基于小波包去噪与PCA的故障检测与诊断方法研究

提出了一种基于小波包去噪和主元分析的故障检测和诊断方法.该方法利用小波包分解系数收缩的信号去噪法先对正常工况下的数据进行处理,然后运用T2统计、Q统计方法,结合主元得分图和变量贡献图对一模型进行了仿真研究.结果表明,该方法是有效的.     

多尺度主元分析的一种改进方法

MSPCA方法在生产过程监控方面有着广泛应用.本文在研究该方法的基础之上,提出了一些改进,在其进行小波分解后即对其小波系数进行阈值处理,使小波消噪与MSPCA方法合为一体,并运用统计控制图中的平方预测误差(SPE)图方法检测引起过程变化或故障的过程变量.在保证其MSPCA算法复杂度不变的前提下,能够消除数据的噪声污染,使故障诊断的误报大为减少.经检验,该算法确实可行,相对于小波消噪与MSPCA方法分别进行,效率提高了大约13%-17%.     

GNSS-RTK形变监测实时数据处理方法研究

针对GNSS-RTK技术应用于形变监测时数据结果不佳导致无法进行有效监测的问题,提出了一种基于形变数据的数据预处理和异常状态识别方法。首先采用改进拉依达准则进行粗差剔除,然后采用小波去噪处理,最后根据形变数据异常模型构建休哈特均值控制图。结果表明,改进拉依达准则可以实现对实测形变数据的实时粗差剔除,根据最优小波系数设置的小波去噪可以有效提高形变数据精度,休哈特均值控制图结合改进拉依达准则可以实现对形变数据的无误警有效监控。     

主元分析在变压器故障检测与识别中的应用

针对IEC三比值法进行变压器故障诊断存在缺编码、编码边界模糊和诊断率偏低的问题,提出了采用主元分析和重构贡献图的故障诊断方法。在建立的PCA(Principal Component Analysis)统计过程模型上,构建SPE、T2统计量和重构贡献指标对变压器故障进行检测,并分析了贡献图法和重构贡献法的故障诊断性能。仿真结果表明基于主元分析和重构贡献图的故障诊断方法对数据更敏感,能够有效弥补IEC三比值法的不足,提高故障诊断正确率。     

基于层叠模型的网络流量异常检测方法*

进行网络流量异常检测,需要对正常流量行为建立准确的模型,根据异常流量与正常模型间的偏离程度作出判断。针对现有网络流量模型中自相似模型与多分形模型无法全面刻画流量特征的不足,提出了一种基于流量层叠模型分析的异常检测算法,采用层叠模型对整个时间尺度上的流量特征进行更准确的描述,并运用小波变换对流量的层叠模型进行估计,分析异常流量对模型估计的影响,提出统计累计偏离量进行异常流量检测的方法。仿真结果表明,该方法能够有效检测出基于自相似Hurst系数方法不能检测的弱异常以及未明显影响Hurst系数变化的异常流。     

基于小波和PCA的火炮输弹系统故障诊断研究

为了分析火炮自动供弹系统运行状态并实现系统故障的快速诊断,研究开发了基于小波分析法和主元分析法的故障诊断系统。提出了利用小波分析供弹系统中变化剧烈的非平稳监控信号,这种方法对监控信号进行小波分解、非线性阈值化降噪处理完成了信号的重构。采用主元分析法(PCA)从监控变量中提取出主元,按照置信界限85%选取主元个数。在此基础上通过计算SPE和T2统计量,并分析统计量与控制限的状态判断系统是否正常工作。分析过程变量对SPE的贡献图识别故障源,并通过实验验证了该方法的有效性。     

基于动态核独立分量分析的高含硫天然气净化过程异常检测与诊断

目前高含硫天然气净化过程存在多参数动态相关的特性,导致基于静态多元统计过程监控方法对于异常状态检测效果较差。提出一种考虑参数时序自相关性的动态核独立分量分析(DKICA)异常检测与诊断方法。首先,引入自回归(AR)模型,通过参数辨识确定模型阶次,描述监控过程的时序自相关性;然后,将原始变量投影到核独立元空间,通过监控独立元对应的T²和SPE统计量是否超出正常状态设定的控制限,实现异常检测;最后计算所述T²统计量对原始变量的一阶偏导数,绘制贡献图实现异常诊断。以某高含硫天然气净化厂采集的数据进行分析,结果表明基于DKICA高含硫天然气净化过程异常检测精度要优于静态独立分量分析所得的检测精度。     

小波变换在车流量检测雷达信号去噪中的应用

分析了车流量检测雷达中频信号的特点,探讨了小波变换在中频信号去噪中的应用;分别采用小波变换阈值法、平移不变量法和小波变换模极大值法对中频信号进行了去噪处理并对这三种方法的去噪性能进行了分析;实验结果表明,三种方法均能有效的抑制噪声,其中小波阈值法去噪效果较好,计算速度很快;平移不变量法去噪效果略优于小波阈值法,但计算速度较慢,模极大值法虽然取得了最好的去噪效果,但计算速度很慢;综合而言,小波变换阈值法最具实用价值。     

