Abstract

ZHAO, Jianjun; ZHOUB, Junwu; SU, Weixing  and  LIU, Fang. Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process. Earth Sci. Res. J. [online]. 2017, vol.21, n.3, pp.135-139. ISSN 1794-6190.  https://doi.org/10.15446/esrj.v21n3.65215.

Given the difficulty of accurate online detection for massive data collecting real-timely in a strong noise environment during the complex geological mineral grade analysis process, an order self-learning ARHMM (Autoregressive Hidden Markov Model) algorithm is proposed to carry out online outlier detection in the geological mineral grade analysis process. The algorithm utilizes AR model to fit the time series obtained from "Online x - ray Fluorescent Mineral Analyzer" and makes use of HMM as a basic detectiontool, which canavoidthe deficiency ofpresettingthethreshold intraditional detectionmethods.Thestructure oftraditional BDT (Brockwell-Dahlhaus-Trindade) algorithm is improved to be a double iterative structure in which iterative calculation from both time and order is applied respectively to update parameters ofARHMM online. With the purpose of reducing the influence of outlier on parameter update of ARHMM, the strategies of detection-before-update and detection-based-update are adopted, which also improve the robustness of the algorithm. Subsequent simulation by model data and practical application verify the accuracy, robustness, and property of online detection of the algorithm. According to the result, it is obvious that new algorithm proposed in this paper is more suitable for outlier detection of mineral grade analysis data in geology and mineral processing.

Keywords : ARHMM; BDT; KICvc; outlier detection; online detection.

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