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

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...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 16; číslo 4; s. 479
Hlavní autoři:	Si, Lei, Wang, Zhongbin, Liu, Xinhua, Tan, Chao, Zhang, Lin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 06.04.2016 MDPI
Témata:	Coal mining Computer simulation Cutting Decomposition Diagnosis distance-based evaluation Fault diagnosis feature extraction Fourier transforms fruit fly optimization algorithm Genetic algorithms Kullback–Leibler divergence Methods Neural networks Optimization algorithms Pattern recognition probabilistic neural network Rockers Sensors shearer cutting state diagnosis Shearers Vibration Wavelet transforms China shearer cutting state diagnosis Kullback–Leibler divergence distance-based evaluation probabilistic neural network feature extraction fruit fly optimization algorithm
ISSN:	1424-8220, 1424-8220
On-line přístup:	Získat plný text
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