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...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 16; no. 4; p. 479
Main Authors: Si, Lei, Wang, Zhongbin, Liu, Xinhua, Tan, Chao, Zhang, Lin
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 06.04.2016
MDPI
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s16040479