Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

• Published in: Machines (Basel) Vol. 11; no. 1; p. 115
• Main Authors: Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2023
• Subjects: Accuracy; Aerospace industry; Algorithms; Artificial intelligence; Artificial neural networks; Avionics; Classification; Classifiers; control chart patterns; Control charts; Datasets; Decision trees; ensemble classifier; Feature selection; Generalized linear models; Machine learning; Manufacturing; Methods; Monitoring; Monte Carlo simulation; Neural networks; Pattern recognition; Principal components analysis; Process controls; Researchers; small variation; Standard deviation; Statistical process control; Support vector machines; X-bar charts; United States > US
• ISSN: 2075-1702; 2075-1702
• DOI: 10.3390/machines11010115

Manufacturing processes have become highly accurate and precise in recent years, particularly in the chemical, aerospace, and electronics industries. This has attracted researchers to investigate improved procedures for monitoring and detection of small process variations to remain in line with such advances. Among these techniques, statistical process controls (SPC), in particular the control chart pattern (CCP), have become a popular choice for monitoring process variance, being utilized in numerous industrial and manufacturing applications. This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. Before advancing to the classification step, Nelson’s Rus Rules were utilized as a monitoring rule to distinguish between stable and unstable processes. The study’s findings indicate that the proposed method improves classification performance for patterns with mean changes of less than 1.5 sigma, and confirm that the performance of the ensemble classifier is superior to that of the individual classifier. The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.