NDLI: Statistical process control with autocorrelated data using neural networks

Content Provider	IEEE Xplore Digital Library
Author	Yuangyai, C. Abrahams, R.
Copyright Year	2011
Description	Author affiliation: Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand (Yuangyai, C.) \|\| Department of Industrial and Manufacturing, Pennsylvania state university, University Park, USA (Abrahams, R.)
Abstract	Statistical Process Control (SPC) is widely used for monitoring the performance of processes in manufacturing. Traditional SPC methods require trained individuals to read data which results in slow and limited detection. Much research has been devoted into developing an online automated system for SPC, so that the abnormality can be detected quickly and corrected by the process operation. To build a system as such, artificial neural networks (ANN) are widely used as tools where complex patterns can be difficult to recognize. Many research projects involve using random data patterns for training and recognition of patterns for ANN/SPC applications. However, many manufacturing processes involve autocorrelated data, to determine the effect of autocorrelated data, green sand data was analyzed and a neural network was built and trained to analyze a number of out of control patterns. Overall, the network performed best for detecting larger mean shifts.
Starting Page	283
Ending Page	287
File Size	1162990
Page Count	5
File Format	PDF
ISBN	9781457706264
e-ISBN	9781457706288
DOI	10.1109/ICQR.2011.6031726
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-14
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Correlation Neural networks Time series analysis Statistical Process Control Process control Neural Networks Mathematical model Autoregressive processes Autocorrelated Data
Content Type	Text
Resource Type	Article

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