基于正交小波变换的心冲击图自适应去噪方法

研究了心冲击图的正交小波变换最小均方自适应去噪;阐述了基于正交小波变换的最小均方自适应去噪原理;利用径向高斯核函数对心冲击图进行自适应时频联合分析,得到了中心频率并确定了小波分解尺度;提出了通过选择小波基函数和输入信号长度确定自适应滤波器阶数的方法;从矩阵角度给出了算法的实现步骤,并分析了正交小波变换提高最小均方算法收敛速度的原因.实验结果表明,正交小波变换最小均方算法使自适应去噪后的心冲击图更快达到稳态,随心动周期的变化趋势更加明显.比较去噪前后心冲击图的功率谱密度可知,正交小波变换最小均方算法在保留心冲击图特征的同时自适应地去除了其中的时变噪声,获得了良好的去噪效果.     

基于HHT算法的扬声器异常音检测技术

为了有效地检测扬声器异常音,提出一种基于希尔伯特-黄变换的异常音识别及去噪方法。分析了小波变换的不足,再进行HHT分析。建立常见的几类扬声器异常音数学模型,并经EMD分解得到包含异常振动信息的IMF分量,利用IMF分量特点进行去噪处理。再求出各模拟异常音信号的Hilbert谱,采用图像二值化技术处理Hilbert谱,和小波变换时频谱比较后证明了HHT方法具有更多的优势。而且,根据不同类型异常音在时频域的特征,即可判断出扬声器异常音类型。matlab仿真实验结果表明,当归一化阈值取为0.065时,会得到清晰的图像,能更精确地检测出异常音类型。     

基于改进PCA的正常工况改变与过程故障识别方法

针对传统的主元分析(PCA)的T~2和平方预测误差(SPE)检验所提供的信息并不一致的缺陷,提出一种改进的PCA方法。该方法采用主元相关变量残差(PVR)和一般变量残差(CVR)统计量代替SPE统计量用于过程监测。将此改进的PCA方法应用到双效蒸发过程的仿真监测,与传统的PCA方法相比,新PCA方法能够有效地识别正常工况改变与过程故障引起的T~2图变化,避免了SPE统计量的保守性,能够提供更详细的过程变化信息,提高了对过程变化的分析与诊断能力。     

Detecting anomalies in spatio-temporal flow data by constructing dynamic neighbourhoods

In massive spatio-temporal datasets, anomalies that deviate from the global or local distributions are not just useless noise but possibly imply significant changes, surprising patterns, and meaningful insights, and because of this, detection of spatio-temporal anomalies has become an important research hotspot in spatio-temporal data mining. For spatio-temporal flow data (e.g., traffic flow data), the existing anomaly detection methods cannot handle the embedded dynamic characteristic. Therefore, this paper proposes the approach of constructing dynamic neighbourhoods to detect the anomalies in spatio-temporal flow data (called spatio-temporal flow anomalies). In this approach, the dynamic spatio-temporal flow is first modelled based on the real-time attribute values of the flow data, e.g., the velocity of vehicles. The dynamic neighbourhoods are then constructed by considering attribute similarity in the spatio-temporal flow. On this basis, global and local anomalies are detected by employing the idea of the G^⁎ statistic and the problem of multiple hypothesis testing is further addressed to control the false discovery rate. The effectiveness and practicality of our proposed approach are demonstrated through comparative experiments on traffic flow data from the central road network of central London for both weekdays and weekends.     

基于主元空间统计的传感器故障诊断与重构

针对主元分析故障诊断方法中所采用SPE统计量的局限性,通过分析主元模型的整体框架,提出一种通过T2统计量来研究传感器数据超限的故障检测与重构,确定故障大小与类型,采用迭代法恢复传感器示值的方法。仿真结果表明,该方法对传感器进行故障检测与重构后,其故障幅值完全处于精度范围内,且低于所要求的控制限。     

基于DPCA方法的传感器故障检测与诊断

针对多传感器的相关时序测量数据,在假设只存在传感器故障的前提下,提出了一种基于动态主成分分析（DPCA）的传感器故障检测方法。根据测量数据建立传感器的DPCA模型,在该模型基础上利用T2和SPE统计量进行传感器的故障检测。同时,将基于主成分分析（PCA）模型的传感器有效度指标SVI推广应用于DPCA模型中。通过对污水处理系统中重要传感器的故障诊断仿真实验表明：该方法能有效地检测和识别出故障传感器。     

空间价格均衡问题的超级网络解法

通过对空间价格均衡基础网络进行扩展, 建立了一个单起讫点对的超级网络。分析得出, 在这一超级网络上建立的用户均衡交通流分配模型涵盖了原来的空间价格均衡问题, 因此通过求解建立在该超级网络上的用户均衡的交通流分配模型, 可以得到相应的空间价格均衡问题的解。新建的超级网络中只增加了两个虚拟节点, 而添加的虚拟路段数量仅比原有起讫点数目多1个, 因此网络的规模并没有明显扩大。利用已有的交通流分配软件可以方便地将空间价格均衡问题的研究扩展到实际大规模的运输网络之中。超级网络法的有效性在应用算例中得到了体现。     

