A new adaptive CUSUM control chart for detecting the multivariate process mean

We propose a new multivariate CUSUM control chart, which is based on self adaption of its reference value according to the information from current process readings, to quickly detect the multivariate process mean shifts. By specifying the minimum magnitude of the process mean shift in terms of its non‐centrality parameter, our proposed control chart can achieve an overall performance for detecting a particular range of shifts. This adaptive feature of our method is based on two EWMA operators to estimate the current process mean level and make the detection at each step be approximately optimal. Moreover, we compare our chart with the conventional multivariate CUSUM chart. The advantages of our control chart detection for range shifts over the existing charts are greatly improved. The Markovian chain method, through which the average run length can be computed, is also presented. Copyright © 2010 John Wiley & Sons, Ltd.     

Monitoring nonlinear profiles using a wavelet-based distribution-free CUSUM chart

WDFTC is a wavelet-based distribution-free CUSUM chart for detecting shifts in the mean of a profile with noisy components. Exploiting a discrete wavelet transform (DWT) of the mean in-control profile, WDFTC selects a reduced-dimension vector of the associated DWT components from which the mean in-control profile can be approximated with minimal weighted relative reconstruction error. Based on randomly sampled Phase I (in-control) profiles, the covariance matrix of the corresponding reduced-dimension DWT vectors is estimated using a matrix-regularisation method; then the DWT vectors are aggregated (batched) so that the non-overlapping batch means of the reduced-dimension DWT vectors have manageable covariances. To monitor shifts in the mean profile during Phase II operation, WDFTC computes a Hotelling's T ²-type statistic from successive non-overlapping batch means and applies a CUSUM procedure to those statistics, where the associated control limits are evaluated analytically from the Phase I data. Experimentation with several normal and non-normal test processes revealed that WDFTC was competitive with existing profile-monitoring schemes.     

Binomial CUSUM chart with curtailment

The binomial cumulative sum (CUSUM) chart has been widely used to monitor the fraction nonconforming (p) of a process. It is a powerful procedure for detecting small and moderate p shifts. This article proposes a binomial CUSUM control chart using curtailment technique (Curt_CUSUM chart in short). The new chart is able to improve the overall detection effectiveness while holding the false alarm rate at a specified level. The results of the comparative studies show that, on average, the Curt_CUSUM chart is more effective than the CUSUM chart without curtailment by 30%, in terms of Average Number of Defectives, under different circumstances. The Curt_CUSUM chart can be applied to a 100% inspection as well as a general random sampling inspection.     

A comparative study of CCC and CUSUM charts

The cumulative count of conforming (CCC) chart is a new type of control chart used for the monitoring of high-quality processes. Instead of counting the number of non-conforming items in samples of fixed size, the cumulative number of conforming items between two non-conforming items is monitored. The CCC chart is convenient to use in a modern manufacturing environment where the product is inspected individually and automatically. The CCC chart has sometimes been confused with the cumulative sum (CUSUM) chart which has been shown to be more sensitive than the traditional Shewhart chart for small process shifts. In this paper the uses of these two types of charts are compared. It shown by numerical illustrations and analytical results that the two charts function in entirely different ways. However, the CUSUM concept can be applied to cumulative counts used in the CCC chart to improve its sensitivity for small process shifts when the process is producing at a very low non-conforming rate. © 1998 John Wiley & Sons, Ltd.     

联合监控均值和方差的累积和控制图优化设计

为了提高监测均值和方差微小偏移的敏感度,围绕生产过程质量控制,建立同时监控均值和方差的累积和控制图.模型考虑均值和方差的变化,针对生产过程中的微小偏差,提出了一个新的累积和控制图,并给出了基于马尔可夫链理论的新控制图的平均链长计算方法.编程求解后对比文献中各控制图的平均链长数据以及更换变量数值改进控制图,通过计算变动比率得出新控制图的检测力度在不同偏移力度下都明显优于其他控制图方法.     

