A situation assessment method for rock burst based on multi-agent information fusion. (July 2015)

• Record Type: Journal Article
• Authors: Jia, Rui-Sheng; Liu, Cong; Sun, Hong-Mei; Yan, Xiang-Hong
• Is Part Of: Computers & electrical engineering. Volume 45(2015)
• Page Start: 22
• Page End: 32
• Publication Date: 2015-07
• Subjects: Rock burst -- Multi-agent system -- Information fusion -- Situation assessment -- Forecasting model; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.04.015
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: The target of this research is assessment situation of rock burst in coal mine. Generally speaking, an assessment approach for the rock burst situation by integrating the Multi-Agent System with data fusion techniques is proposed. To improve the accuracy and reliability of source data, an optimal algorithm for multi-sensor data fusion is discussed. Some intelligent model-based situation quantization methods is described and rock burst situation quantitative assessment model is discussed by improved Dempster–Shafer theory. The Auto-Regressive and Moving Average Model and Holt–Winters model are used to deal with indefinability and inaccuracy in the process of predicting rock burst. Case study results shows the proposed rock burst situation assessment model can give a relatively accurate forecast, and can help coal mine decision-makers to grasp an overview of the development trend of the rock burst. Abstract: An assessment approach to assess the likelihood of rock burst in coal mines by integrating the Multi-Agent System with data fusion techniques is proposed in this paper. We discuss an optimal algorithm for multi-sensor data fusion to improve the accuracy and reliability of the source data. Some model-based situation quantization methods are described and a rock burst situation quantitative assessment model incorporating improved Dempster–Shafer theory is presented. The Auto-Regressive, Moving Average and Holt–Winters models are used to address indefinability and inaccuracy of the prediction. A case study demonstrates that the proposed situation assessment model is capable of producing relatively accurate forecasts, and thereby it can provide coal mine decision-makers with an overview of the development of rock bursts.