Affiliation:
1. Institut Teknologi Sepuluh Nopember
2. Universiti Teknologi Malaysia
Abstract
Abstract
In this work, the mixed multivariate T2 control chart's detailed performance evaluation based on PCA Mix is presented. The control limit of the proposed control chart is calculated using the kernel density approach. Through simulation studies, the proposed chart's performance is assessed in terms of its capacity to identify outliers and process shifts. When 30% more outliers are included in the data, the proposed chart provides a consistent accuracy rate for identifying mixed outliers. For the balanced percentage of attribute qualities, misdetection happens because of the high false alarm rate. For unbalanced attribute qualities and excessive proportions, the masking effect is the key issue. The proposed chart shows the improved performance for the shift in identifying the shift in the process.
Publisher
Research Square Platform LLC
Reference42 articles.
1. D. C. Montgomery, Introduction to statistical quality control. John Wiley & Sons (New York), 2009.
2. S. Sorooshian, “Basic developments of quality characteristics monitoring,” J. Appl. Math., vol. 2013, 2013.
3. X. Pu, Y. Li, and D. Xiang, “Mixed variables-attributes test plans for single and double acceptance sampling under exponential distribution,” Math. Probl. Eng., vol. 2011, 2011, doi: 10.1155/2011/575036.
4. Multivariate Control Chart based on PCA Mix for Variable and Attribute Quality Characteristics;Ahsan M;Prod. Manuf. Res.,2018
5. M. Ahsan, M. Mashuri, H. Kuswanto, D. D. Prastyo, and H. Khusna, “Outlier detection using PCA mix based T2 control chart for continuous and categorical data,” Commun. Stat. - Simul. Comput., pp. 1–28, Apr. 2019, doi: 10.1080/03610918.2019.1586921.