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

Jianjun Zhao,Junwu Zhoub,Fang Liu,Weixing Su

doi:10.15446/esrj.v21n3.65215

Abstract

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 detection tool, which can avoid the deficiency of presetting the threshold in traditional detection methods. The structure of traditional 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 of ARHMM 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.

Highlights

Mineral composition analysis is a key factor in determining whether or not to carry out mining
At present, automated testing equipment is used in ore grade analysis, such as “BOX-A type on-stream x-ray fluorescence analyzer”, which uses spectral obtain by irradiating X-rays to the pulp to get the results of ore grade
Considering the problem that the model order of chemical or physical testing equipment’s hard to be determined, the new detected method which is based on residual error has the function of model order self-learning

Summary

Introduction

Mineral composition analysis is a key factor in determining whether or not to carry out mining. Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process. A new algorithm is proposed here to especially do outlier detection for ore inspection data which obtain from chemical or physical testing equipment.

Results

Conclusion