| Autor: |
Yu Zhang, Hongwei Ding, Yange Wang, Fuqiang Ren, Yongzhen Li, Zhaoyong Lv |
| Předmět: |
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| Zdroj: |
International Journal of Performability Engineering; Jul2018, Vol. 14 Issue 7, p1431-1438, 8p |
| Abstrakt: |
In recent years, with the increase of the resource exploitation, mining depth is getting deeper and deeper. Meanwhile, the lives of mining workers have been threatened strongly. In order to reduce the probability of rockburst, researchers have carried out in-depth research on rockburst. He Manchao, the academician of the State Key Laboratory for GeoMechanics and Deep Underground Engineering, has initially simulated the occurrence of rockburst in the laboratory as well as studied the mechanism of rockburst. Because the amount of data accumulated in the experiment is as large as 1000T, using these valuable experimental data becomes a difficult problem. Therefore, we have introduced big data technology into the field of rockburst. We have designed and realized the automated collaborative analysis system of the rockburst mechanism based on big data. We have used acoustic emission sensors as data collection methods and selected the multi-task online learning algorithm for data processing and analyzing. We have achieved the selection of the inflection point in the process of force changes using Matlab. In addition, the inflection point to be checked in the system can obtain the corresponding part of analysis diagrams. The theoretical analyses and experimental studies show that the automated collaborative analysis system can have an obvious influence on rockburst data processing, which provides a good foundation for the study of the rockburst mechanism. [ABSTRACT FROM AUTHOR] |
| Databáze: |
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