Acoustic-Based Cutting Pattern Recognition for Shearer through Fuzzy C-Means and a Hybrid Optimization AlgorithmReportar como inadecuado


Acoustic-Based Cutting Pattern Recognition for Shearer through Fuzzy C-Means and a Hybrid Optimization Algorithm


Acoustic-Based Cutting Pattern Recognition for Shearer through Fuzzy C-Means and a Hybrid Optimization Algorithm - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

1

School of Mechatronic Engineering, China University of Mining and Technology, No.1 Daxue Road, Xuzhou 221116, China

2

Institute for Neural Computation, University of California, San Diego UCSD, No.3950 Mahaila Ave, San Diego 92093, CA, USA

3

Collaborative Innovation Center of Intelligent Mining Equipment in Jiangsu Province, No.1 Daxue Road, Xuzhou 221116, China





*

Author to whom correspondence should be addressed.



Academic Editor: Dimitrios G. Aggelis

Abstract As the conventional cutting pattern recognition methods for shearer are huge in size, have low recognition reliability and an inconvenient contacting measurement method, a fast and reliable coal-rock cutting pattern recognition system is always a baffling problem worldwide. However, the recognition rate has a direct relation with the outputs of coal mining and the safety quality of staff. In this paper, a novel cutting pattern identification method through the cutting acoustic signal of the shearer is proposed. The signal is clustering by fuzzy C-means FCM and a hybrid optimization algorithm, combining the fruit fly and genetic optimization algorithm FGOA. Firstly, an industrial microphone is installed on the shearer and the acoustic signal is collected as the source signal due to its obvious advantages of compact size, non-contact measurement and ease of remote transmission. The original sound is decomposed by multi-resolution wavelet packet transform WPT, and the normalized energy of each node is extracted as a feature vector. Then, FGOA, by introducing a genetic proportion coefficient into the basic fruit fly optimization algorithm FOA, is applied to overcome the disadvantages of being time-consuming and sensitivity to initial centroids of the traditional FCM. A simulation example, with the accuracy of 95%, and some comparisons prove the effectiveness and superiority of the proposed scheme. Finally, an industrial test validates the practical effect. View Full-Text

Keywords: cutting pattern recognition; acoustic signal; fuzzy C-means clustering; hybrid optimization; fruit fly optimization algorithm; genetic algorithm; genetic proportion coefficient cutting pattern recognition; acoustic signal; fuzzy C-means clustering; hybrid optimization; fruit fly optimization algorithm; genetic algorithm; genetic proportion coefficient





Autor: Jing Xu 1, Zhongbin Wang 1,* , Jiabiao Wang 1, Chao Tan 1, Lin Zhang 1,2 and Xinhua Liu 1,3

Fuente: http://mdpi.com/



DESCARGAR PDF




Documentos relacionados