Negative selection algorithm with constant detectors for anomaly detection

In the paper, two novel negative selection algorithms (NSAs) were proposed: FB-NSA and FFB-NSA. FB-NSA has two types of detectors: constant-sized detector (CFB-NSA) and variable-sized detector (VFB-NSA). The detectors of traditional NSA are generated randomly. Even for the same training samples, the position, size, and quantity of the detectors generated in each time are different. In order to eliminate the effect of training times on detectors, in the proposed approaches, detectors are generated in non-random ways. To determine the performances of the approaches, the experiments on 2-dimensional synthetic datasets, Iris dataset and ball bearing fault data were performed. Results show that FB-NSA and FFB-NSA outperforms the other anomaly detection methods in most cases. Besides, CFB-NSA can detect the abnormal degree of mechanical equipment. To determine the performances of CFB-NSA, the experiments on ball bearing fault data were performed. Results show that the abnormal degree based on the CFB-NSA can be used to diagnose the different fault types with the same fault degree, and the same fault type with the different fault degree.     

V-detector: An efficient negative selection algorithm with “probably adequate” detector coverage

This paper describes an enhanced negative selection algorithm (NSA) called V-detector. Several key characteristics make this method a state-of-the-art advance in the decade-old NSA. First, individual-specific size (or matching threshold) of the detectors is utilized to maximize the anomaly coverage at little extra cost. Second, statistical estimation is integrated in the detector generation algorithm so the target coverage can be achieved with given probability. Furthermore, this algorithm is presented in a generic form based on the abstract concepts of data points and matching threshold. Hence it can be extended from the current real-valued implementation to other problem space with different distance measure, data/detector representation schemes, etc. By using one-shot process to generate the detector set, this algorithm is more efficient than strongly evolutionary approaches. It also includes the option to interpret the training data as a whole so the boundary between the self and nonself areas can be detected more distinctly. The discussion is focused on the features attributed to negative selection algorithms instead of combination with other strategies.     

An antigen space density based real-value negative selection algorithm

The negative selection algorithm (NSA) is an important detector generation algorithm for artificial immune systems. In high-dimensional space, antigens (data samples) distribute sparsely and unevenly, and most of them reside in low-dimensional subspaces. Therefore, traditional NSAs, which randomly generate detectors without considering the distribution of the antigens, cannot effectively distinguish them. To overcome this limitation, the antigen space density based real-value NSA (ASD-RNSA) is proposed in this paper. The ASD-RNSA contains two new processes. First, in order to improve detection efficiency, ASD-RNSA utilizes the antigen space density to calculate the low-dimensional subspaces where antigens are densely gathered and directly generate detectors in these subspaces. Second, to eliminate redundant detectors and prevent the algorithm from prematurely converging in high-dimensional space, ASD-RNSA suppresses candidate detectors that are recognized by other mature detectors and adopts an “antibody suppression rate” to replace the “expected coverage” as the termination condition. Experimental results show that ASD-RNSA achieves a better detection rate and has better generation quality than classical real-value NSAs.     

Sensor selection for fault diagnosis in uncertain systems

ABSTRACT

Finding the cheapest, or smallest, set of sensors such that a specified level of diagnosis performance is maintained is important to decrease cost while controlling performance. Algorithms have been developed to find sets of sensors that make faults detectable and isolable under ideal circumstances. However, due to model uncertainties and measurement noise, different sets of sensors result in different achievable diagnosability performance in practice. In this paper, the sensor selection problem is formulated to ensure that the set of sensors fulfils required performance specifications when model uncertainties and measurement noise are taken into consideration. However, the algorithms for finding the guaranteed global optimal solution are intractable without exhaustive search. To overcome this problem, a greedy stochastic search algorithm is proposed to solve the sensor selection problem. A case study demonstrates the effectiveness of the greedy stochastic search in finding sets close to the global optimum in short computational time.     

Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine

In this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.     

Immune K-means and negative selection algorithms for data analysis

During the last decade artificial immune systems have drawn much of the researchers’ attention. All the work that has been done allowed to develop many interesting algorithms which come in useful when solving engineering problems such as data mining and analysis, anomaly detection and many others. Being constantly developed and improved, the algorithms based on immune metaphors have some limitations, though. In this paper we elaborate on the concept of a novel artificial immune algorithm by considering the possibility of combining the clonal selection principle and the well known K-means algorithm. This novel approach and a new way of performing suppression (based on the usefulness of the evolving lymphocytes) in clonal selection result in a very effective and stable immune algorithm for both unsupervised and supervised learning. Further improvements to the cluster analysis by means of the proposed algorithm, immune K-means, are introduced. Different methods for clusters construction are compared, together with multi-point cluster validity index and a novel strategy based on minimal spanning tree (mst) and a analysis of the midpoints of the edges of the (mst). Interesting and useful improvements of the proposed approach by means of negative selection algorithms are proposed and discussed.     

