A sensing identification method for shearer cutting state based on modified multi-scale fuzzy entropy and support vector machine
A sensing identification method for shearer cutting state based on modified multi-scale fuzzy entropy and support vector machine
复制标题
基于改进多尺度模糊熵和支持向量机的采煤机截割状态传感识别方法
DOI：
10.1016/j.engappai.2018.11.003
复制
发表时间：
2019-02-01
影响因子：
8
通讯作者：
Tan, Chao
中科院分区：
文献类型：
--
作者：
Si, Lei;Wang, Zhongbin;Tan, Chao
采煤机截割状态的准确识别是实现煤矿安全高效生产的前提。本文提出了一种基于改进的多尺度模糊熵（MMFE）和支持向量机（SVM）的状态诊断方法。 MMFE 在多尺度熵和模糊熵的基础上，旨在对一定范围内的短时间序列获得稳定且准确的估计。因此，采用MMFE提取采煤机摇臂振动信号的特征信息，通过仿真分析可以合理体现时间序列的复杂性。此外，利用Fisher评分（FS）方法对获得的特征按照重要程度进行排序，选择信息最重要的前5个特征作为特征向量。随后，提出一种改进的果蝇优化算法（IFOA）来优化SVM的参数，并构建基于IFOA-SVM的多分类器来实现自动状态识别。实验结果表明，所提出的状态识别方法优于其他方法，能够有效区分不同工况下采煤机的不同截割状态。
Accurate identification of shearer cutting state is a prerequisite for achieving safe and efficient production in coal mines. In this paper, a novel state diagnosis method is put forward based on modified multi-scale fuzzy entropy (MMFE) and support vector machine (SVM). On the basis of multi-scale entropy and fuzzy entropy, MMFE is designed to obtain stable and accurate estimation for short time series over a range of scales. Therefore, MMFE is employed to extract the feature information of vibration signals of shearer rocker arm and the complexity of time series can be reasonably embodied through some simulation analysis. Besides, the Fisher score (FS) method is utilized to sort the obtained features according to their importance and the first five features with the most important information are selected as the feature vectors. Subsequently, an improved fruit fly optimization algorithm (IFOA) is presented to optimize the parameters of SVM and the IFOA-SVM based multi-classifier is constructed to fulfill an automatic state identification. The experiment results indicate that the proposed state identification method is outperforming others and can effectively distinguish different cutting states of shearer with different operation conditions.