Research on roof stability assessment method of coal roadway
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摘要: 对用于煤巷顶板稳定性评价的经典方法中常用的单指标法和复合指标法，以及机器学习方法中常用的有监督学习方法和无监督学习方法进行了总结、分析，指出经典方法采用单一指标或仅针对某一类煤岩体进行顶板稳定性评价，评价结果不全面、不可靠，而机器学习方法需要人工标注大量顶板监测数据，工作量大，实际应用效果较差。基于深度学习方法可从顶板监测数据中自动提取特征的优势，提出了采用深度学习中的生成对抗网络对煤巷顶板进行稳定性评价的新模式，从而减少人工工作量。Abstract: Existing roof stability assessment methods of coal roadway were summarized and analyzed, which included single index method and compound index method in classic methods and supervised learning method and unsupervised learning method in machine learning methods. It was pointed out that the classic methods assessed roof by single index or for a certain type of coal rock so that assessment result is incomplete or unreliable, and the machine learning method needed a large number of hand-crafted labeling of roof monitoring data with large workload and poor actual application effect. A new roof stability assessment mode of coal roadway based on generative adversarial network in deep learning was proposed according to advantage of extracting features from roof monitoring data automatically of the deep learningmethod, so as to decrease labor workload.
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期刊类型引用(3)
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百度学术
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3. 王渊，李红卫，郭卫，贺海涛，郏高祥. 基于图像识别的液压支架护帮板收回状态监测方法. 工矿自动化. 2019(02): 47-53 . 本站查看
其他类型引用(1)
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