Cutting performance evaluation of a roadheader machine by PCA and RBF

Lijuan Zhao, Jianyong Wang, Xu Zhu
  • SIMULATION, February 2018, SAGE Publications
  • DOI: 10.1177/0037549717753992

Intelligent research on mining machinery

What is it about?

As coal mining technology has continuously evolved, gradually the industry has moved toward fully mechanized mining.A roadheader machine is important mechanical equipment for roadway drivage through mechanical crushing. Through analysis and research to discover the key parameters relating to the cutting performance of the roadheader machine, the performance of the roadheader machine must be optimized and costs reduced, as well as productivity increased.

Why is it important?

As one of the most important statistical methods, principal component analysis (PCA) could not only reduce many factors into fewer overall targets, it could also provide the comparative item weighting, improving the computational efficiency and error precision of the radial basis function neural network, eliminating the correlation of each input variable, and increasing the stability of the network model. The principal variables are determined and a cutting performance evaluation model developed that allow both performance prediction and cutting performance evaluation. From the analysis it is concluded that the primary indicators of the roadheader cutting performance were the unidirectional compressive strength, the cutting resistance fluctuation, the weaving speed of the cutting head, the cutting power fluctuation, and the traction resistance fluctuation. The model is consistent with the practical test results and contributes to discovery of future optimization procedures.


Dr Jianyong Wang (Author)
Liaoning Technical University

Writing this article was a great pleasure as it has co-authors with whom I have had long standing collaborations. This paper provides a very effective method for predicting the performance of mining machinery,which can reduce losses and increase efficiency for enterprises.

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The following have contributed to this page: Dr Jianyong Wang