A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.

Fault Detection Using Negative Selection and Genetic Algorithms

In this paper,negative selection and genetic algorithms are combined and an improved bi-objective optimization scheme is presented to achieve optimized negative selection algorithm detectors.The main aim of the optimal detector generation technique is maximal nonself space coverage with reduced number of diversified detectors.Conventionally,researchers opted clonal selection based optimization methods to achieve the maximal nonself coverage milestone;however,detectors cloning process results in generation of redundant similar detectors and inefficient detector distribution in nonself space.In approach proposed in the present paper,the maximal nonself space coverage is associated with bi-objective optimization criteria including minimization of the detector overlap and maximization of the diversity factor of the detectors.In the proposed methodology,a novel diversity factorbased approach is presented to obtain diversified detector distribution in the nonself space.The concept of diversified detector distribution is studied for detector coverage with 2-dimensional pentagram and spiral self-patterns.Furthermore,the feasibility of the developed fault detection methodology is tested the fault detection of induction motor inner race and outer race bearings.

Research on a randomized real-valued negative selection algorithm

A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.

A Cuckoo Search Detector Generation-based Negative Selection Algorithm

The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificial immune system.A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities.However,these detectors have limited performance.Redundant detectors are generated,leading to difficulties for detectors to effectively occupy the non-self space.To alleviate this problem,we propose the nature-inspired metaheuristic cuckoo search(CS),a stochastic global search algorithm,which improves the random generation of detectors in the NSA.Inbuilt characteristics such as mutation,crossover,and selection operators make the CS attain global convergence.With the use of Lévy flight and a distance measure,efficient detectors are produced.Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA,with an average increase of 3.52%detection rate on the tested datasets.The proposed method shows superiority over other models,and detection rates of 98%and 99.29%on Fisher’s IRIS and Breast Cancer datasets,respectively.Thus,the generation of highest detection rates and lowest false alarm rates can be achieved.

A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,the nonselfdetector generation efficiency is low;a large number of nonselfdetector is needed for precise detection;low detection rate with various application data sets.Aiming at those problems,a novel radius adaptive based on center-optimized hybrid detector generation algorithm(RACO-HDG)is put forward.To our best knowledge,radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity.RACO-HDG works efficiently in three phases.At first,a small number of self-detectors are generated,different from typical NSAs with a large number of self-sample are generated.Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible.Secondly,without any prior knowledge of the data sets or manual setting,the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism.In this way,the number of abnormal detectors is decreased sharply,while the coverage area of the nonself-detector is increased otherwise,leading to higher detection performances of RACOHDG.Finally,hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected.Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate,lower false alarm rate and higher detection efficiency compared with other excellent algorithms.

Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.

基于协同进化的免疫检测器分布优化算法

为解决免疫实值检测器的黑洞问题,分析检测器规模对检测性能的影响,提出一种基于协同进化的免疫实值检测器分布优化算法。将检测器集分成不同子集,寻找每个子集的最优个体,利用各子集间的相互作用与影响对各子集进行优化处理,取并集构成完整检测器集。实验结果表明,与否定选择算法相比,该算法不仅可以有效减少黑洞的产生,并且能以较少的检测器精确地覆盖非自体空间,从而提高检测器性能。

免疫入侵检测多形态检测算法

针对基于免疫机制的入侵检测系统的单一形态检测器检测性能低下的问题,分析了二进制形态空间和实值形态空间各自的不足,借鉴免疫独特型网络理论和免疫危险理论的信号机制,提出了多形态检测算法,该算法使用二进制形态和实值形态两种检测器协同检测,通过协同信号判定事件是否异常。经实验表明,该多形态检测算法较单一形态检测算法在检测性能上有了极大的提高。

基于免疫克隆优化的阴性选择算法在电机故障检测中的应用

阴性选择算法(NSA)和免疫克隆选择算法是两种典型的人工免疫系统。首先介绍了阴性选择算法和免疫克隆算法的基本原理,由于阴性选择算法的检测器存在大量无法检测的黑洞。采用免疫克隆优化算法对阴性选择算法生成的检测器进行优化,以提高故障检测率。最后通过检测电机的故障轴承证明了方法的有效性。

