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

Sensors

Lei Si¹  Zhongbin Wang¹  Xinhua Liu¹  Chao Tan¹  Jing Xu¹  Kehong Zheng¹ 

[1] School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China; E-Mails:

关键词: shearer;    cutting condition identification;    parallel quasi-Newton algorithm;    neural network;    Dempster-Shafer theory;    feature extraction;   

DOI  :  10.3390/s151128772

来源: mdpi

【 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.