GUO Xiucai, WU Ni, CAO Xin. Fault diagnosis of rolling bearing of mine ventilator based on characteristic fusion and DB[J]. Journal of Mine Automation, 2021, 47(10): 14-20. DOI: 10.13272/j.issn.1671-251x.2021070050
Citation: GUO Xiucai, WU Ni, CAO Xin. Fault diagnosis of rolling bearing of mine ventilator based on characteristic fusion and DB[J]. Journal of Mine Automation, 2021, 47(10): 14-20. DOI: 10.13272/j.issn.1671-251x.2021070050

Fault diagnosis of rolling bearing of mine ventilator based on characteristic fusion and DB

More Information
  • Published Date: October 19, 2021
  • The existing mine ventilator rolling bearing fault diagnosis method only extracts time-frequency component characteristics and adopts shallow network structure, thus causing low fault diagnosis accuracy. In order to solve this problem, a fault diagnosis method of rolling bearing of mine ventilator based on multi-domain characteristic fusion and deep belief network (DBN) is proposed. Firstly, the method performs wavelet packet noise reduction on the bearing vibration signal, and extracts time domain characteristics, frequency domain characteristics and IMF energy characteristics from the bearing vibration signal after noise reduction to obtain a relatively comprehensive set of high-dimensional characteristic set. Secondly, the characteristic selection method based on intra-class and inter-class standard deviation is used to eliminate the characteristics that are not effective for classification and characteristics with no obvious effect so as to filter out high-efficiency characteristics. Finally, kernel principal component analysis (KPCA) is used to reduce and fuse the high-dimensional screening characteristics, eliminate the redundancy between characteristics, and input the fused characteristics into DBN to complete the fault diagnosis. The experimental results show that compared with the diagnosis method based on single characteristic and shallow network, the average accuracy of mine ventilator rolling bearing fault diagnosis method based on multi-domain characteristic fusion and DBN has average accuracy and less average diagnosis time showing good stability and generalization ability for different damage fault data.
  • Related Articles

    [1]PAN Wenlong, LI Shengjun, GAO Quanjun, YANG Luyu, LIU Qingfu, ZHANG Heming. Research on information model of coal mine fully mechanized mining equipment based on industrial Internet[J]. Journal of Mine Automation, 2024, 50(5): 84-92. DOI: 10.13272/j.issn.1671-251x.2024010022
    [2]CAO Xiangang, DUAN Yong, ZHAO Jiangbin, YANG Xin, ZHAO Fuyuan, FAN Hongwei. Summary of research on health status assessment of fully mechanized mining equipment[J]. Journal of Mine Automation, 2023, 49(9): 23-35, 97. DOI: 10.13272/j.issn.1671-251x.18143
    [3]CAI Anjiang, ZHANG Yan, REN Zhigang. Fault knowledge graph construction for coal mine fully mechanized mining equipment[J]. Journal of Mine Automation, 2023, 49(5): 46-51. DOI: 10.13272/j.issn.1671-251x.2023020005
    [4]JI Lei. Study on the weakening of coal wall with section resistance adjustment of fully mechanized support in hard thick coal seam with large mining height[J]. Journal of Mine Automation, 2022, 48(3): 5-10. DOI: 10.13272/j.issn.1671-251x.2022010006
    [5]MENG Guangrui, DING Zhen, LI Haodang. Discussion on key technologies of intelligent and autonomous coal cutting in fully mechanized mining[J]. Journal of Mine Automation, 2021, 47(S2): 1-3.
    [6]LIU Qing, HAN Xiuqi, XU Lanxin, QIN Wenguang. Cooperative control technology of shear and hydraulic support on fully-mechanized coal mining face[J]. Journal of Mine Automation, 2020, 46(5): 43-48. DOI: 10.13272/j.issn.1671-251x.17520
    [7]GAO Weiyong, ZHANG Minjuan. Research on following automation technology of hydraulic support on fully—mechanized coal mining face[J]. Journal of Mine Automation, 2018, 44(11): 14-17. DOI: 10.13272/j.issn.1671—251x.2018050040
    [8]CHEN Lei. Determination of real-time working resistance of support in fully-mechanized working face of medium-thickness coal seam[J]. Journal of Mine Automation, 2016, 42(12): 36-41. DOI: 10.13272/j.issn.1671-251x.2016.12.008
    [9]DU Yan, MENG Guoying, ZHANG Hanwen, ZHANG Miaotian, FENG Yu. Design of embedded monitoring and control platform of conveyor equipment of fully mechanized coal face of coal mine[J]. Journal of Mine Automation, 2014, 40(7): 96-98. DOI: 10.13272/j.issn.1671-251x.2014.07.026
    [10]HU Yua. New Technology of Quick and Safe Support Removal of Fully Mechanized Face[J]. Journal of Mine Automation, 2010, 36(10): 85-88.

Catalog

    Article Metrics

    Article views (169) PDF downloads (25) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return