NDLI: A Neural Network ensemble for classifying source(s) in multivariate manufacturing processes

Content Provider	IEEE Xplore Digital Library
Author	Jian-Bo Yu Li-Feng Xi
Copyright Year	2007
Description	Author affiliation: Shanghai Jiaotong Univ., Shanghai (Jian-Bo Yu; Li-Feng Xi)
Abstract	In multivariate statistical process control, most multivariate quality control charts are shown to be effective in detecting out-of-control signals based upon an overall statistics. But these charts do not relieve the need for pinpointing the source(s) of the out-of-control signals. Neural networks (NNs) have excellent noise tolerance and high pattern identification capability, which have been used successfully in MSPC. This study proposed a selective NNs ensemble approach DPSOEN, where several NNs selected are jointly used to classify source(s) of out-of-control signals in multivariate processes. Extensive experiment is also carried out to examine effects of six statistical features on the performance of DPSOEN. The investigation proposed a heuristic approach for applying DPSOEN as an effective tool to identify abnormal source(s) in bivariate SPC with potential application for MSPC in general.
Starting Page	1246
Ending Page	1250
File Size	503345
Page Count	5
File Format	PDF
ISBN	9781424415281
DOI	10.1109/IEEM.2007.4419391
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-02
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Statistical analysis Artificial neural networks Neural network ensemble Statistics Multivariate manufacturing processes Manufacturing processes Neural networks Control charts Quality control Signal processing Multivariate control chart Signal detection Monitoring
Content Type	Text
Resource Type	Article

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