algorithm, this paper redefines the artificial bee colony and introduces search strategies for group escape and foraging based on Levy flight. The proposed algorithm is named artificial bee colony algorithm based on escaped foraging strategy (EFSABC).There are different strategies for scout bees, onlookers, and free bees searching for honey sources in the EFSABC: all working bees relinquish old honey sources due to disturbance, and select different routines to seek new honey sources. Sixteen typical high-dimensional standard functions are used to verify the effectiveness of the proposed algorithm. The EFSABC algorithm outperforms the traditional artificial bee colony algorithm in all aspects.     

Inverse analysis of geomechanical parameters by the artificial bee colony algorithm and multi-output support vector machine

Geomechanical parameters play a very important role in geotechnical engineering design and construction. Inverse analysis provides a powerful tool to characterize geomechanical parameters based on the behaviour of rock under certain boundary conditions. In this paper, a new method for inverse analysis that takes advantage of multi-output support vector machine (MSVM) and artificial bee colony (ABC) is proposed and applied to solving a real field problem. MSVM is used to map the relationship between geomechanical parameters and displacements. ABC is adopted in inverse analysis to find the optimal geomechanical parameters. The proposed method is used to recognize the parameters of the permanent shiplock slope of the Three Gorges in China. Results show that the proposed method can effectively determine geomechanical parameters.     

Improved artificial bee colony algorithm for crack identification in beam using natural frequencies only

An improved artificial bee colony algorithm (I-ABC) is proposed for crack identification in beam structures. ABC is a heuristic algorithm and swarm technique with simple structure, which is easy to implement but with slow convergence rate. In the I-ABC, the differential evolution (DE) mechanism is introduced to employed bee phase, roulette selection strategy is replaced by tournament selection strategy and a new formula is used to simulate onlooker bee’s behaviour. A discrete open crack is used for vibration analysis of the cracked beam and only the changes in the first few natural frequencies are utilized to establish the objective function of the optimization problem for crack identification. A numerical simulation and an experimental work are studied to illustrate the efficiency of the proposed method. Studies show that the present techniques can produce more accurate damage identification results when compared with original ABC, DE algorithm, particle swarm optimization and genetic algorithm.     

An efficient hybrid artificial bee colony algorithm for disassembly line balancing problem with sequence-dependent part removal times

This study deals with a sequence-dependent disassembly line balancing problem by considering the interactions among disassembly tasks, and a multi-objective mathematical model is established. Subsequently, a novel hybrid artificial bee colony algorithm is proposed to solve the problem. A new rule is used to initialize a bee colony population with certain diversity, and a dynamic neighbourhood search method is introduced to guide the employed/onlooker bees to promising regions. To rapidly leave the local optima, a global learning strategy is employed to explore higher quality solutions. In addition, a multi-stage evaluation method is designed for onlookers to effectively select employed bees to follow. The performance of the proposed algorithm is tested on a set of benchmark instances and two case scenarios, and the results are compared with several other metaheuristics in terms of solution quality and computation time. The comparisons demonstrate that the proposed algorithm exhibits superior performance.