Journal of Quality Engineering and Management

Abstract Todays, the performance of a process or the quality of a product in conditions of uncertainty and under distributions from the exponential distribution family is evaluated by a fuzzy communication model with binary data called fuzzy generalized linear profiles. Generalized linear profiles are a type of nonlinear profile in which process observations follow the Bernoulli or binomial distribution. In this research, approaches In order to monitor fuzzy generalized linear profiles in phase 2, we propose. The main purpose of this paper is to monitor the fuzzy statistical process to detect the time of occurrence of changes in processes as fuzzy change point and based on the principle of maximum likelihood (MLE). Is based on fuzzy observations. Performance of the proposed method for monitoring fuzzy generalized linear profiles based on the probability of an out-of-control signal using the fuzzy control diagram (FEWMA) and then estimating the fuzzy change point for the simulated data and real data.

Abstract Sometimes, a quality characteristic is expressed as a specific functional relationship, which is referred to as a profile in statistical quality control. In this paper, linear profile analysis in Phase I control charts is investigated from a new perspective. In statistical quality control, it is usually assumed that the process has a constant mean. However, there may be processes where the mean of the parameters is not constant over time, and this variability is an inherent characteristic of the process. For controlling such processes, most existing control chart methods are ineffective. To statistically monitor such processes in Phase I, it is necessary to segment the process into sections where it is stable and consistent before performing statistical process control. For process segmentation, few studies have proposed the use of time-based clustering techniques, and the existing studies have mainly focused on univariate data. In this paper, we develop a process segmentation method for profile monitoring. Different segmentation algorithms for functional data are compared, and the results are reported. Studies show that the functional data clustering algorithm based on the Fuzzy c-means method performs well for profile segmentation.