Fuzzy T2 Hotelling (T_f^2 ) Control Chart

Multivariate Multinomial T2 Control Chart using Fuzzy Approach

Economic- Statistical design of T2 control chart with the VSSC scheme

T2 control charts are used to monitor a process when more than one quality variable associated with process is being observed. Recent studies have shown that using variable sample size (VSS) schemes result in charts with more statistical power when detecting small to moderate shifts in the process mean vector. This paper presents an economic- statistical design of T2 control charts with variabl...

A New Bootstrap Based Algorithm for Hotelling’s T2 Multivariate Control Chart

Normality is a common assumption for many quality control charts. One should expect misleading results once this assumption is violated. In order to avoid this pitfall, we need to evaluate this assumption prior to the use of control charts which require normality assumption. However, in certain cases either this assumption is overlooked or it is hard to check. Robust control charts and bootstra...

Control Chart Recognition Patterns using Fuzzy Rule-Based System

Control Chart Patterns (CCPs) recognition is one the most important concepts in control chart application. Relating the patterns exhibited on the control chart to assignable causes is an ambiguous and vague task especially when multiple patterns co-exist. In this study, a fuzzy rule-based system is developed for X ̅ control charts to prioritize the control chart causes based on the accumulated e...

A Literature Review on the Fuzzy Control Chart; Classifications & Analysis

Quality control plays an important role in increasing the product quality. Fuzzy control charts are more sensitive than Shewhart control chart. Hence, the correct use of fuzzy control chart leads to producing better-quality products. This area is complex because it involves a large scope of industries, and information is not well organized. In this research, we provide a literature review of th...