一类基于神经网络非线性观测器的鲁棒故障检测和诊断

利用神经网络的非线性建模能力，对一类具有建模不确定项的非线性系统提出一种基于观测器的故障检测和诊断的方法。设计的观测器不仅能实现故障检测，而旦应用神经网络设计的故障估计器能在线估计系统中的故障向量。通过分析验证了该方法对系统中的建模误差和外部扰动具有良好的鲁棒性。仿真结果表明所提出的方法是有效的。     

基于改进BP网络算法的故障诊断分析

介绍BP神经网络结构和学习方法,针对误差反向传播神经网络模型学习收敛速度慢、容易陷入局部极小点等缺点,本文对BP网络模型进行了改进。对原始数据采用非线性的归一化函数,提出一种更加有效的学习率改进算法,提高了网络的收敛速度,采用了一种新的权值及阈值初始化方法,以避免训练时误差陷入局部极小解,并对改进BP算法与传统的BP算法进行比较,验证了该算法的优越性。     

基于免疫文化算法的故障特征选择方法

提出了一种新的文化算法,基于免疫克隆选择原理改进了文化算法的种群空间,同时设计了一种新的历史知识及其影响函数。为了去除工业中故障诊断过程中的冗余变量,实现数据降维,提高故障诊断性能,将该免疫文化算法应用到故障特征选择当中,提出了一种封装式的特征选择方法。该方法利用抗体种群进行全局搜索,通过文化算法的信念空间保留历代最优个体,并对UCI数据集的高维数据进行特征子集选择。将该方法应用到TE过程故障诊断中,结果表明,相比于直接使用高维数据进行故障诊断,该算法有效降低了特征空间的维数,提高了分类精度。     

一种改进的基于免疫网络模型(aiNet) 的故障诊断算法

根据抗体群与抗原群的匹配关系,提出一种改进的基于免疫网络模型(aiNet)的故障诊断算法.建立了自适应调整剪枝和抑制阈值的规则,并对K近邻算法的附加距离阈值加以限制,提高了基于aiNet故障诊断算法对已知故障的识别率,克服了其不能识别新故障的缺点.仿真结果表明,改进算法具有优良的故障诊断性能.     

基于模糊神经网络的连铸机故障诊断研究

针对连铸机结晶器液压振动系统故障特点,采用模糊理论与神经网络相结合的方法对其进行故障诊断,用模糊信息处理方法对输入信号进行处理,然后采用神经网络的逼近能力实现连铸机结晶器液压系统振动故障诊断,利用现场数据进行了仿真实验,仿真结果表明该系统具有很好的识别能力,可以对不确定行知识进行很好的处理,提高故障诊断的精度。     

Human-like fault diagnosis using a neural network implementation of plausibility and relevance

In real systems, fault diagnosis is performed by a human diagnostician, and it encounters complex knowledge associations, both for normal and faulty behaviour of the target system. The human diagnostician relies on deep knowledge about the structure and the behaviour of the system, along with shallow knowledge on fault-to-manifestation patterns acquired from practice. This paper proposes a general approach to embed deep and shallow knowledge in neural network models for fault diagnosis by abduction, using neural sites for logical aggregation of manifestations and faults. All types of abduction problems were considered. The abduction proceeds by plausibility and relevance criteria multiply applied. The neural network implements plausibility by feed-forward links between manifestations and faults, and relevance by competition links between faults. Abduction by plausibility and relevance is also used for decision on the next best test along the diagnostic refinement. A case study on an installation in a rolling mill plant is presented.     

非线性系统的故障诊断技术

文中对非线性系统的故障诊断方面问题给予了归纳总结,指出了基于数学模型方法,基于信号处理方法和基于知识的方法在实现非线性系统故障诊断的基本思想,并进一步指出了各各非线性系统故障诊断方法及可能的发展方向。     

Artificial immune systems applied to fault detection and isolation: A brief review of immune response-based approaches and a case study

This paper aims to document the application of a new generation of artificial immune systems (AIS) in fault detection and isolation problems. These kind of algorithms are able to explore normal and anomalous behavior evidences, however, they may often require a more explicit prior knowledge provided by experts, usually difficult to obtain in some practical cases. Thus, many immune inspired approaches applied to fault detection and isolation (FDI) in the literature are based on negative selection algorithms. Considering these points, this work presents a review on three AIS approaches. Once reviewed and contextualized, the evaluated techniques are properly adjusted considering their main parameters and ways of processing data, and then, applied to a case study of fault detection and isolation in order to provide a performance analysis of these techniques, according to their applicability to these problems.     

