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Sensors 2015, 15(11), 28772-28795; doi:10.3390/s151128772

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

1
School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China
2
School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 18 September 2015 / Revised: 8 November 2015 / Accepted: 10 November 2015 / Published: 13 November 2015
(This article belongs to the Section Physical Sensors)
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Abstract

In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system. View Full-Text
Keywords: shearer; cutting condition identification; parallel quasi-Newton algorithm; neural network; Dempster-Shafer theory; feature extraction shearer; cutting condition identification; parallel quasi-Newton algorithm; neural network; Dempster-Shafer theory; feature extraction
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.; Xu, J.; Zheng, K. Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory. Sensors 2015, 15, 28772-28795.

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