Pick wear condition identification based on wavelet packet and SOM neural network

被引：0
|
作者：
Zhang Q. [1 ]
Gu J. [1 ]
Liu J. [1 ]
Liu Z. [1 ]
Tian Y. [1 ]
机构：
[1] School of Mechanical Engineering, Liaoning Technical University, Fuxin
来源：
Meitan Xuebao/Journal of the China Coal Society | 2018年 / 43卷 / 07期
关键词：
Acoustic emission; Coal shearer; Identification; Pick wear; SOM neural network; Wavelet packet decomposition;
D O I：
10.13225/j.cnki.jccs.2017.1213
中图分类号：
学科分类号：
摘要：
In order to realize the real-time monitoring on the wear degree of coal shearer picks in the cutting process, the acoustic emission signals under individual wear degrees are collected via acoustic emission sensors.The wavelet packet analysis is used to analyze the trend of the signal under individual wave band, the sample space of the energy is established, and the pick wear identification model based on SOM (Self Organizing Maps) neural network is built to realize the real-time monitoring of the pick wear degree.The model is proved via random testing experiments.The results show that the accuracy of the model, which is based on wavelet packet analysis and SOM neural network, is high, and the accuracy is about 95%.The results provide an important technical mean for identifying the wear degree of picks precisely and improving the work efficiency. © 2018, Editorial Office of Journal of China Coal Society. All right reserved.
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页码：2077 / 2083
页数：6
相关论文
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