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

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.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► Classification test results of the condition of tool blades on a multi-tool head were presented. ► Signals of the mining power by sharp and blunt mining tools were recorded. ► Noise of the signals was reduced by means of discrete wavelet transform. ► Statistical measures of signals which were used as entry data for the artificial neural network were calculated. ► For classification of tools conditions fuzzy neural network was used.