关键词: 统计过程控制, 自相关观测, 隐马尔科夫模型, 控制图

Abstract: The observations are usually auto-correlated in actual processes.To solve the problem that the traditional control charts could not monitor effectively caused by auto-correlation in actual processes,by considering the auto-correlated data,a modification of Hidden Markov Model (HMM) was proposed.Through building the first order auto-correlation relation of observation sequence probability on time domain,the residual chart was built.Results of case study and simulation showed that the proposed method made significant improvements in monitoring auto-correlation process.Specifically it performed higher sensitivity and was easier to be implemented by comparing with residual charts based on Auto-Regressive and Moving Average (ARMA) models.

Key words: statistical process control, auto-correlated observations, hidden Markov model, control charts