Control charting methods for autocorrelated cyber vulnerability data

Control charting cyber vulnerabilities is challenging because the same vulnerabilities can remain from period to period. Also, hosts (personal computers, servers, printers, etc.) are often scanned infrequently and can be unavailable during scanning. To address these challenges, control charting of the period-to-period demerits per host using a hybrid moving centerline residual-based and adjusted demerit (MCRAD) chart is proposed. The intent is to direct limited administrator resources to unusual cases when automatic patching is insufficient. The proposed chart is shown to offer superior average run length performance compared with three alternative methods from the literature. The methods are illustrated using three datasets.     

A mixed control chart to monitor the process

In this paper, we propose a mixed control chart to monitor the process quality using attribute data combined with variable data. The proposed control chart proceeds like an np control chart based on the number of nonconforming parts but requires variable data only when the decision is indeterminate. The control coefficients are determined by considering the in-control and the out-of-control average run lengths for various specified parameters. The extensive tables are provided for the industrial use. The advantages of the proposed control chart are discussed over the traditional np control chart.     

A control scheme integrating the T chart and TCUSUM chart

This article proposes an integrated scheme (T&TCUSUM chart) which combines a Shewhart T chart and a TCUSUM chart (a CUSUM‐type T chart) to monitor the time interval T between the occurrences of an event or the time between events. The performance studies show that the T&TCUSUM chart can effectively improve the overall performance over the entire T shift range. On average, it is more effective than the T chart by 26.66% and the TCUSUM chart by 14.12%. Moreover, the T&TCUSUM chart performs more consistently than other charts for the detection of either small or large T shifts, because it has the strength of both the T chart (more sensitive to large shifts) and the TCUSUM chart (more sensitive to small shifts). The implementation of the new chart is almost as easy as the operation of a TCUSUM chart. Copyright © 2010 John Wiley & Sons, Ltd.     

Support Vector Regression control charts for multivariate nonlinear autocorrelated processes

Statistical process control charts are one of the most widely used techniques in industry and laboratories that allow monitoring of systems against faults. To control multivariate processes, most classical charts need to model process structure and assume that variables are linearly and independently distributed. This study proposes to use a nonparametric method named Support Vector Regression to construct several control charts that allow monitoring of multivariate nonlinear autocorrelated processes. Also although most statistical quality control techniques focused on detecting mean shifts, this research investigates detection of different parameter shifts. Based on simulation results, the study shows that, with a controlled robustness, the charts are able to detect the different applied disturbances. Moreover in comparison to Artificial Neural Networks control chart, the proposed charts are especially more effective in detecting faults affecting the process variance.     

Computing the Percentage Points of the Run-Length Distributions of Multivariate CUSUM Control Charts

Multivariate CUSUM control charts are often used instead of the standard Hotelling's control charts in many practical problems when detection of small shifts in the process mean is important. However, design of multivariate CUSUM control charts are usually based on the average run length (ARL). In this work, we will compute the percentage points of the run-length distributions of two multivariate CUSUM control charts. It will be shown that interpretations based on ARL can be misleading since the in-control run-length distribution of a multivariate CUSUM is highly skewed. On the other hand, the percentage points of the run-length distribution provide additional information such as the median run length, early false out-of-control signals, and the skewness of the run-length distribution for a particular scheme. These extra information might provide quality control engineers further knowledge of a particular multivariate CUSUM control chart scheme.     

A combined synthetic&X chart for monitoring the process mean

The Shewhart X chart (or X chart) is widely used to monitor the mean of a quality characteristic x. This chart decides the process status based on the magnitude of the sample mean x and is effective for detecting large mean shifts. The synthetic chart is also a Shewhart type chart for monitoring the process mean, but it utilises the information about the time interval between two nonconforming samples. Here a sample is nonconforming if its x value falls beyond the predetermined warning limits. Unlike the X chart, the synthetic chart is more powerful to detect small shifts. The applications of the X and synthetic charts cover a wide variety of manufacturing processes and production lines, e.g., the monitoring of the mean values of the inside diameter of a piston-ring, the viscosity of aircraft paint, the resistivity of silicon wafers. This article proposes a combined scheme, the Syn-X chart, that comprises a synthetic chart and an X chart. The results of the performance studies show that the Syn-X chart always outperforms the individual X chart and synthetic chart under different conditions. It is more effective than the X chart and synthetic chart by 47% and 20%, respectively, over the wide range of mean shift values in different experiment runs.