In this research, an iterative approach is employed to recognize and classify control chart patterns. To do this, by taking new observations on the quality characteristic under consideration, the Maximum Likelihood Estimator of pattern parameters is first obtained and then the probability of each pattern is determined. Then using Bayes’ rule, probabilities are updated recursively. Finally, when...

0360-8352/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.cie.2012.10.009 q This manuscript was processed by Area Editor Mi ⇑ Corresponding author. Tel.: +61 3 52272268; fax: E-mail addresses: [email protected] (L. Xie) Gu), [email protected] (D. Li), [email protected] ( Tan), [email protected] (S. Nahavandi). Since abnormal control chart patterns (CCPs) a...

Each control chart pattern (CCP) has its own geometric shape and various related features can represent this shape. The shape features can represent the main characteristics of the original data in a condensed form. Different patterns can, therefore, be efficiently discriminated based on these shape features extracted from the control chart plot. In this paper, a feature-based heuristic approac...

Control charts primarily in the form of X chart are widely used to identify the situations when control actions will be needed for manufacturing systems. Various types of patterns are observed in control charts. Identification of these control chart patterns (CCPs) can provide clues to potential quality problems in the manufacturing process. This paper introduces a novel hybrid intelligent syst...

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 obje...

Neural networks because of their abilities are used to patterns recognition. In statistical process control charts, a common cause variation distort expected form of unnatural patterns and so detection of assignable causes efficiently and precisely in a real-time is difficult. Therefore it would be logical to propose models based neural networks for recognition and analysis of patterns in proce...

This paper presents a semi-supervised learning algorithm for a control chart pattern recognition system. A learning neural network is trained with labeled control chart patterns based on unsupervised learning. We then use the classification method based on a statistical correlation coefficient approach to test patterns. We find that the proposed semi-supervised learning algorithm is effective a...

This paper presents a control chart pattern recognition system using a statistical correlation coefficient method. Pattern recognition techniques have been widely applied to identify unnatural patterns in control charts. Most of them are capable of recognizing a single unnatural pattern for different abnormal types. However, before an unnatural pattern occurs, a change point from normal to abno...

neural networks because of their abilities are used to patterns recognition. in statistical process control charts, a common cause variation distort expected form of unnatural patterns and so detection of assignable causes efficiently and precisely in a real-time is difficult. therefore it would be logical to propose models based neural networks for recognition and analysis of patterns in proce...

Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characterist...