A neural networks-based negative selection algorithm in fault diagnosis

Inspired by the self/nonself discrimination theory of the natural immune system, the negative selection algorithm (NSA) is an emerging computational intelligence method. Generally, detectors in the original NSA are first generated in a random manner. However, those detectors matching the self samples are eliminated thereafter. The remaining detectors can therefore be employed to detect any anomaly. Unfortunately, conventional NSA detectors are not adaptive for dealing with time-varying circumstances. In the present paper, a novel neural networks-based NSA is proposed. The principle and structure of this NSA are discussed, and its training algorithm is derived. Taking advantage of efficient neural networks training, it has the distinguishing capability of adaptation, which is well suited for handling dynamical problems. A fault diagnosis scheme using the new NSA is also introduced. Two illustrative simulation examples of anomaly detection in chaotic time series and inner raceway fault diagnosis of motor bearings demonstrate the efficiency of the proposed neural networks-based NSA.     

Clonal optimization-based negative selection algorithm with applications in motor fault detection

The Negative Selection Algorithm (NSA) and clonal selection method are two typical kinds of artificial immune systems. In this paper, we first introduce their underlying inspirations and working principles. It is well known that the regular NSA detectors are not guaranteed to always occupy the maximal coverage of the nonself space. Therefore, we next employ the clonal optimization method to optimize these detectors so that the best anomaly detection performance can be achieved. A new motor fault detection scheme using the proposed NSA is also presented and discussed. We demonstrate the efficiency of our approach with an interesting example of motor bearings fault detection, in which the detection rates of three bearings faults are significantly improved.     

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.     

区域否定选择算法

否定选择算法将单个自体点和其邻近点作为自体区域训练检测器。研究了实值否定算法,定义了连续的自体区域,采用动态聚类法将自体样本点分类到自体区域,训练时根据自体区域半径和与自体区域重心之间的余弦距离做局部训练,并在自体区域内使用可变阈值检测器。实验证明当耐受自体点被当成一个整体使用时能提供更多的信息,可以探测出自体区域边界,使系统效率和检测率得到提高。     

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。     

基于AdaBoost算法的故障诊断仿真研究

AdaBoost算法是提高预测学习系统预测能力的有效工具。提出一种基于AdaBoost算法的神经网络故障诊断方法,利用多层前向神经网络作为故障弱分类器,实现了对多类故障的诊断。为了克服AdaBoost对数据噪声比较敏感的不足,通过降低错分样本的权重改进了算法。针对一个涡轮喷气发动机气路部件故障的仿真实验表明,这种方法提高了最终故障分类器的泛化能力,改善了其噪声鲁棒性,便于工程应用。     

一种故障诊断的贝叶斯优化算法研究*

提出一种基于改进贝叶斯优化算法的故障模式聚类算法,通过结合贝叶斯优化算法中的先验知识来提高算法的可靠性和全局收敛性。将改进的优化算法应用到高维数据最优统计聚类分析中,可快速优化聚类参数,得到全局最优解。以飞行控制系统操纵面的故障诊断为例进行仿真验证,结果表明该算法结构简单、故障识别可靠。     

轨道车辆故障诊断算法参数优选和故障监测部位确定

转向架作为轨道车辆的重要部件,其弹性部件与阻尼部件的安全对车辆的安全有着重要影响。对轨道车辆转向架的故障种类、故障状态以及故障诊断方法中参数的选择进行了研究。对车辆的故障产生后果和现实情况进行了分析。为实现转向架故障诊断算法的输入参数优选和对应故障敏感部位的确定,提出P指标进行评判。利用matlab对车体、转向架以及转向架与轮对之间的弹性部件和阻尼部件建模,建立轨道车辆一系悬挂、二系悬挂的故障模型以及部件性能衰退的故障模型。用P作为评价故障前后统计参数改变程度的指标。实现车辆故障诊断算法输入参数的优选和对应故障敏感部位的确定。经过仿真实验可以利用指标P对故障诊断算法的参数输入做出优选,对故障的监测部位有明确定位。     

Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis

Feature selection can directly ascertain causes of faults by selecting useful features for fault diagnosis, which can simplify the procedures of fault diagnosis. As an efficient feature selection method, the linear kernel support vector machine recursive feature elimination (SVM-RFE) has been successfully applied to fault diagnosis. However, fault diagnosis is not a linear issue. Thus, this paper introduces the Gaussian kernel SVM-RFE to extract nonlinear features for fault diagnosis. The key issue is the selection of the kernel parameter for the Gaussian kernel SVM-RFE. We introduce three classical and simple kernel parameter selection methods and compare them in experiments. The proposed fault diagnosis framework combines the Gaussian kernel SVM-RFE and the SVM classifier, which can improve the performance of fault diagnosis. Experimental results on the Tennessee Eastman process indicate that the proposed framework for fault diagnosis is an advanced technique.     

