붓스트랩을 활용한 이상원인변수의 탐지 기법Bootstrap-Based Fault Identification Method
- Other Titles
- Bootstrap-Based Fault Identification Method
- Authors
- 강지훈; 김성범
- Issue Date
- 2011
- Publisher
- 한국품질경영학회
- Keywords
- Hotelling’s T^2; Decomposition; Bootstrap; Multivariate Process
- Citation
- 품질경영학회지, v.39, no.2, pp.234 - 243
- Indexed
- KCI
- Journal Title
- 품질경영학회지
- Volume
- 39
- Number
- 2
- Start Page
- 234
- End Page
- 243
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/113808
- ISSN
- 1229-1889
- Abstract
- Multivariate control charts are widely used to monitor the performance of a multivariate process over time to maintain control of the process. Although existing multivariate control charts provide control limits to monitor the process and detect any extraordinary events, it is a challenge to identify the causes of an out-of-control alarm when the number of process variables is large. Several fault identification methods have been developed to address this issue. However, these methods require a normality assumption of the process data. In the present study, we propose a bootstrapped-based T^2 decomposition technique that does not require any distributional assumption. A simulation study was conducted to examine the properties of the proposed fault identification method under various scenarios and compare it with the existing parametric T^2 decomposition method. The simulation results showed that the proposed method produced better results than the existing one, especially in nonnormal situations.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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