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
相关论文
共 35 条
  • [1] Application of Multi-sensor Data Fusion in Defects Evaluation based on Dempster-Shafer Theory
    Li Guohou
    Huang Pingjie
    Chen Peihua
    Hou Dibo
    Zhang Guangxin
    Zhou Zekui
    2011 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC), 2011, : 53 - 57
  • [2] Research on Multi-sensor Information Fusion Method Based on Dempster-Shafer Evidential Theory
    He Guo
    Pan Xinglong
    Zang Chaojie
    Ming Tingfeng
    Wang Xiaochuan
    INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL, 2012, 15 (12A): : 5329 - 5336
  • [3] Solving of multi-sensor data fusion problem by using Dempster-Shafer method
    Ning, Y.
    Tian, S.
    Ning, P.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2001, 23 (03): : 98 - 101
  • [4] An On-Board System for Detecting Driver Drowsiness Based on Multi-Sensor Data Fusion Using Dempster-Shafer Theory
    Feng, Ruijia
    Zhang, Guangyuan
    Cheng, Bo
    2009 IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL, VOLS 1 AND 2, 2009, : 887 - 892
  • [5] Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory
    Basir, Otman
    Yuan, Xiaohong
    INFORMATION FUSION, 2007, 8 (04) : 379 - 386
  • [6] A Fuzzy Dempster-Shafer Evidence Theory Method with Belief Divergence for Unmanned Surface Vehicle Multi-Sensor Data Fusion
    Qiao, Shuanghu
    Song, Baojian
    Fan, Yunsheng
    Wang, Guofeng
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2023, 11 (08)
  • [7] A novel divergence measure in Dempster-Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion
    Xu, Shijun
    Hou, Yi
    Deng, Xinpu
    Chen, Peibo
    Ouyang, Kewei
    Zhang, Ye
    INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2021, 17 (07)
  • [8] Tension prediction for the scraper chain through multi-sensor information fusion based on improved Dempster-Shafer evidence theory
    Zhang, Xing
    Ma, Yansong
    Li, Yutan
    Zhang, Chuanjin
    Jia, Chenxi
    ALEXANDRIA ENGINEERING JOURNAL, 2023, 64 : 41 - 54
  • [9] Sensory Task Assignment Based on Dempster-Shafer Theory and Multi-Attribute Fusion in Mobile Sensor Networks
    Zhang, Li
    Zhang, Shukui
    Tao, Ye
    Long, Hao
    IEEE ACCESS, 2019, 7 : 133962 - 133973
  • [10] Combining Neural Networks Based on Dempster-Shafer Theory for Classifying Data with Imperfect Labels
    Tabassian, Mahdi
    Ghaderi, Reza
    Ebrahimpour, Reza
    ADVANCES IN SOFT COMPUTING - MICAI 2010, PT II, 2010, 6438 : 233 - 244