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Sensors 2016, 16(4), 479; doi:10.3390/s16040479

Cutting State Diagnosis for Shearer through the Vibration of Rocker Transmission Part with an Improved Probabilistic Neural Network

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: Leonhard M. Reindl
Received: 17 February 2016 / Revised: 23 March 2016 / Accepted: 30 March 2016 / Published: 6 April 2016
(This article belongs to the Section Sensor Networks)
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Abstract

In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN) and fruit fly optimization algorithm (FOA). The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy. View Full-Text
Keywords: shearer cutting state diagnosis; probabilistic neural network; fruit fly optimization algorithm; Kullback–Leibler divergence; distance-based evaluation; feature extraction shearer cutting state diagnosis; probabilistic neural network; fruit fly optimization algorithm; Kullback–Leibler divergence; distance-based evaluation; 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.; Zhang, L. Cutting State Diagnosis for Shearer through the Vibration of Rocker Transmission Part with an Improved Probabilistic Neural Network. Sensors 2016, 16, 479.

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