A feature extraction method based on composite multi-scale permutation entropy and Laplacian score for shearer cutting state recognition
A feature extraction method based on composite multi-scale permutation entropy and Laplacian score for shearer cutting state recognition
复制标题
基于复合多尺度排列熵和拉普拉斯评分的采煤机截割状态识别特征提取方法
DOI：
10.1016/j.measurement.2019.05.070
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发表时间：
2019-10-01
期刊：
影响因子：
5.6
通讯作者：
Liu, Xinhua
中科院分区：
文献类型：
--
作者：
Si, Lei;Wang, Zhongbin;Liu, Xinhua
在煤炭开采领域，采煤机精确截割状态识别是实现自动截煤的基础和前提。本文提出了一种基于复合多尺度排列（CMPE）、拉普拉斯评分（LS）和基于飞行优化算法的支持向量机分类（FOA-SVM）的采煤机截割状态识别方法。 CMPE是为了克服MPE的缺点而提出的，可以从采煤机摇臂振动信号中提取隐藏状态特征。提供了一些仿真来选择适当的参数设置并证明 CMPE 相对于 MPE 的优越性。此外，采用LS算法对提取的不同尺度特征按照重要程度进行排序，科学地生成敏感特征组合。构建FOA-SVM分类器，实现采煤机截割状态的智能识别。最后进行了实验，对比结果表明，该方法能够实现采煤机截割状态的识别，且比现有方法具有更高的准确度。 (C) 2019 Elsevier Ltd. 保留所有权利。
In the field of coal mining, accurate cutting state recognition of shearer is the basis and premise for achieving automatic coal cutting. In this paper, a novel shearer cutting state recognition method is presented based on composite multi-scale permutation (CMPE), Laplacian score (LS) and fly optimization algorithm-based support vector machine classification (FOA-SVM). CMPE is proposed to overcome the shortcomings of MPE and can extract the hidden state characteristics from the vibration signals of shearer rocker arm. Some simulations are provided to select the appropriate parameter settings and prove the superiority of CMPE to MPE. In addition, LS algorithm is employed to sort the extracted features over different scales according to their importance and the sensitive feature combinations can be generated scientifically. The FOA-SVM classifier is constructed to achieve intelligent recognition of shearer cutting state. Finally, some experiments are presented and the comparison results indicated that the proposed method can realize the recognition of shearer cutting state with higher accuracy than the existing methods. (C) 2019 Elsevier Ltd. All rights reserved.