It was hosted by Professor C. The workshop was jointly organized by Professors H. Lenz, C. Mastrangelo, W. Schmid and P. The twenty-seven papers in this volume were carefully selected by the scientific program committee, reviewed by its members, revised by the authors and, finally, adapted for this volume by the editors. The book is divided into two parts: Part I "On-line Control" covers fields like control charting, monitoring and surveillance as well as acceptance sampling.
In the presence of dependence the authors usually assume dependencies in the form of autocorrelated and normally distributed data. However, there exist many other types of dependencies which are described by other mathematical models. The question arises then, how classical control charts are robust to different types of dependencies. This problem has been sufficiently well discussed for the case of autocorrelated and normal data.
In the paper we use the concept of copulas to model dependencies of other types. We use Monte Carlo simulation experiments to investigate the impact of type and strength of dependence in data on the value of the ARL of Shewhart control charts. Misleading signals MS correspond to the misinterpretation of a shift in the process mean variance as a shift in the process variance mean.
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MS occur when: The individual chart for the mean triggers a signal before the one for the variance, even though the process mean is on-target and the variance is off-target;. The individual chart for the variance triggers a signal before the one for the mean, although the variance is in-control and the process mean is out-of-control. MS can lead to a misdiagnosis of assignable causes and to incorrect actions to bring the process back to target.
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Unsurprisingly, the performance assessment of simultaneous schemes for the process mean and variance requires not only the use of run length RL related performance measures, but also the probability of misleading signals PMS. This assessment is done by means of some stochastic ordering results and some illustrations. The standard task within SPC is the detection of an unforeseen shift in the mean level of the sequence of typically normally distributed random variables.
Only some papers deal with a not considerably less common pattern in industrial practice: gradual changes because of tool wear or similar causes. In the small list of the currently available papers both existing control charts for the mean under drift are studied and new ones are created. It is worth noting that except Gan J Stat Comput Simul —, ; Statistician —84, no convincing numerical algorithms are presented for calculating characteristics for control charts under and for drift.
The good message is that mean level control charts are also suited for detecting drifts. In this paper we consider a general family of EWMA charts for an arbitrary parameter of the target process. Our assumptions on the target process are very weak and they are usually satisfied if it is stationary. In the case of the EWMA chart with exact variance the in-control variance of the EWMA recursion at time t is used for the decision at time t while in the case of the asymptotic variance at each time point the limit of the in-control variance of the EWMA chart for t tending to infinity is applied.
It is analyzed how the distributions of the corresponding run lengths behave if the smoothing parameter tends to zero. We show that the distribution of the run length of the EWMA chart based on the exact variance converges to the distribution of the run length of the repeated significance test while the limit of the EWMA scheme based on the asymptotic variance is degenerate.
It is either 0 or 1. This result underlines the weakness of the schemes based on the asymptotic variance if the smoothing parameter is small. Moreover, several properties of the limit chart, i. When controlling a process mean one can achieve optimal performance in terms of the criterion of average run length ARL by using a CUSUM control chart rather than a Shewhart control chart, although for very large shifts the Shewhart control chart is equivalent to a CUSUM chart.
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Recent advances of multivariate Statistical Process Control SPC show that the introduction of Principal Component Analysis PCA methods for reduction of process data is a promising area in system monitoring and fault diagnosis. The advantage of these techniques is to identify sets of variables which describe the key variations of the operating data and which allow process handling and control based on a reduced number of charts.
However, because the basic PCA method stipulates that relationships between process characteristics are linear, the application of such techniques to nonlinear systems that undergo many changes has been limited in many real cases. In order to overcome this issue, some recent studies suggested the use of nonlinear adaptive PCA methods in order to track process variation and detect abnormal events at early stages. For this reason, this study develops and analyses an online Kernel PCA chart as a key technique to model nonlinear systems and to monitor the evolution of non-stationary processes.
Results based on an analysis of a simulated process show that the control chart is robust and provides a reduced rate of false alarms with high fault detection abilities. This paper is developed from Higashide et al. Front Stat Qual Control —84, Automatic process control APC is frequently used in the semiconductor manufacturing process; however, statistical process control SPC is also needed to control the APC controller. Through case studies on the semiconductor manufacturing process, the remarks above are discussed.
Our proposals for the integration of SPC and APC are as follows: a The process rate is used as the control characteristic to control the between-subgroup variation. In the production of chemicals, a process adjustment such as feedback control is frequently used to reduce process variability. It is very important to judge whether or not the adjustment should be done automatically because an automatic process control APC system requires a large capital investment. This paper presents the determination of the adjustment timing on the basis of the process capability, and control charts combining information about the state of statistical control and process capability are also presented for the judgment of adjustment timing.
Practitioners can assess both the adjustment interval and the number of adjustments by simulation or trial using the presented method. Moreover, the information is very useful for judging whether or not the automatic adjustment system should be introduced. We examine one of the methods implemented by the U. The EARS W2r method allows one to monitor the proportion of counts of a particular syndrome at a facility relative to the total number of visits.
We investigate the performance of the W2r method with negative binomial inputs designed using an empirical recurrence interval RI.
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An adaptive threshold monitoring method is studied based on estimating the underlying negative binomial distributions, then converting the current counts to a Z -score through a p -value. We study the effect of the input distributions on the upper thresholds required for both Shewhart and exponentially weighted moving average EWMA versions of the W2r and adaptive threshold methods.
We simulate 1-week outbreaks and compare the outbreak detection properties of the methods. Motivated by the applications in healthcare surveillance, this paper discusses the spatiotemporal surveillance problem of detecting the mean change of Poisson count data in a non-homogeneous population environment.
Through Monte Carlo simulations, we investigate several likelihood ratio-based approaches and compare them under various scenarios depending on four factors 1 the population trend, 2 the change time, 3 the change magnitude, and 4 the change coverage. Most literature of spatiotemporal surveillance evaluated the performance based on the average run length if a change occurs at the beginning of surveillance, which is often noted by ARL 1.
On the other hand, our comparison is based on the average run length after the time when a change occurs later.
Our simulation study shows that no method is uniformly better than others in all scenarios. It is found that the difference between generalized likelihood ratios GLR approach and weighted likelihood ratios WLR approach depends on population trend and change time, not the change coverage or change magnitude. Hospital-associated infections are a major concern in hospitals due to the potential loss of life and increased treatment costs.
Monitoring the incidences of infections is an established part of quality maintenance programs for infectious disease departments in hospitals. However, traditional methods of analysis are often inadequate since the incidences of infections occur at relatively low rates. The g-type control chart is ideal for use since it monitors days between infections.
However, users of the control charts find the g-type chart counter-intuitive and would prefer to use a u-chart or even a control chart for individuals. In this paper, we investigate g-type chart alternatives and how these charts may be applied to infection control surveillance data from Seattle Childrens Hospital. Frontiers in Statistical Quality Control 4.
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Front Matter Pages I-X. Front Matter Pages Attriables Acceptance Sampling Plans. Pages Kaijage, J.