Supervised context classification methods for an industrial machinery

机译：工业机械的监督上下文分类方法

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摘要

The paper describes a method of supervised context classification for an industrial machinery. The main objective of this study is to compare single and ensemble classifiers in order to classify groups of contexts which are based on an operating state of the device. The applied research was conducted with the assumption that only classic and well-practised classification methods would be adopted. The comparison study was carried out using real data recorded from an industrial machinery working underground in a mine in Poland. The achieved results confirm the effectiveness of the proposed approach and also show its limitations.

机译：本文描述了一种用于工业机械的监督上下文分类的方法。这项研究的主要目的是比较单个分类器和整体分类器，以便基于设备的运行状态对上下文组进行分类。进行应用研究的前提是仅采用经典且实践良好的分类方法。比较研究是使用从波兰某矿山的地下工作的工业机械记录的真实数据进行的。所取得的结果证实了该方法的有效性，并显示了其局限性。

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《Federated Conference on Computer Science and Information Systems》|2015年|1667-1672|共6页
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Kalisch Mateusz;
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learning (artificial intelligence); machinery; mining; mining equipment; pattern classification; production engineering computing; Poland; ensemble classifiers; industrial machinery; mine; supervised context classification methods; Accuracy; Bayes methods; Context; Decision trees; Fault detection; Machinery; Mathematical model;

机译：学习（人工智能）;机械;采矿;采矿设备;模式分类;生产工程计算;波兰;集成分类器;工业机械;矿山;监督上下文分类方法;精度;贝叶斯方法;上下文;决策树;故障检测;机械;数学模型;

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Supervised context classification methods for an industrial machinery

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