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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 9674942, 12 pages
Research Article

A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

1School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
2School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
3Xuyi Mine Equipment and Materials R&D Center, China University of Mining and Technology, Huai’an 223001, China

Received 25 January 2016; Revised 3 March 2016; Accepted 7 March 2016

Academic Editor: Cheng-Jian Lin

Copyright © 2016 Zhongbin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN) and support vector machine (SVM) methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others.