气候变化领域集成服务门户(CCPortal): Coal and rock interface identification based on wavelet packet decomposition and fuzzy neural network

CCPortal

DOI	10.3233/JIFS-179620
	Coal and rock interface identification based on wavelet packet decomposition and fuzzy neural network
	Liu, Yanbing; Dhakal, Sanjev; Hao, Binyao
通讯作者	Liu, YB (通讯作者)
发表日期	2020
ISSN	1064-1246
EISSN	1875-8967
起始页码	3949
结束页码	3959
卷号	38 期号:4
英文摘要	The coal-rock interface identification function enables the shearer to automatically identify the coal-rock interface and demonstrates outstanding advantages in improving economic efficiency and safe operation. It can improve the recovery rate of coal seam, reduce the content of rock, ash and sulfur in coal, improve the efficiency of coal mining operation and reduce equipment wear. It is one of the key equipments to realize coal mining automation. At present, there are more and more researchers on the research of coal rock interface identification technology. A common method is to use a single sensor to establish a coal rock identification system, and use the neural network algorithm as the core algorithm of the system. Therefore, this paper proposes a recognition system based on wavelet packet decomposition and fuzzy neural network. A variety of sensors are used to collect the response signal of the shearer, and then the multi-signal feature extraction and data fusion of the coal-rock interface identification method are realized, thereby improving the recognition rate. On the basis of the physical simulation system of coal and rock interface, a large number of tests were carried out, and a large amount of test data was collected through experiments. In view of the many advantages of wavelet analysis, this paper uses wavelet packet technology to extract signal features. An energy allocation method based on wavelet packet decomposition can determine the sensitive frequency band of each sensor signal and extract each feature value. The wavelet packet energy method is used for feature extraction, which completes the conversion from mode space to feature space, and provides reliable and accurate feature level data for data fusion. The results show that neural networks and genetic neural networks can be trained and simulated using experimental data. Data fusion based on genetic neural network can perform state recognition and has high recognition accuracy. Multi-sensor data fusion technology based on genetic neural network is feasible in coal-rock interface identification.
英文关键词	Multi-sensor; coal-rock interface identification; fuzzy neural network; wavelet packet decomposition
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000534641700045
来源期刊	JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259795
推荐引用方式 GB/T 7714	Liu, Yanbing,Dhakal, Sanjev,Hao, Binyao. Coal and rock interface identification based on wavelet packet decomposition and fuzzy neural network[J]. 中国科学院青藏高原研究所,2020,38(4).
APA	Liu, Yanbing,Dhakal, Sanjev,&Hao, Binyao.(2020).Coal and rock interface identification based on wavelet packet decomposition and fuzzy neural network.JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,38(4).
MLA	Liu, Yanbing,et al."Coal and rock interface identification based on wavelet packet decomposition and fuzzy neural network".JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 38.4(2020).