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Appl. Sci. 2016, 6(10), 294; doi:10.3390/app6100294

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

1
School of Mechatronic Engineering, China University of Mining & 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
Received: 1 August 2016 / Revised: 9 September 2016 / Accepted: 8 October 2016 / Published: 12 October 2016
(This article belongs to the Section Acoustics)
View Full-Text   |   Download PDF [9505 KB, uploaded 12 October 2016]   |  

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
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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. (CC BY 4.0).

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MDPI and ACS Style

Xu, J.; Wang, Z.; Wang, J.; Tan, C.; Zhang, L.; Liu, X. Acoustic-Based Cutting Pattern Recognition for Shearer through Fuzzy C-Means and a Hybrid Optimization Algorithm. Appl. Sci. 2016, 6, 294.

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