Article1993

X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING

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Advanced Statistical Process Monitoring Neural Networks and Applications Fault Detection and Control Systems

Abstract

Back-propagation pattern recognizers (BPPR) are proposed to identify unnatural patterns exhibited on Shewhart control charts. These unnatural patterns, such as cycles and trends, can provide valuable information for real-time process control. In a computer-integrated manufacturing environment, the operator need not routinely monitor the control chart but, rather, can be alerted to patterns by a computer signal generated by the proposed algorithm. In this paper, an off-line analysis is performed to investigate the training and learning speed of these BPPRs on simulated X¯ data. The best configuration of the network is further tested to demonstrate the classification capability of the proposed BPPR.

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References

1984The Shewhart Control Chart—Tests for Special Causes 1985Interpreting Shewhart X Control Charts 1993Back-propagation pattern recognizers for control charts: Methodology and performance 1958Properties of Control Chart Zone Tests

Cited by

2005A hybrid learning-based model for on-line detection and analysis of control chart patterns 1996A neural fuzzy control chart for detecting and classifying process mean shifts 1998Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters 1999On-line control chart pattern detection and discrimination—a neural network approach 1999A neural network approach to characterize pattern parameters in process control charts 2004Artificial neural networks to classify mean shifts from multivariate χ2 chart signals 2001Control chart pattern recognition using back propagation artificial neural networks 2002Mean shifts detection and classification in multivariate process: a neural-fuzzy approach

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