title: Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory.
abstract: 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.
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China. lei.si@cumt.edu.cn.|School of Information and Electrical Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China. lei.si@cumt.edu.cn.
authors.forename: Lei
authors.initials: L
authors.lastname: Si
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China. wangzbpaper@126.com.
authors.forename: Zhongbin
authors.initials: Z
authors.lastname: Wang
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China. l_xinhua_2006@126.com.
authors.forename: Xinhua
authors.initials: X
authors.lastname: Liu
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China. tccadcumt@126.com.
authors.forename: Chao
authors.initials: C
authors.lastname: Tan
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China. xujingcumt@126.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Xu
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China. zkhcumt@gmail.com.
authors.forename: Kehong
authors.initials: K
authors.lastname: Zheng
keywords: Dempster-Shafer theory; cutting condition identification; feature extraction; neural network; parallel quasi-Newton algorithm; shearer