A framework for expert system development in statistical quality control
Computers and Industrial Engineering
Back-propagation pattern recognizers for X¯ control charts: methodology and performance
Computers and Industrial Engineering
Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems
Computers and Industrial Engineering
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Feature-based recognition of control chart patterns
Computers and Industrial Engineering
Applying ICA and SVM to mixture control chart patterns recognition in a process
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Integrated use of ICA and ANN to recognize the mixture control chart patterns in a process
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Neural networks for detecting cyclic behavior in autocorrelated process
Computers and Industrial Engineering
Mining association rules from time series to explain failures in a hot-dip galvanizing steel line
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Industrial Engineering
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Computers and Industrial Engineering
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Recognition of various control chart patterns (CCPs) can significantly reduce the diagnostic search process. Feature-based approaches can facilitate efficient pattern recognition. The full potentiality of feature-based approaches can be achieved by using the optimal set of features. In this paper, a set of seven most useful features is selected using a classification and regression tree (CART)-based systematic approach for feature selection. Based on these features, eight most commonly observed CCPs are recognized using heuristic and artificial neural network (ANN) techniques. Extensive performance evaluation of the two types of recognizers reveals that both these recognizers result in higher recognition accuracy than the earlier reported feature-based recognizers. In this work, various features are extracted from the control chart plot of actual process data in such a way that their values become independent of the process mean and standard deviation. Thus, the developed feature-based CCP recognizers can be applicable to any general process.