Method for Outlier Detection in Process Control Field
LIU Fang1, MAO Zhi-zhong1,2
1. School of Information Science & Engineering, Northeastern University, Shenyang, Liaoning 110004, China;
2. Key Laboratory of Synthetical Automation for Process Industry of Ministry of Education, Northeastern University, Shenyang, Liaoning 110004, China
Abstract：Aiming at the characteristics of data in process industry which are large volume of data and on-line detection,an outlier detection algorithm which combines the improved RBF network and ARHMM is proposed.In the new algorithm, improved RBF network is used to model base on major data in kernel space,and then according to the residual errors,the detection results are made by kernel ARHMM.Forgetting factor and penalty factor are introduced by improved RBF network,which can make the algorithm more robust and accuracy.In order to avoid preselecting the detection threshold,KARHMM is used to detect outlier in process industry.The practicality is proved by experimentation and application,and through the comparison with AR model, it shows that the nonlinear KARHMM algorithm is more suitable for process data.
刘芳, 毛志忠. 过程控制异常值的在线检测方法研究[J]. 计量学报, 2013, 34(1): 84-89.
LIU Fang, MAO Zhi-zhong. Method for Outlier Detection in Process Control Field. Acta Metrologica Sinica, 2013, 34(1): 84-89.
［1］ Takeuchi J, Yamanishi K. A unifying framework for detecting outliers and change points from time series［J］. IEEE transactions on knowledge and data engineering, 2006, 18(4): 482-492.
［2］ Trindade A A. Implementing modified burg algorithms in multivariate subset autoregressions modeling［J］. J Statist Sofftware, 2003,39(8):1-68.
［3］ Brockwell P J, Dahlhaus R, Trindade A A. Modified burg algorithms for multivariate subset autorgegression［J］. Statistica Sinica, 2005,15(1), 197-213.
［4］ Lou H L. Implementing the viterbi algorithm ［J］. IEEE Signal Processing Magazine, 1995, 12(5): 42-52.
［5］ Kavcˇic′ A, Moura J M F, The Viterbi algorithm and Markov noise memory［J］. IEEE transactions on information theory, 2000, 46(1): 291-301.
［6］ Alexandridis A, Sarimveis H, Bafas G. A new algorithm for online structure and parameter adaptation of RBF networks［J］. Neural Networks, 2003,16(7):1003-1017.
［7］ Bilmes J A. What HMMs Can Do ［J］. IEICE TRANS INF & SYST, 2006,E89-D(3): 869-891.
2013, Vol. 34 
