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Sensors 2016, 16(1), 90; doi:10.3390/s16010090

Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm

1
School of Mechatronic Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China
2
School of Information and Electrical Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 11 November 2015 / Revised: 16 December 2015 / Accepted: 8 January 2016 / Published: 12 January 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [5167 KB, uploaded 12 January 2016]   |  

Abstract

Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system. View Full-Text
Keywords: shearer cutting pattern identification; least squares support vector machine; fruit fly optimization algorithm; ensemble empirical mode decomposition; feature extraction shearer cutting pattern identification; least squares support vector machine; fruit fly optimization algorithm; ensemble empirical mode decomposition; feature extraction
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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

Si, L.; Wang, Z.; Liu, X.; Tan, C.; Liu, Z.; Xu, J. Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm. Sensors 2016, 16, 90.

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