Cumulative Sum Control Charts for Monitoring Weibull‐distributed Time Between Events

The Weibull distribution can be used to effectively model many different failure mechanisms due to its inherent flexibility through the appropriate selection of a shape and a scale parameter. In this paper, we evaluate and compare the performance of three cumulative sum (CUSUM) control charts to monitor Weibull‐distributed time‐between‐event observations. The first two methods are the Weibull CUSUM chart and the exponential CUSUM (ECUSUM) chart. The latter is considered in literature to be robust to the assumption of the exponential distribution when observations have a Weibull distribution. For the third CUSUM chart included in this study, an adjustment in the design of the ECUSUM chart is used to account for the true underlying time‐between‐event distribution. This adjustment allows for the adjusted ECUSUM chart to be directly comparable to the Weibull CUSUM chart. By comparing the zero‐state average run length and average time to signal performance of the three charts, the ECUSUM chart is shown to be much less robust to departures from the exponential distribution than was previously claimed in the literature. We demonstrate the advantages of using one of the other two charts, which show surprisingly similar performance. Copyright © 2014 John Wiley & Sons, Ltd.     

ON‐LINE MONITORING OF POLLUTION CONCENTRATIONS WITH AUTOREGRESSIVE MOVING AVERAGE TIME SERIES

The concentration of aerosol particles, largely caused by traffic volume and often enhanced during temperature inversion episodes in the cold season, can be a concern for human health in the urban environment. This particulate matter is typically recorded as PM₁₀, the total mass of particles below 10 μm in diameter. It is suspected that, within the PM₁₀ class, ultrafine particles ( < 100 nm) may be responsible for causing respiratory and cardiovascular diseases. Because of their low mass, ultrafine particles are hard to detect, and researchers try to utilize PM₁₀ in combination with nitrogen oxides NO_x and other trace gases to monitor their dynamic evolution. To meet pollution standards set by national government and European Union regulation, the city of Klagenfurt, Austria, began using calcium magnesium acetate as a deicer on 11 January 2012, hoping to literally glue pollutants to the ground and thereby reducing pollution concentrations. With the statistical methodology developed in this article, the dynamic evolution of PM₁₀ and NO_x is traced for the time period starting 4 January and ending 25 January 2012, and a change in dynamics is found. The findings are based on on‐line monitoring procedures that sequentially detect structural breaks in the mean and the parameter values of an autoregressive moving average process. These are defined in terms of model residuals and one‐step ahead predictors. Theoretical properties are studied, and a simulation study shows that the proposed procedures work well in finite samples.     

Parameter change test for random coefficient integer-valued autoregressive processes with application to polio data analysis

Abstract.  In this paper, we consider the problem of testing for a parameter change in a first-order random coefficient integer-valued autoregressive [RCINAR(1)] model. We employ the cumulative sum (CUSUM) test based on the conditional least-squares and modified quasi-likelihood estimators. It is shown that under regularity conditions, the CUSUM test has the same limiting distribution as the supremum of the squares of independent Brownian bridges. The CUSUM test is then applied to the analysis of the monthly polio counts data set.     

A comparison of formulas for the design of cumulative sum control charts

A conventional cumulative sum control chart has a V‐mask where each arm makes an angle θ with the horizontal and has a lead distance d. These parameters are usually related to h (=dA tan(?), A is a scaling factor) and k(=A tan(?)). Two systems of formulas are examined for deriving h and k for selected values of the in control and out of control average run lengths. This is the first time an exhaustive comparison has been made. Copyright © 2009 John Wiley & Sons, Ltd.     

DDoS攻击实时检测防御系统的硬件实现

分布式拒绝服务攻击是因特网安全的头号威胁。针对DDoS攻击,本文介绍了一种基于MPC860和FPGA的实时检测防御系统的体系结构与实现原理,探讨了基于非参数累积和(CUSUM)算法检测新IP地址到达速率变化的DDoS攻击检测方法。实验结果表明该系统不仅实时检测准确性高、在线检测速度快、防御效果好,而且不损失网络信息吞吐量,保证了合法用户的正常访问。     

A distribution-free tabular CUSUM chart for autocorrelated data

A distribution-free tabular CUSUM chart called DFTC is designed to detect shifts in the mean of an autocorrelated process. The chart's Average Run Length (ARL) is approximated by generalizing Siegmund's ARL approximation for the conventional tabular CUSUM chart based on independent and identically distributed normal observations. Control limits for DFTC are computed from the generalized ARL approximation. Also discussed are the choice of reference value and the use of batch means to handle highly correlated processes. The performance of DFTC compared favorably with that of other distribution-free procedures in stationary test processes having various types of autocorrelation functions as well as normal or nonnormal marginals.     

与控制点方法论相结合的V-mask累积和控制图

V-mask累积和控制图虽然能够有效地监控过程中发生的微小偏移,但是因为它需要存储大量统计量且计算时间较长,所以在计算机中实施起来比较困难.为了解决这一问题,介绍了将控制点方法论应用于V-mask累积和控制图这一方法,并通过实例来进一步说明.结果表明,与控制点方法论结合的控制图减少了存储量,缩短了计算时间,而且将在顾客满意度控制中得到广泛应用.     

Estimating the Change Point of the Process Fraction Non‐conforming with a Monotonic Change Disturbance in SPC

Knowing when a process has changed would simplify the search for and identification of the special cause. In this paper, we propose a maximum‐likelihood estimator for the change point of the process fraction non‐conforming without requiring knowledge of the exact change type a priori. Instead, we assume the type of change present belongs to a family of monotonic changes. We compare the proposed change‐point estimator to the maximum‐likelihood estimator for the process change point derived under a simple step change assumption. We do this for a number of monotonic change types and following a signal from a binomial cumulative sum (CUSUM) control chart. We conclude that it is better to use the proposed change point estimator when the type of change present is only known to be monotonic. The results show that the proposed estimator provides process engineers with an accurate and useful estimate of the time of the process change regardless of the type of monotonic change that may be present. Copyright © 2006 John Wiley & Sons, Ltd.     

基于流量统计特征的端口扫描检测算法

根据网络流量的统计特征提出一种慢速端口扫描行为检测算法，以主机数和端口数的比值及被访问主机端口集合之间的相似度为基础，采用非参数累积和CUSUM算法及小波变换方法对流量统计特征进行分析，进而判断是否存在端口扫描行为。实验结果表明，所提取的网络流量特征及算法可以有效地检测异常行为，该方法和Snort相比较具有低的漏报率和误报率。