基于自适应性分类器的垃圾邮件检测

垃圾邮件形式内容多变,容易伪装成正常邮件而绕过检测,其中新型垃圾邮件的检测漏报率较高。为此,结合反向选择和支持向量机(SVM)的思想,设计一种新的自适应性分类器并应用于垃圾邮件检测。使用SVM的最优超平面对邮件进行预分类,得到与预测模型匹配的"正常邮件"和垃圾邮件,运用反向选择算法(NSA)对筛选出的"正常邮件"数据集进行二次过滤以检测出新型垃圾邮件,并利用含有标签的正常邮件和垃圾邮件集合自适应更新原有的最优超平面,循环上述检测过程直至垃圾邮件的识别率趋于稳定,最终得到的最优超平面符合当前检测最优。实验结果表明,相对于SVM与NSA,该检测方法能在保证正常邮件高识别率的基础上,提高新型垃圾邮件的识别率。

基于物元-阴性选择算法的轴箱轴承故障检测

针对高速列车轴箱轴承故障数据获取困难的问题,提出了一种无需先验知识的利用物元和阴性选择算法进行轴承故障检测的方法.首先利用多维物元构建阴性选择算法的检测器模型,以检测器与训练样本之间的综合关联度作为匹配规则,并在综合关联度约束范围内引入控制参数,实现检测器对非己空间的更大覆盖;其次,根据匹配规则和控制参数构建适应度函数,采用粒子群优化算法生成候选检测器,分析控制参数对检测器生成和粒子群优化算法收敛速度的影响;此外,为降低候选检测器集合的冗余度,基于关联度提出了检测器特征参数区间的合并规则,将成熟检测器个数降低至18个;最后,通过信号模拟方法生成轴箱轴承的各类故障信号,建立100组测试样本,并利用18个成熟检测器进行故障检测.研究结果表明:成熟检测器对不同类轴承故障均具有较好的检测性能,正常样本的检测器激活率为1.11%,故障样本的检测器激活率不低于96.67%.

基于自适应Voronoi检测器的故障检测算法

否定选择算法在单分类算法中具有良好特性,但在故障检测中,传统的否定选择算法训练时间过长,实际的检测精度不高。针对这些问题,提出一种基于自适应Voronoi检测器的否定选择算法。算法利用自体空间的内外边界样本生成检测器,弥补了实值检测器存在孔洞的缺陷,提高了检测器的覆盖率,且检测器仅需一次训练,减少了训练时间。通过对Iris数据和华北某电厂真实数据进行实验,将传统否定选择算法同V-Detector算法进行对比。实验证明该算法相对传统否定选择算法减少了检测器的生成时间,提高了算法整体的检测精度,避免了检测器间孔洞的发生。

Improved Ladder Wave Modulation of Circulating Current Suppressing Control Strategy of MMC

This paper partitions the arm current of MMC into uncontrollable current and controllable current. The former is determined by the load that can’t be controlled by taking any control strategy. The later caused by the unbalanced total inserted voltage of three arms can be controlled by some improved algorithms. The conclusion based on the researching the essence of circulating current is reached that change the number of the inserted sub-modules in each phase can suppress the circulating current. Combined with the improved ladder wave modulation, a novel circulating current suppression strategy particularly for the inverter station is developed. The improved strategy can adapt to load changes and reduce the circulating current and output voltage THD of MMC ac terminals greatly without increasing any peripheral circuits. Finally, the simulation model of 100 submodules in each phase is constructed in MATLAB and the simulation results verify the correctness and effectiveness of the modified control algorithm.

基于局部线性嵌入的免疫检测器优化生成算法

网络安全已上升到国家安全战略层面,入侵检测技术是其重要的组成部分,已得到广泛关注.在基于免疫的入侵检测研究中,针对传统实值否定选择算法不利于高效分析数据而造成的检测器生成速度慢、检测效率低等问题,引入局部线性嵌入算法,借鉴其能对高维数据进行映射降维的特点,提出一种基于局部线性嵌入的免疫检测器优化生成算法,利用局部线性嵌入对高维数据预处理优化降维,并结合实值否定选择算法生成检测器.将该算法用于检测模型,从而提升检测器的生成速率,并可保证生成的检测器高效地处理高维数据.该算法在降维前后可保证样本的局部线性结构不变,具有可变参数少、计算时间短的特点.实验结果表明,所提出算法在显著提高检测器生成速率和对数据检测效率的基础上,检测性能也表现出很好的水平.

一种非线性过程监控方法

实际工业过程都具有非线性等特征。传统的监控方法有将降维后的非线性数据映射到高维线性空间再进行数据处理,实现过程的监控。本文是在一种否定选择算法的基础上,首先利用最大方差展开(MVU)方法对正常高维数据进行降维,再利用否定选择算法直接对降维后的多维非线性数据建立"超球体群"模型,实现对过程的监控,保证工业过程的平稳运行。仿真实验是基于TE模型进行的,仿真结果表明该方法较传统方法及其他改进方法具有更好的监控能力,说明了该方法的有效性。