Optimal selection of signal features in the diagnostics of mining head tools condition

Abstract The fact that mining tools must be replaced earlier than other parts to prevent machine failure and, consequently, its stoppage results in a significant increase in machinery operating costs. The replacement of cutting tools usually depends on the operator’s subjective decision rather than on specific parameters of the machine. This paper presents the numerical results of a study investigating the suitability of selected feature selection methods for providing information about the cutters’ condition. The feature selection methods used in this study to determine the resistance signal of a mining head allow us to select signal features that are variables of the classifying neural systems. A detailed survey of the literature on the subject confirms that such study is necessary. The development and implementation of an effective system for measuring wear of a cutting tool is of a great practical significance. The method proposed in this study can be implemented not only in underground mining but in other branches of the industry, too.

[1]  P. Filipek.  Estimating air-fuel mixture composition in the fuel injection control process in an SI engine using ionization signal in the combustion chamber , 2013 .

[2]  Figlus Tomasz,et al.  Assessment of the vibroactivity level of SI engines in stationary and non-stationary operating conditions , 2014 .

[3]  Grzegorz Litak,et al.  Quantitative estimation of the tool wear effects in a ripping head by recurrence plots , 2008 .

[4]  Jerzy Podgórski,et al.  Numerical simulation of brittle rock loosening during mining process , 2008 .

[5]  Jakub Gajewski,et al.  Geometry optimization of a thin-walled element for an air structure using hybrid system integrating artificial neural network and finite element method , 2017 .

[6]  Józef Jonak,et al.  Identifying the cutting tool type used in excavations using neural networks , 2006 .

[7]  James Bailey,et al.  Can high-order dependencies improve mutual information based feature selection? , 2016, Pattern Recognit..

[8]  Adam Glowacz,et al.  Diagnostics of Synchronous Motor Based on Analysis of Acoustic Signals with the use of Line Spectral Frequencies and K-nearest Neighbor Classifier , 2015 .

[9]  Mohammad Rezaei,et al.  Evaluation of the maximum horizontal displacement around the power station caverns using artificial neural network , 2017 .

[10]  R. Kaczyński,et al.  Examination of the coating of a cutting tool based on measurements microhardness and scratch tests , 2017 .

[11]  Józef Jonak,et al.  Identification of ripping tool types with the use of characteristic statistical parameters of time graphs , 2008 .

[12]  Juha Reunanen,et al.  Overfitting in Making Comparisons Between Variable Selection Methods , 2003, J. Mach. Learn. Res..

[13]  Okko Johannes Räsänen,et al.  Random subset feature selection in automatic recognition of developmental disorders, affective states, and level of conflict from speech , 2013, INTERSPEECH.

[14]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[15]  Jamal Rostami,et al.  Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs , 2016 .

[16]  Heba Abusamra,et al.  A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data of Glioma , 2013 .

[17]  Okko Johannes Räsänen,et al.  Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits , 2015, Comput. Speech Lang..

[18]  Rossitza Setchi,et al.  Feature selection using Joint Mutual Information Maximisation , 2015, Expert Syst. Appl..

[19]  Józef Jonak,et al.  Utilisation of neural networks to identify the status of the cutting tool point , 2006 .

[20]  Günther Meschke,et al.  Model update and real-time steering of tunnel boring machines using simulation-based meta models , 2015 .

[21]  Józef Jonak,et al.  A disassembly-free method for evaluation of spiral bevel gear assembly , 2017 .

[22]  Józef Jonak,et al.  Towards the identification of worn picks on cutterdrums based on torque and power signals using Artificial Neural Networks , 2011 .

[23]  Tomasz Wydro,et al.  Parametric Factors for the Tangential-Rotary Picks Quality Assessment / Wskaźniki Parametryczne Oceny Jakości Noży Styczno-Obrotowych , 2015 .

[24]  Yong Sun,et al.  Parametric study of rock cutting with SMART∗CUT picks , 2017 .

[25]  Łukasz Bołoz,et al.  Unique Project of Single-Cutting Head Longwall Shearer Used for Thin Coal Seams Exploitation / Projekt Jednoorganowego Kombajnu ŚCIANOWEGO O Specjalnej Konstrukcji Przeznaczonego do Eksploatacji POKŁADÓW Cienkich , 2013 .

[26]  A. Wayne Whitney,et al.  A Direct Method of Nonparametric Measurement Selection , 1971, IEEE Transactions on Computers.