Information Fusion Identification for Shearer Cutting Status through Improved Support Vector Data Description

Title:Information Fusion Identification for Shearer Cutting Status through Improved Support Vector Data Description

Volume: 8 Issue: 2

Author(s): Lei Si, Zhongbin Wang, Chao Tan, Xinhua Liu and Lin Zhang

Affiliation:

Keywords: Dissipative operation, identification method, inertia weight, particle swarm optimization algorithm, shearer cutting status, support vector data description.

Abstract: In order to research the identification methods for shearer cutting status, some relevant patents and scientific references are studied and analyzed. A new method based on the integration of particle swarm optimization (PSO) and support vector data description (SVDD) is proposed to identify the shearer cutting status. The PSO is provided to solve the constraint optimization problem of SVDD. Some key technologies are proposed to balance the search ability and enhance the population diversity of PSO, and the flowchart of proposed method is designed. Furthermore, some simulation examples are carried out and the compared results indicate that the proposed method is feasible and efficient. Finally, an industrial application example of coal mining face is demonstrated to specify the effect of proposed system.

Cite this article as:

Si Lei, Wang Zhongbin, Tan Chao, Liu Xinhua and Zhang Lin, Information Fusion Identification for Shearer Cutting Status through Improved Support Vector Data Description, Recent Patents on Mechanical Engineering 2015; 8 (2) . https://dx.doi.org/10.2174/2212797608666150428000536