A situation assessment method for rock burst based on multi-agent information fusion

• 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.

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.

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