控制理论与应用2012，Vol.29Issue(4)：424-432,9.
过程控制时间序列中异常值的动态检测
Dynamic outlier detection for process control time series
摘要
Abstract
To improve the traditional outlier-detection by using wavelet analysis method and to deal with the instability characteristic of data from regulatory control process,we propose an improved outlier-detection method.This method combines an improved robust auto-regression（AR） model with the wavelet analysis method to eliminate the deficiency of the wavelet method in outlier-detection.To avoid the requirement of a pre-selected threshold value in the traditional method,we introduce the hidden Markov model（HMM） which analyzes the wavelet coefficients and updates online the coefficient values to improve the detection precision.Experiments and applications show that this method is especially suitable to oscillatory data in control processes.关键词
异常数据检测/自回归模型/小波/隐马尔科夫模型/时间序列Key words
outlier detection/auto-regression/wavelet/hidden Markov model(HMM)/time series分类
信息技术与安全科学引用本文复制引用
刘芳,毛志忠..过程控制时间序列中异常值的动态检测[J].控制理论与应用,2012,29(4):424-432,9.基金项目
国家高新技术研究发展计划（“863”计划）资助项目 （）
