Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process

Earth sciences research journal

Liu, Fang¹  Zhoub, Junwu²  Zhao, Jianjun³  Su, Weixing⁵ 

[1] Metallurgy, BeiJing, China;Institute of Mining &Northeastern University, Shenyang, China;Tianjin Polytechnic University, Tianjin, China;amp

关键词: ARHMM;    BDT;    KICvc;    outlier detection;    online detection.;   

DOI  :  10.15446/esrj.v21n3.65215

学科分类：天文学(综合)

来源: Universidad Nacional de Colombia * Departamento de Geociencias

【 摘 要 】

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.

【 授权许可】

CC BY