基于选择判据与贴近度的电网故障诊断算法

针对传统D-S 证据理论难以解决高度冲突证据融合问题, 提出一种新的证据合成算法. 将贴近度概念引入D-S 证据合成中, 通过证据的一致性度量计算其权重, 实现冲突证据的加权融合. 提出证据合成方法选择判据, 将证据合成分为冲突和非冲突2 类, 分别采用改进算法和传统算法对证据进行融合. 实例验证表明, 所提出的方法信息聚焦性能优越, 可以有效解决冲突证据合成问题, 在解决电力系统故障诊断问题方面有良好的效果.

     

QoS-aware web service selection with negative selection algorithm

Web service selection, as an important part of web service composition, has direct influence on the quality of composite service. Many works have been carried out to find the efficient algorithms for quality of service (QoS)-aware service selection problem in recent years. In this paper, a negative selection immune algorithm (NSA) is proposed, and as far as we know, this is the first time that NSA is introduced into web service selection problem. Domain terms and operations of NSA are firstly redefined in this paper aiming at QoS-aware service selection problem. NSA is then constructed to demonstrate how to use negative selection principle to solve this question. Thirdly, an inconsistent analysis between local exploitation and global planning is presented, through which a local alteration of a composite service scheme can transfer to the global exploration correctly. It is a general adjusting method and independent to algorithms. Finally, extensive experimental results illustrate that NSA, especially for NSA with consistency weights adjusting strategy (NSA+), significantly outperforms particle swarm optimization and clonal selection algorithm for QoS-aware service selection problem. The superiority of NSA+ over others is more and more evident with the increase of component tasks and related candidate services.     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

改进AFSA算法优化SVM的变压器故障诊断

提出一种基于改进人工鱼群算法优化支持向量机（SVM）的变压器故障诊断方法。首先对基本人工鱼群算法进行改进,引入柯西变异优化觅食行为,并在算法的迭代过程中利用鱼群搜索到的信息和[t]分布变异的特点,对劣质个体鱼进行消亡与重生,提高鱼群算法的寻优效率和求解精度。然后,利用改进的人工鱼群算法优化SVM的核函数参数及惩罚系数,使SVM分类器获得最佳的分类精度。最后采用决策导向无环图（DDAG）方法建立变压器故障诊断SVM多分类决策模型。通过仿真实验将提出的方法与网格搜索法Grid-SVM、GA-SVM、PSO-SVM比较,所建模型具有更高的诊断正确率。     

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。     

Sensor deployment for fault diagnosis using a new discrete optimization algorithm

Optimal allocation of the sensor in a wireless sensor network (WSN) is required to have a satisfactory fault diagnosis within the system. In fact, the sensor nodes in the network should be located in an arrangement to maximize the failure diagnosis. In this paper, the sensor deployment optimization to diagnose the distributed failures in a wireless unmanned aerial vehicles (UAVs) network has been studied. In this way, a novel evolutionary optimization algorithm inspired by the gases Brownian and turbulent rotational motion is utilized which is called Discrete Gases Brownian Motion Optimization (DGBMO) algorithm. An integer linear programming (ILP) approach is used to formulate the sensor deployment. Then the sensor deployment optimization is solved by DGBMO as well as generic ILP solvers and Boolean satisfiability-based ILP solvers. The results show that DGBMO is suitable for sensor disposition optimization especially in large-sized UAV networks.     

一种可变模糊匹配阴性选择算法

通过对人工免疫系统中阴性选择算法机理的分析,定义了连续相似度与背离度,提出了一种可变模糊匹配阴性选择免疫算法.算法通过调整匹配阈值的方法降低黑洞数量;利用模糊思想,实现了具有一定连续相似度的模糊匹配,模糊程度可控;为了消除检测器间的冗余,提高检测器集的检测效率,算法在模糊匹配的基础上,生成了有效检测器集.仿真实验表明,可变模糊匹配阴性选择算法生成的成熟检测器检测范围较大,空间覆盖率明显提高,黑洞数量大幅下降,算法具有较强的鲁棒性.     

基于否定选择的身份认证技术

为了提高用户身份认证的有效性,给出了一个集成肯定认证机制和否定认证机制的双层认证模型.首先,基于人体免疫系统T细胞识别自体和非自体的原理,设计了基于否定选择的身份认证机制;接着研究了否定认证机制的关键技术;最后给出双层认证模型的实现细节及性能分析.仿真实验表明,该身份认证模型能够承受各种口令攻击,有效过滤非法用户的登录请求,具有较好的鲁棒性和可用性.     

Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

In this paper, a hybrid online learning model that combines the fuzzy min–max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks.