基于L-M算法的雷达板级电路快速故障诊断

新型雷达可更换的板级电路很多,为了快速诊断板级电路故障,将一种基于L-M算法的神经网络用于故障诊断;分析了L-M算法BP网络进行雷达板级电路故障诊断的原理;建立了故障诊断样本训练和测试平台;并以实例在该平台上对L-M算法BP网络进行训练和实际诊断,结果表明,该方法诊断准确性高,比其它方法更为快速有效,较好地解决了雷达可更换板级电路的故障诊断问题。     

改进多重最小支持度关联规则算法在故障诊断中的应用

Apriori算法的前提是数据库中各项目的频率和重要性是相同或者相似的,但在故障诊断的实际应用中并非如此。在Apriori算法的基础上进行改进,利用多重最小支持度解决了设备故障诊断中非频繁项目的挖掘;同时针对在实际的应用中项目集的重要程度不一致的问题,提出一种基于"组件信誉值"的加权多重最小支持度算法,并通过实际的例子证明了该算法在故障诊断中的正确性和有效性。     

基于随机RF的集成SVM故障诊断改进算法

针对工业系统监控数据不均衡导致的故障状态难以被识别问题,提出一种基于随机旋转森林的集成支持向量机(RRFESVM)故障诊断算法,通过将监控数据进行属性随机分割、组合、PCA变换和样本有放回重采样,组建多个新训练子集并使用支持向量机算法进行训练,得到多个支持向量机故障诊断基分类器,集成得到强分类器,由此既保证基分类器之间的差异性,又保证故障诊断精度和分类器性能稳定性,从而解决故障诊断易偏置问题,提高作为少数类的故障状态实时诊断准确率。亚轨道飞行器再入过程实验与TE化工过程实验都表明RRFESVM故障诊断算法能够有效提升不均衡数据情况下的实时故障诊断性能。     

MMAS与粗糙集在齿轮箱故障诊断中的应用

粗糙集理论是一种新的处理含糊和不确定性知识的数学工具,属性约简是粗集理论研究的重要内容,属性约简算法有很多种,而计算一个最佳约简是NP难问题。为了能够有效地获取信息系统的约简,提出了一种新的约简算法。该算法选择最大-最小蚂蚁系统(MMAS),以Fisher准则作为启发式信息来提高搜索效率,将蚁群优化算法引入属性约简中,利用粗糙集理论对故障诊断决策表进行约简,形成清晰、简明的故障诊断规则,为下一步的故障诊断打下了坚实的基础。     

基于天牛须搜索优化支持向量机液压泵故障诊断

为准确地对液压泵的典型故障进行诊断，同时针对支持向量机（Support Vector Machine，SVM）在故障诊断中的参数选择问题，将天牛须搜索算法（Beetle Antennae Search，BAS）应用到SVM的参数优化中，建立BAS-SVM液压泵故障诊断模型，以此来提高SVM在液压泵故障诊断中的性能。液压泵故障诊断实例的结果表明，相比于遗传算法、粒子群算法、果蝇算法等算法的改进算法，BAS得到的SVM参数更优，使SVM获得更高的诊断精度，从而验证了所提方法的有效性。     

基于LS-SVM ARX模型的除湿机故障诊断

针对非线性系统故障诊断问题相对复杂的情况,以除湿机作为研究对象,应用ARX模型进行了研究。引入LS-SVM优化算法对ARX模型进行了改进,克服了传统SVM算法的不足。结合实验采集到的实际数据样本,对模型进行了训练。结果表明,改进的算法具有较低的运算复杂度和较快的学习训练速度,对于新的样本输入能够正确分析诊断。由此说明,将LS-SVM ARX模型应用于故障诊断是可行的。     

