Change Points Estimation in Shewhart Control Charts Using Fuzzy Clustering Approach

Change Points Estimation in Shewhart Control Charts Using Fuzzy Clustering Approach

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Part of #Change Points Estimation in Shewhart Control Charts Using Fuzzy Clustering Approach# :

Publishing year : 2012

Conference : Eighth International Industrial Engineering Conference

Number of pages : 5

Abstract: Control charts are mainly used to determine whether a process is in a state of statistical control or not. If the chart shows the out-of-control signal, it indicates a variation in the process. Aweakness of these charts is that they can not indicate the real time of the process change to determine the source of variations.In this article we explore the problem of finding change points in different types of control charts by modifying fuzzystatistical clustering algorithm. To implement the algorithm in the change points estimation, we considered the probability of membership of each observation to its assumed cluster and use it as a similarity measure in clustering. Using an objective function, process change points in different types of control charts were estimated using either fixed or variable sampling strategy. Several simulations are conducted to evaluate the performance of the proposed approach for some types of control charts.