Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

被引:22
|
作者
Si, Lei [1 ,2 ]
Wang, Zhongbin [1 ]
Liu, Xinhua [1 ]
Tan, Chao [1 ]
Xu, Jing [1 ]
Zheng, Kehong [1 ]
机构
[1] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Peoples R China
[2] China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Peoples R China
基金
中国博士后科学基金; 国家高技术研究发展计划(863计划); 中国国家自然科学基金;
关键词
shearer; cutting condition identification; parallel quasi-Newton algorithm; neural network; Dempster-Shafer theory; feature extraction; TRANSFERABLE BELIEF MODEL; SUPPORT VECTOR MACHINE; VIBRATION ANALYSIS; ALGORITHM;
D O I
10.3390/s151128772
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system.
引用
收藏
页码:28772 / 28795
页数:24
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