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Mine Gallery Rig Performance Test Based on BP Neural Network and Multi-sensor Data Fusion

机译:基于BP神经网络和多传感器数据融合的矿山画廊钻机性能测试

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Traditional gallery rig's factory inspection process is complex and the result is not accurate. It is necessary to find a simple way to realize the factory inspection. In this paper multisensor data fusion based on Grubbs criterion is used in gallery rig performance test, and a classification algorithm based on BP neural network is analyzed and simulated. The results showed the validity of the algorithm, and it successfully realized the accurate evaluation of the gallery rig performance. So it has a strong practical value under the premise of both control costs and ensure the evaluation effect.
机译:传统的画廊钻机的工厂检测过程很复杂,结果不准确。有必要找到一种实现工厂检查的简单方法。在本文中,基于Grubbs标准的多传感器数据融合在Gallery Rig性能测试中使用,分析了基于BP神经网络的分类算法和模拟。结果表明算法的有效性,成功实现了对画廊钻机性能的准确评估。因此,在控制成本的前提下,它具有强大的实用价值,并确保评估效果。

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