面向子空间的否定选择算法

传统的否定选择算法无法有效识别落入到低维子空间的样本,导致算法在高维空间检测性能不佳。为此,本文提出了面向子空间的否定选择算法（Subspace-oriented Real Negative Selection Algorithm, SONSA）。在训练常规检测器的基础上,SONSA将搜索样本分布较密度高的低维子空间以进一步训练面向子空间的检测器,从而提高算法对低维子空间内样本的识别能力。实验结果表明在标准数据集Haberman’s Survival（3维）与Breast Cancer Wisconsin (9维)上,相对于经典的V-Detector算法以及采用PCA降维的V-Detector算法,SONSA能在误报率相似的情况下显著地提高检测率。     

MILA – multilevel immune learning algorithm and its application to anomaly detection

T-cell-dependent humoral immune response is one of the more complex immunological events in the biological immune system, involving interaction of B cells with antigen (Ag) and their proliferation, differentiation and subsequent secretion of antibody (Ab). Inspired by these immunological principles, a Multilevel Immune Learning Algorithm (MILA) is proposed for novel pattern recognition. This paper describes the detailed background of MILA, and outlines its main features in different phases: Initialization phase, Recognition phase, Evolutionary phase and Response phase. Different test problems are studied and experimented with MILA for performance evaluation. The results show MILA is flexible and efficient in detecting anomalies and novel patterns.     

一种基于输入训练神经网络的非线性PCA 故障诊断方法

简要讨论了线性PCA故障诊断方法存在的问题，提出一种基于输入训练神经网络的非线性PCA故障诊断方法。该方法首先利用输入训练神经网络和BP网络双网络机制，实现非线性主元的识别，并采用统计方法进行故障检测与故障分离。对CSTR的仿真研究结果表明，该方法能够克服线性PCA方法在提取过程变量的非线性特征方面存在的不足，并能够准确地进行故障检测和分离。     

基于遗传算法的WANN的加速器故障诊断分析

利用基于遗传算法的全局优化能力,小波分析具有数据压缩和特征提取的特性,神经网络具有非线性映射和学习推理的优点.将遗传算法(GA)、小波(WA)与概率神经网络(PNN)算法相结合,用于加速器故障.实例证明,利用GA-WANN模型进行故障,故障诊断速度快,鲁棒性好,正确率高.     

基于改进负选择算法的异常检测

为解决基于负选择的异常检测算法中检测器数目和检测器对非我空间的覆盖二者之间的矛盾问题,采用粒子群优化算法(PSO)来优化负选择算法中随机产生的检测器的位置,从而实现用较少的检测器实现对非我空间更大的覆盖.在保证检测器尽可能小的覆盖自我空间的前提下,扩大检测器集合对非我空间的覆盖,并且在这个过程中检测器的数目是一定的.对正弦时间序列信号(artificial datasets)和轴承滚珠故障的振动信号(real-word datasets)进行了仿真实验.实验结果表明,该算法相对于原始的负选择算法在对非我空间的覆盖和检测率的提高方面有显著的效果.     

A hybrid artificial immune system and Self Organising Map for network intrusion detection

Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer systems over a network. Two broad approaches exist to tackle this problem: anomaly detection and misuse detection. An anomaly detection system is trained only on examples of normal connections, and thus has the potential to detect novel attacks. However, many anomaly detection systems simply report the anomalous activity, rather than analysing it further in order to report higher-level information that is of more use to a security officer. On the other hand, misuse detection systems recognise known attack patterns, thereby allowing them to provide more detailed information about an intrusion. However, such systems cannot detect novel attacks.A hybrid system is presented in this paper with the aim of combining the advantages of both approaches. Specifically, anomalous network connections are initially detected using an artificial immune system. Connections that are flagged as anomalous are then categorised using a Kohonen Self Organising Map, allowing higher-level information, in the form of cluster membership, to be extracted. Experimental results on the KDD 1999 Cup dataset show a low false positive rate and a detection and classification rate for Denial-of-Service and User-to-Root attacks that is higher than those in a sample of other works.