Coal-rock drilling states recognition of drilling robot for rockburst prevention based on multi-sensor information fusion

Abstract

Accurate recognition of coal-rock drilling sates is a prerequisite for achieving intelligent drilling pressure relief. In this paper, a novel coal-rock drilling states recognition method of drilling robot for rockburst prevention is proposed. Firstly, different coal-rock drilling signals are collected and processed by using improved antlion optimization (ALO) algorithm and variational mode decomposition (VMD). Meanwhile, the elite opposition-based learning (EOL) strategy is used to improve the global search ability and optimization performance of ALO, and the EOL-ALO is developed and employed to automatically search the optimal key parameters of VMD. Subsequently, the root mean square of frequency and kurtosis are used to extract the feature information from the decomposed signals and the singular value decomposition method is employed to reduce the dimensionality of high-dimensional feature vectors. Furthermore, an improved D-S evidence theory is developed to fuse the recognition results of support vector machine through a single sensor information and the fusion recognition framework of coal-rock drilling states is designed. Finally, a coal-rock drilling experimental platform is established and some experimental analysis is carried out. The experimental results indicate the feasibility and superiority of proposed coal-rock drilling states recognition method.