NOVEL APPROACHES FOR STATISTICAL PROCESS CONTROL CHARTS PATTERN RECOGNITION

Fast and accurate recognition of the Statistical Control Chart Patterns (SPCCP) is significant for supervising manufacturing processes to accomplish better control and to make high value products. SPCCP can display eight kinds of patterns: normal, stratification, systematic, increasing trend, decreasing trend, up shift, down shift and cyclic. With the exception of the natural pattern, all other patterns indicate that the supervised manufacturing process is not performing properly and actions need to be taken to correct the problems. This research proposes new approaches, neural networks and neural-fuzzy systems, to the (SPCCP) recognition. This dissertation also investigates the use of features extracted from statistical analysis for simple patterns, and wavelet analysis for concurrent patterns as the components of the input vectors. Results based on simulated data show that the proposed approaches perform better than conventional approaches. Our work concluded that the extracted features improve the performance of the proposed recognizer systems.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Campus Only Dissertation

Discipline thesis:degree_discipline

Engineering Science

Year

2010

Author dc:creator

• el homani, Abdellatif

Contributors dc:contributor

• Aouadi, Samir

• chart
• control
• fuzzy logic
• pattern
• process
• wavelet

Repository record dc:identifier

https://opensiuc.lib.siu.edu/dissertations/152

OAI identifier oai:identifier

oai:opensiuc.lib.siu.edu:dissertations-1152