Advisory system for control chart selection
Computers and Industrial Engineering
The application of expert systems to manufacturing process control
Computers and Industrial Engineering
A framework for expert system development in statistical quality control
Computers and Industrial Engineering
Synthetic neural networks for process control
Computers and Industrial Engineering
Group technology and expert systems concepts applied to statistical process control in small-batch manufacturing
Design of a knowledge-based expert system for statistical process control
Computers and Industrial Engineering
Back-propagation pattern recognizers for X¯ control charts: methodology and performance
Computers and Industrial Engineering
Back propagation artificial neural networks for the analysis of quality control charts
Proceedings of the 15th annual conference on Computers and industrial engineering
A multi-layer neural network model for detecting changes in the process mean
Computers and Industrial Engineering
Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems
Computers and Industrial Engineering
Unnatural pattern recognition on control charts using correlation analysis techniques
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Computers and Industrial Engineering
Automated unnatural pattern recognition on control charts using correlation analysis techniques
Computers and Industrial Engineering
Control charts for monitoring processes with autocorrelated data
Proceedings of second world congress on Nonlinear analysts
A neural network applied to pattern recognition in statistical process control
Proceedings of the 23rd international conference on on Computers and industrial engineering
A neural network based model for abnormal pattern recognition of control charts
Computers and Industrial Engineering
A neural network-based procedure for the monitoring of exponential mean
Computers and Industrial Engineering
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Identification, Prediction, and Control
Neural Networks for Identification, Prediction, and Control
Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes
Computers and Industrial Engineering
Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks
Computers and Industrial Engineering
Artificial neural networks to classify mean shifts from multivariate χ2 chart signals
Computers and Industrial Engineering
Feature-based recognition of control chart patterns
Computers and Industrial Engineering
Real time statistical process advisor for effective quality control
Decision Support Systems
A hybrid fuzzy adaptive sampling - Run rules for Shewhart control charts
Information Sciences: an International Journal
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Recognition of control chart patterns using improved selection of features
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
A control chart pattern recognition system using a statistical correlation coefficient method
Computers and Industrial Engineering
Features extraction and analysis for classifying causable patterns in control charts
Computers and Industrial Engineering
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
SPC without Control Limits and Normality Assumption: A New Method
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Simultaneous process mean and variance monitoring using artificial neural networks
Computers and Industrial Engineering
Information Sciences: an International Journal
Control chart pattern recognition using a novel hybrid intelligent method
Applied Soft Computing
Using neural networks to detect the bivariate process variance shifts pattern
Computers and Industrial Engineering
Data mining model-based control charts for multivariate and autocorrelated processes
Expert Systems with Applications: An International Journal
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Identification and interpretation of manufacturing process patterns through neural networks
Mathematical and Computer Modelling: An International Journal
Computers and Industrial Engineering
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Control Chart Pattern Recognition (CCPR) is a critical task in Statistical Process Control (SPC). Abnormal patterns exhibited in control charts can be associated with certain assignable causes adversely affecting the process stability. Abundant literature treats the detection of different Control Chart Patterns (CCPs). In fact, numerous CCPR studies have been developed according to various objectives and hypotheses. Despite the widespread literature on this topic, efforts to review and analyze research on CCPR are very limited. For this reason, this survey paper proposes a new conceptual classification scheme, based on content analysis method, to classify past and current developments in CCPR research. More than 120 papers published on CCPR studies within 1991-2010 were classified and analyzed. Major findings of this survey include the following. (1) The most popular CCPR studies deal with independently and identically distributed process data. (2) Some recent studies on identification of mean shifts or/and variance shifts of a multivariate process are based on innovative techniques. (3) The percentage of studies that address concurrent pattern identification is increasing. (4) The majority of the reviewed articles use Artificial Neural Network (ANN) approach. Feature-based techniques, in particular wavelet-denoise, are investigated for improving the recognition performance of ANN. For the same reason, there is a general trend followed by many authors who propose hybrid, modular and integrated ANN recognizer designs combined with decision tree learning, particle swarm optimization, etc. (5) There are two main categories of performance criteria used to evaluate CCPR approaches: statistical criteria that are related to two conventional Average Run Length (ARL) measures, and recognition-accuracy criteria, which are not based on these ARL measures. The most applied criteria are recognition-accuracy criteria, mainly for ANN-based approaches. Performance criteria which are related to ARL measures are insufficient and inappropriate in the case of concurrent pattern identification. Finally, this paper briefly discusses some future research directions and our perspectives.