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Paper Information

Journal:   JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL   JANUARY 2010 , Volume 6 , Number 10; Page(s) 16 To 30.
 
Paper: 

IDENTIFYING THE TIME OF A STEP CHANGE WITH MEWMA CONTROL CHARTS BY ARTIFICIAL NEURAL NETWORK

 
 
Author(s):  AHMADZADEH F.*, NOUR ALSANA R., SAGHAI A.
 
* DEP. OF INDUSTRIAL ENGINEERING, ISLAMIC AZAD UNIVERSITY, KARAJ BRANCH, KARAJ, IRAN
 
Abstract: 

Quality control charts have proven to be very effective in detecting out of control signals. It is very important to practitioners to determine at what point in the past the signal was initiated. If a control chart signals a change in the process parameter, identifying the time of the change will substantially help the signal diagnostics procedure since it simplifies the search for special causes. In this paper the researchers have proposed the observations following multivariate normal distribution. They have used Multivariate Exponentially Weighted Moving Average (MEWMA) control chart to detect signals. This research provides two ways to detect the change point, first MLE, and then neural network is used to identify the time of the change in the parameters (mean) in the past. The researchers intended to assess the performance of two approaches and compare them through computer simulation experiments. The results show that neural network performs effectively and equally well for the whole process dimensions while shift magnitudes are considered. Thus, the neural network provides process engineers with an accurate and useful estimate of the actual time of the change in the process mean.

 
Keyword(s): STATISTICAL PROCESS CONTROL, MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING AVERAGE (MEWMA), CHANGE POINT ESTIMATION, MONTE CARLO SIMULATION, NEURAL NETWORK, MAXIMUM LIKELIHOOD ESTIMATOR ( MLE)
 
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