A Semi‐empirical Bayesian Chart to Monitor Weibull Percentiles

A New Control Chart to Monitor Mean Shifts of Bi-Variate Quality Control Processes

A New Uni-attribute Control Chart to Monitor the Number of Nonconformities

The most well-known uni-arribute control chart used to monitor the number of nonconformities per unit is the Shewhart type C-chart. In this paper, a new method is proposed in an attempt to reduce the false alarm rate in the C-chart. To do this, the decision on beliefs (DOB) concept is first uti [1] Corresponding author e-mail: [email protected]   lized to design an iterative method, where...

A bootstrap control chart for Birnbaum-Saunders percentiles

The problem of detecting a shift in the percentile of a Birnbaum–Saunders population in a process monitoring situation is considered. For example, such problems may arise when the quality characteristic of interest is tensile strength or breaking stress. The parametric bootstrap method is used to develop a quality control chart for monitoring percentiles when process measurements have a Birnbau...

A Control Chart to Monitor a Multivariate Binomial Process

The most applied statistical methods for monitoring multivariate attribute processes have been developed assuming that they have a multinomial distribution, see e.g. Marcucci (1985) and Cassady and Nachlas (2006). However this assumption is not always reasonable; indeed, it is more general and correct to suppose that in each item it is possible to identify one or more of k ordered and not mutua...

A Bayesian Approach for the Recognition of Control Chart Patterns

In this research, an iterative approach is employed to recognize and classify control chart patterns. To do this, by taking new observations on the quality characteristic under consideration, the Maximum Likelihood Estimator of pattern parameters is first obtained and then the probability of each pattern is determined. Then using Bayes’ rule, probabilities are updated recursively. Finally, when...