Xgboost在滚动轴承故障诊断中的应用

针对不同轴承数据特征选择困难和单个分类器方法在滚动轴承故障诊断中精度较低的问题,提出一种基于分类与回归树的Xgboost(e Xtreme Gradient Boosting)轴承故障诊断算法。Xgboost是包含多个分类器的集成学习方法。通过Xgboost的"提升"思想来提高滚动轴承故障诊断的精度。首先,从滚动轴承的振动信号中提取时域特征参数;然后利用Xgboost算法对滚动轴承故障进行诊断。将SQI-MFS实验平台的轴承振动数据,与传统分类器(支持向量机、邻近算法和人工神经网络)以及单个分类回归树的诊断结果相比,结果表明Xgboost在轴承故障诊断率上优于上述几种算法,且计算时间比传统提升决策树算法短。     

基于改进麻雀算法优化支持向量机的滚动轴承故障诊断研究

针对群智能算法优化支持向量机模型应用在滚动轴承故障诊断领域中易陷入局部最优、准确率较低的问题,提出了一种基于改进麻雀算法(sparrow search algorithm, SSA)优化支持向量机(support vector machine, SVM)的滚动轴承故障诊断方法。首先引入均匀化分布Chebyshev混沌映射初始化麻雀种群,以提高种群空间分布均匀性,之后将自适应惯性权重融入麻雀算法的发现者位置更新,最后对更新位置后的最优麻雀进行随机游走扰动,提高算法的全局和局部搜索能力,避免算法陷入局部最优。将该算法用于支持向量机的参数优化,构建改进麻雀算法优化支持向量机故障诊断模型实现对轴承故障信号的分类诊断。滚动轴承故障诊断试验分析结果表明,该算法模型故障分类效果明显优于粒子群算法优化支持向量机模型、遗传算法优化支持向量机模型和麻雀算法优化支持向量机模型,能够有效识别滚动轴承各故障类型。     

被动免疫算法在模拟电路故障诊断中的应用研究

基于生物免疫系统的信息处理机理,介绍了被动免疫算法的实现过程,并将其应用到某装备电路板的故障诊断之中。该算法具有边检测边学习的动态调整功能,仿真和实验实例表明,该算法适合于电路板的故障诊断,有较高的故障诊断率。     

一种基于分段幂函数插值法的经验模态分解方法及其应用研究

针对经验模态分解(EMD)方法的分解不完全问题,提出一种改进EMD算法。该算法采用分段幂函数插值法代替原EMD算法中的三次样条插值法,实验表明其分解效果更充分更完全。在此基础上,结合时间序列分析中的AR模型,提出一种基于EMD和AR模型的故障诊断方法,并将其应用到电磁换向阀的故障诊断中。实验结果表明,该方法能够正确有效地实现电磁换向阀的故障诊断。     

正向选择免疫算法在结构损伤诊断中的应用

结构状况可根据结构响应信号的异常进行判断,人工免疫系统能有效地应用于信号的异常检测。对基于免疫机制的正向选择算法进行了研究,利用具有时频局部化特性的小波包分解得到表征结构特征的小波包能量谱,通过正向选择算法检测小波包能量谱的异常来辨识结构状况。对正向选择算法进行了改进,可减少"自我"空间检测子的生成数目,加快检测速度,促进正向选择算法的实际应用。以ASCE学会提出的基准结构为对象,验证了正向选择算法在损伤诊断中的有效性,并与反向算法进行了比较。     

计算智能技术在陶瓷材料优化设计中的应用

综述了计算智能在陶瓷材料优化设计中的应用现状,阐明了利用人工神经网络以及遗传算法预测陶瓷材料性能和组分优化的方法,介绍了人工神经网络、遗传算法与免疫算法和模拟退火算法相结合的高效计算智能方法以及模糊神经网络在材料设计中的应用,分析了陶瓷材料优化设计中存在的问题并提出了今后的研究方向。     

A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called ‘antivirus’) to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.     

Managing warehousing in an agile supply chain environment: an F-AIS algorithm based approach

In recent years, application of the agile concept in the manufacturing sector has been researched extensively to reduce the varying effect of customer demands. However, most of the research work is focused on the shop floor of different manufacturing processes, while issues concerning the control of warehouse scheduling in a supply chain have been neglected so far. Realising this in the present research an attempt has been made to address the scheduling aspect of a warehouse in an agile supply chain environment. To resolve the warehouse problem in this paper, the authors have proposed a new Fuzzy incorporated Artificial Immune System Algorithm (F-AIS). This algorithm encapsulates the salient features of a fuzzy logic controller and immune system. The proposed algorithm has been compared with genetic algorithm (GA), simulated annealing (SA) and artificial immune system (AIS) algorithm to reveal the efficacy of the proposed F-AIS algorithm.     

