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

Waseem Alwan, Waseem Alwan and Ngadiman, Nor Hasrul Akhmal and Hassan, Adnan and Syahril Ramadhan Saufi, Syahril Ramadhan Saufi and Mahmood, Salwa (2023) Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns. Machines, 11 (115). pp. 1-33.

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	control chart patterns; ensemble classifier; small variation
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering Technology > FTK
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	26 Jun 2024 07:40
Last Modified:	26 Jun 2024 07:40
URI:	http://eprints.uthm.edu.my/id/eprint/11251

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