AUTOMATED UNNATURAL PATTERN RECOGNITION ON CONTROL CHARTS USING CORRELATION ANALYSIS TECHNIQUES

Pattern recognition techniques are currently pursued to identify unnatural patterns on quality control charts. This approach has been shown to enhance the ability to utilize the information of the chart more effectively than conventional run rules. This paper presents analysis and development of a p...

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Bibliographic Details
Published inComputers & industrial engineering Vol. 32; no. 3; pp. 679 - 690
Main Authors AL-GHANIM, AMJED M, LUDEMAN, LONNIE C
Format Journal Article
LanguageEnglish
Published Seoul Elsevier Ltd 01.07.1997
Oxford Pergamon Press
New York, NY Pergamon Press Inc
Subjects
Algorithms
Applied sciences
Artificial intelligence
Automation
Computer science; control theory; systems
Control charts
Correlation analysis
Exact sciences and technology
Operational research and scientific management
Operational research. Management science
Pattern recognition systems
Pattern recognition. Digital image processing. Computational geometry
Quality control
Reliability theory. Replacement problems
Studies
Performance evaluation
Control chart
Matched filter
Simulation
Correlation analysis
Quality control
Cross correlation
Pattern recognition
Learning algorithm
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Summary:Pattern recognition techniques are currently pursued to identify unnatural patterns on quality control charts. This approach has been shown to enhance the ability to utilize the information of the chart more effectively than conventional run rules. This paper presents analysis and development of a pattern recognition system for identifying unnatural patterns on quality control charts. The system is based on correlation analysis, where a set of optimal matched filters are generated. To illustrate the design methodology and operation of the system, a set of commonly encountered patterns is utilized, such as the trend, the systematic, and the cyclic patterns. A training algorithm that minimizes the probabilities of Type I and Type II errors i presented. To evaluate the system performance, a testing algorithm as well as a set of newly-defined performance measures are introduced. The obtained results, based on extensive simulation runs, have proved the effectiveness of correlation analysis for control chart pattern recognition. © 1997 Elsevier Science Ltd
ISSN:0360-8352
1879-0550
DOI:10.1016/S0360-8352(96)00214-8