A weight-based multiobjective immune algorithm: WBMOIA

A novel immune algorithm is suggested for finding Pareto-optimal solutions to multiobjective optimization problems based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. In the proposed algorithm, a randomly weighted sum of multiple objectives is used as a fitness function, and a local search algorithm is incorporated to facilitate the exploitation of the search space. Specifically, a new truncation algorithm with similar individuals (TASI) is proposed to preserve the diversity of the population. Also, a new selection operator is presented to create the new population based on TASI. Simulation results on seven standard problems (ZDT2, ZDT6, DEB, VNT, BNH, OSY and KIT) show that the proposed algorithm is able to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the vector immune algorithm and the elitist non-dominated sorting genetic system.     

无量纲指标及人工免疫系统故障诊断技术的应用研究

针对时域分析法有量纲指标在阴性选择算法中进行故障诊断时的不足,在深入研究人工免疫原理的同时,首次将阴性选择算法与无量纲指标结合,通过模拟机组的实验,验证了将阴性选择算法与无量纲指标结合进行故障诊断的正确性和有效性。最后通过一个实际诊断的例子说明了该方法对缓变故障诊断的准确性。     

改进的人工免疫算法在图像配准参数优化中的应用

借鉴禁忌搜索的思想改进了人工免疫网络算法(aiNet),提出一种禁忌人工免疫网络算法(TS-aiNet).在算法中引入禁忌表,禁忌在网络迭代中亲和力不再增加的细胞,通过特赦准则赦免一些被禁忌的优良状态;增加记忆表,保存成熟的记忆细胞;重新定义高斯变异方式,保证多样化的搜索.利用Markov链分析了该算法的全局收敛性,通过对典型系统的仿真实验分析了该算法的性能,并与克隆选择算法和opt-aiNet算法进行了比较,最终将改进的算法运用到红外与可见光图像配准中,像素级配准精度可以达到0.5像素.实验结果表明,该算法在多模态搜索空间中具有更好的全局收敛性、稳定性和发现极值点能力,能够克服早熟现象,提高图像配准的速度和精度,是一种有效的全局优化方法.     

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.     

Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms

The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field. New algorithms are continually being designed on the basis of observations of nature, wildlife, and humanity. In this paper, we review eight major evolutionary algorithms that emulate the behavior of civilization, ants, bees, fishes, and birds (i.e., genetic algorithms, bee colony optimization, simulated annealing, particle swarm optimization, biogeography-based optimization, artificial immune system optimization, cuckoo algorithm and imperialist competitive algorithm). We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems. Results from a literature survey show the best results found for series, series–parallel, bridge, and applied case problems (e.g., overspeeding gas turbine benchmark). Review of literature from recent years indicates an extensive improvement in the algorithm reliability performance. However, this improvement has been difficult to achieve for high-reliability applications. Insights and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper.     

Modeling and identification of the multi-axis drive system using support vector machine and granular computing

Advanced manufacturing technology requires high-precision capability in multi-axis computer numerical control (CNC) machine tools. At present, the modeling and identification for the drive system of CNC machine tools has some defects. In order to solve the problem, some interdisciplinary theories and methods, such as support vector machines, granular computing, artificial immune algorithms, and particle swarm optimization algorithms, have been used to model and identify multi-axis drive systems for CNC machine tools. An identification method using a support vector machine, based on granular computing, is presented to identify a multi-axis servo drive system model for improving the precision of model identification, and an immune particle swarm optimization algorithm, based on crossover and mutation functions, is proposed to optimize the structure parameters of the support vector machine based on granular computing. The proposed identification method was evaluated by experiments using the multi-axis servo drive system. The experimental results showed that the proposed approach is capable of improving modeling and identification precision.