Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207540701241883
Title: Toward identifying the source of mean shifts in multivariate SPC: A neural network approach
Authors: Hwarng, H.B. 
Keywords: Back-propagation
Bivariate
Control charts
Hotelling T 2 chart
Multivariate SPC
Neural networks
Issue Date: 2008
Source: Hwarng, H.B. (2008). Toward identifying the source of mean shifts in multivariate SPC: A neural network approach. International Journal of Production Research 46 (20) : 5531-5559. ScholarBank@NUS Repository. https://doi.org/10.1080/00207540701241883
Abstract: To identify the source(s) of process shifts under a multivariate setting is a challenging problem. Though some statistical techniques have been proposed, they are limited or restricted in their level of success and ease of use. In this paper, we propose a neural-network based identifier (NNI) to detect process mean shifts as well as indicate the variable(s) responsible for the shifts in a process where variables are correlated. Various network configurations and training strategies were investigated to develop an effective network. This research demonstrates how the NNI with a simple network structure, i.e. without any hidden layers, can perform superiorly to the Hotelling T 2 chart and comparably to the MEWMA chart in detecting small to moderate shifts for bivariate processes. The run length analysis also indicates that the NNI performs much more stably than the Hotelling T 2 chart and the MEWMA chart. One of the great advantages of this approach is that the proposed identifier, aided with the NNI output chart, can indicate the source(s) of the shift(s), i.e. the variable(s) responsible for the shift(s). The NNI output chart allows this monitoring scheme to easily interpret the underlying structures of the process variables.
Source Title: International Journal of Production Research
URI: http://scholarbank.nus.edu.sg/handle/10635/44044
ISSN: 00207543
DOI: 10.1080/00207540701241883
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

21
checked on Dec 7, 2017

WEB OF SCIENCETM
Citations

15
checked on Nov 23, 2017

Page view(s)

63
checked on Dec 11, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.