基于Netflow的异常流量分离以及归类

针对以往的各种异常流量检测算法只能在宏观上进行流量异常监测,不能进一步实时地将异常流量分离处理,提出了在Netflow流数据环境下对单体IP历史数据的研究的方法,通过对单体IP统计、预测,能快速的检测出导致网络异常流量的主机,并根据其流的类型判断,分类以发现其发生异常的原因并提供ACL策略,从而将网络流量控制在稳定的空间和时间之内,实验结果表明了此方法的可行性和有效性.     

Diagnosing Traffic Anomalies Using a Two-Phase Model

Network traffic anomalies are unusual changes in a network,so diagnosing anomalies is important for network management.Feature-based anomaly detection models (ab)normal network traffic behavior by analyzing packet header features.PCA-subspace method (Principal Component Analysis) has been verified as an efficient feature-based way in network-wide anomaly detection.Despite the powerful ability of PCA-subspace method for network-wide traffic detection,it cannot be effectively used for detection on a single link.In this paper,different from most works focusing on detection on flow-level traffic,based on observations of six traffic features for packet-level traffic,we propose a new approach B6SVM to detect anomalies for packet-level traffic on a single link.The basic idea of B6-SVM is to diagnose anomalies in a multi-dimensional view of traffic features using Support Vector Machine (SVM).Through two-phase classification,B6-SVM can detect anomalies with high detection rate and low false alarm rate.The test results demonstrate the effectiveness and potential of our technique in diagnosing anomalies.Further,compared to previous feature-based anomaly detection approaches,B6-SVM provides a framework to automatically identify possible anomalous types.The framework of B6-SVM is generic and therefore,we expect the derived insights will be helpful for similar future research efforts.     

骨干通信网络流量告警信息关联分析*

提出一种以骨干通信网络流量特征参数告警信息为基础的关联分析方法,首先提取通信网络中多个与异常事件相关的相对粗粒度的流量特征参数,将这些特征参数看做是随时间变化的信号（以下称为流量特征信号）,通过流量特征信号分析获得异常事件的多个告警信息;然后采用Apriori算法进行告警信息关联分析,获得告警信息与异常事件的关联规则。实际网络流量数据的分析表明：使用上述规则能有效地发现骨干通信网异常事件。     

An anomaly-based detection in ubiquitous network using the equilibrium state of the catastrophe theory

It has been increasingly important for Pervasive and Ubiquitous Applications (PUA) of the network traffic, especially anomaly detection which plays a critical role in enforcing a high protection level of the network against threats. In this paper, we present a network traffic anomaly detection method based on the catastrophe theory. In order to characterize the normal behavior of the network, we construct a profile of the normal network traffic by using an equilibrium surface of the catastrophe theory. When anomalies occur, the state of the network traffic will deviate from the normal equilibrium surface. Then, taking the normal equilibrium surface as a reference, we monitor the ongoing network traffic and we use a new index called as catastrophe distance to quantify the deviation. According to the decision theory, network traffic anomalies can be identified by the catastrophe distance. We evaluate the performance of our approach using the DARPA intrusion detection data set. Experiment results show that our approach is significantly effective on the network traffic anomaly detection.     

ClariSense+: An enhanced traffic anomaly explanation service using social network feeds

The explosive growth in social networks that publish real-time content begs the question of whether their feeds can complement traditional sensors to achieve augmented sensing capabilities. One such capability is to explain anomalous sensor readings. In our previous conference paper, we built an automated anomaly clarification service, called ClariSense, with the ability to explain sensor anomalies using social network feeds (from Twitter). In this extended work, we present an enhanced anomaly explanation system that augments our base algorithm by considering both (i) the credibility of social feeds and (ii) the spatial locality of detected anomalies. The work is geared specifically for describing small-footprint anomalies, such as vehicular traffic accidents. The original system used information gain to select more informative microblog items to explain physical sensor anomalies. In this paper, we show that significant improvements are achieved in our ability to explain small-footprint anomalies by accounting for information credibility and further discriminating among high-information-gain items according to the size of their spatial footprint. Hence, items that lack sufficient corroboration and items whose spatial footprint in the blogosphere is not specific to the approximate location of the physical anomaly receive less consideration. We briefly demonstrate the workings of such a system by considering a variety of real-world anomalous events, and comparing their causes, as identified by ClariSense+, to ground truth for validation. A more systematic evaluation of this work is done using vehicular traffic anomalies. Specifically, we consider real-time traffic flow feeds shared by the California traffic system. When flow anomalies are detected, our system automatically diagnoses their root cause by correlating the anomaly with feeds on Twitter. For evaluation purposes, the identified cause is then retroactively compared to official traffic and incident reports that we take as ground truth. Results show a great correspondence between our automatically selected explanations and ground-truth data.