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Classification of wear level of mining tools with the use of fuzzy neural network Jakub

机译:使用模糊神经网络Jakub对采矿工具的磨损程度进行分类

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摘要

In the article, classification test results of the condition of mining tool blades were presented. The tools work as a unit on a multi-tool head. On the research position, signals of mining power for sharp and blunt tools were recorded. Noise of signal power is reduced with the use of discrete wavelet transform in order to emphasize information.Statistical features of signals of mining power were specified, which were later used as entry data for the artificial neural network. Then, the fuzzy neural network, on the basis of calculated signal features, classifies the mining tools in terms of their wear.
机译:本文介绍了采矿工具刀片状况的分类测试结果。工具在多功能工具头上作为一个单元工作。在研究位置上,记录了尖锐和钝工具的挖掘力信号。利用离散小波变换来降低信号功率的噪声以强调信息。指定了挖掘功率信号的统计特征,随后将其用作人工神经网络的输入数据。然后,模糊神经网络根据计算出的信号特征对采矿工具的磨损进行分类。

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