Statistical Process Monitoring with Uncorrelated Component Analysis

• Main Authors: Yi-Yi Wang, 王議億
• Other Authors: Shu-Kai Fan
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/51728354524459744211

碩士 === 元智大學 === 工業工程與管理學系 === 95 === The uncorrelated component analysis (UCA) model is applied to solving the mixed process signal separation problem. In order to seek optimum demixing matrix of UCA model, particle swarm optimization (PSO) which is one of the latest developed population-based optimization methods is adopted. In the context of signal separation, the presented method could find efficiently and reliably the optimal estimate of the demixing matrix for the mixed process signals, such as autoregressive (AR) series, step change in process mean, and Gaussian noises. Furthermore, the proposed method can be applied to EWMA control chart in process monitoring to detect small shift in process mean with shorter out-of-control average run length (ARL).