Paper Info
Title
Synergistic-ANN Recognizers for Monitoring and Diagnosis of Multivariate Process Shift Patterns
Abstract
An intelligent control chart pattern recognition system is essential for efficient monitoring and diagnosis process variation in automated manufacturing environment. Artificial neural networks (ANN) have been applied for automated recognition of control chart patterns since the last 20 years. In early study, the development of control chart patterns recognizers was mainly based on generalized-ANN model. There has been an increasing trend among researchers to move beyond generalized recognizer particularly for addressing complex recognition tasks. However, the existing works mainly focus on univariate process cases. This paper aims to investigate an effective synergistic-ANN model for on-line monitoring and diagnosis multivariate process patterns. The recognition performances of a generalized-ANN and the parallel distributed ANN recognizers for learning dynamic patterns of multivariate process patterns were discussed.
Year
DOI
Venue
2009
10.1109/SoCPaR.2009.61
SoCPaR
Keywords
Field
DocType
complex recognition task,diagnosis multivariate process pattern,multivariate process pattern,univariate process case,recognition system,multivariate process shift patterns,control chart pattern,diagnosis process variation,control chart patterns recognizers,recognition performance,synergistic-ann recognizers,automated recognition,artificial neural network,process variation,statistical process control,process control,control charts,artificial neural networks,neural nets,pattern recognition,control chart,intelligent control
Intelligent control,Computer science,Chart pattern,Control chart,Artificial intelligence,Process control,Statistical process control,Univariate,Artificial neural network,Process patterns,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
2
Name
Order
Citations
PageRank
Ibrahim Masood121.14
Adnan Hassan2143.48