ISSN 2756-3375
African Journal of Immunology Research ISSN 9431-5833 Vol. 5 (12), pp. 440-452, December, 2018. © International Scholars Journals
Full Length Research Paper
A method for improving the accuracy of pattern recognition in control charts through the autocorrelation of product characteristics
Hui-Ping Cheng* and Chuen-Sheng Cheng
1Department of Business Innovation and Development, MingDao University, 369 Wen-Hua Rd., Peetow, Changhua
52345, Taiwan.
2Department of Industrial Engineering and Management, Yuan-Ze University, 135 Yuan-Tung Rd., Chung-Li, Taoyuan 320, Taiwan.
*Corresponding author, E-mail: [email protected].
Accepted 09 May, 2017
Abstract
A control chart is a tool for statistical process control (SPC) used to determine variations in manufacturing processes resulting from common or assignable causes. The presence of unnatural patterns in control charts is an indication that the process has been influenced by assignable causes, and corrective actions must be taken. However, the assumption of uncorrelated or independent observations is not suitable for all of the characteristics pertaining to a product. This study presents a method with which to improve the accuracy of pattern recognition in control charts, through the autocorrelation of product characteristics. Particularly, we developed an artificial algorithm-based machine learning model to recognize unnatural patterns and processes in AR(1) simultaneously. The proposed method integrates an artificial immune system and support vector machine within a recognition system. This study evaluated the accuracy with which four patterns could be recognized. The four patterns include trends, sudden shifts, cyclic patterns, and normal patterns. Identifying unnatural patterns can greatly narrow the range of possible causes that must be investigated, thereby speeding up the diagnostic processes.
Keyword: Pattern recognition, artificial immune algorithm, support vector machine, autocorrelation.